These companies are seeing success as enterprises realize ROI through reduced need for human support staff, and lowering costs and increasing revenue through improved operational efficiency. These results explain why the market rates highly on our Mosaic Score, averaging 714, placing it in the top 5% of all markets in the enterprise tech industry.

So who’s leading today, who’s gaining ground fastest?

Using CB Insights’ revenue data, we identified each company’s customer service AI agents & copilots revenue to measure the current market size and estimate market shares for private players in the space.

If you are active in the customer service AI agents & copilots market and want to submit your company’s revenue data, please reach out to researchanalyst@cbinsights.com. 

See all customer service AI agents & copilots market ranked by revenue

Key takeaways

• New entrants are gaining ground rapidly using generative AI technology, to challenge leaders. Companies like Sierra, Crescendo, Decagon, Chatbase, founded in the last 2 years have already broken into the top 10 by revenue generation, competing directly with legacy players. This highlights low barriers to achieving scale in customer service AI where superior AI models and user experience can quickly displace incumbents who are reacting by acquiring some of these startups to stay competitive. For example, ServiceNow acquired Moveworks for $2.85B while Cognigy was bought out by NiCE in a $955M deal.
• The market is rapidly transitioning to autonomous agents that resolve customer issues end-to-end without human escalation. This is disrupting traditional pricing models with vendors like Intercom, Sierra and Decagon now charging per successful resolution rather than per seat or usage. Enterprise adopters must ensure any tool they adopt includes performance validation capabilities that link AI spending directly to resolution success and cost reduction metrics.
• Value of enterprise adoption will come from integration depth. AI agents require seamless integration to company-specific context such as historical tickets, internal knowledge bases, CRM data, and proprietary documentation, to deliver personalized, informed resolutions while maintaining consistent brand voice. Enterprise value emerges when agents turn customer inquiries into closed-loop resolutions, eliminating inefficient human handoffs and workflow bottlenecks. According to CB Insights buyer reports, successful vendors offer agentic solutions with ease of deployment and integration.

• Agentic voice AI expands the value proposition. Advances in voice AI, from natural dialogue and real-time tone analysis to sentiment-based adaptation, position voice as the emerging primary interface between humans and technology. By capturing emotional cues and conversational nuance that text misses, agentic voice systems can detect upsell opportunities, flag churn risks, and surface product insights during live interactions, transforming voice support from a cost center into a revenue driver. As these capabilities mature, enterprises should shift KPIs from operational efficiency (e.g., time or cost savings) toward strategic outcomes such as customer retention, conversion lift, and topline revenue growth directly attributable to AI-enabled engagement.

Market Overview

The customer service AI agents & copilots market comprises tools that automate client support inquiries and processes across multiple communication channels. These solutions converse with customers, answer questions, support inquiries, and resolve issues. Many can handle complex inquiries and complete processes end-to-end with minimal human oversight. They may also provide real-time assistance to human agents.

The market includes a mix of commercially mature players (~40%), challengers like Decagon and Crescendo, and established customer service incumbents. They have raised a combined $1B in equity funding so far this year, already matching last year’s amount.

The post The customer service AI agents leading the market in 2025 appeared first on CB Insights Research.

Since our last mapping in March 2025, the landscape has exploded from roughly 300 players to thousands as tech companies race to launch AI agent offerings across horizontal use cases and industry applications.

Agentic solutions have become a leading acquisition target for enterprise software incumbents, while 1 in 5 new unicorns ($1B+ valuation) are now developing agents.

To help navigate this expansion, we mapped 400+ promising private companies building AI agent applications.

Track 1,700+ AI agent companies

We selected companies for inclusion based on Mosaic startup health and potential scores (600+). We included private companies only with agent offerings and organized them according to their primary focus. This market map is not exhaustive of the space. For companies building infrastructure for agents, see our AI agent tech stack.

Please click to enlarge.

Want to be considered for future AI agent research? Brief our analysts to ensure we have the most up-to-date data on your company.

Here’s what today’s AI agent landscape signals about the future of tech:

• The private AI agent market is moving towards greater specialization. Horizontal AI agent startups outnumber verticalized solutions nearly 2-1 in the landscape. However, since we last published the map, industry-specific solutions have surged. This iteration features 47 companies in the healthcare & life sciences category, up from 7 in March. Partly behind the surge are commercially mature private companies launching agent offerings (or rebranding) to meet the moment. At the same time, emerging startups (see our analysis of Y Combinator’s recent batches here and here) are betting that highly regulated industries will favor specialized solutions over horizontal tools. 
• Leading AI agent companies are growing revenue at lightning speed, with the top 20 revenue leaders (with agents as a primary product) averaging just 3.8 years old. Software development leads revenue activity with 6 coding agents making the top rankings including Anysphere (Cursor) and Replit. This market is the most crowded on the map, reflecting the value agents bring to well-defined workflows and testable environments. But it’s also facing challenges as reasoning models drive higher inference costs, previewing the pricing pressures other agent categories will face. CB Insights customers can dig into revenue data for the full AI agent & copilot landscape with this search. 
• AI agent startups focused on cybersecurity operations are most primed to exit, based on average CB Insights M&A probability scores. Nullify (autonomous agents for application security) and Strike Ready (AI-powered SOC platform) top the list with 70%+ probability of getting acquired within the next two years. AI-related cybersecurity M&A (both for AI security solutions and AI-powered solutions) has already reached record levels in 2025 so far. Cyber leaders are snapping up competitors to keep up with an evolving attack surface. 

Methodology & category notes

Companies profiled on the map are working on various levels of autonomy, from agentic, LLM-powered workflows to fully autonomous agents.

These agents combine reasoning (foundation models for decision-making), memory (information storage and retrieval), tool use (external system integration), and planning (task decomposition and adaptation) capabilities.

We excluded companies building agent-specific infrastructure, focusing on startups targeting core enterprise use cases and industry-specific workflows.

Companies were selected from a pool of over 1,700 based on their CB Insights Mosaic score. 

Mosaic score

Evaluates the overall health and growth potential of private companies based on performance, financial stability, market conditions, and management strength. It combines these factors into a single score (out of 1,000).

Enterprise tech

Click into each market to view the full description and market players on the CB Insights platform. 

This segment primarily features startups targeting enterprises, with industry-agnostic applications across job functions such as:

• Data analytics
• Coding
• Finance and accounting
• Sales
• Marketing
• Customer service
• Recruiting
• Security operations centers
• Legal (in-house)
• IT operations & support 

Companies in the productivity & personal assistants category are targeting consumers and employees directly across applications like research, time management, and other browser-based tasks. 

General workflow automation & knowledge management tools are one of the largest categories on the map, encompassing the AI agent builder platforms market, which offers no-code and low-code solutions for business users to automate workflows. 

Industry-specific 

This layer features companies catering to industry applications, including: 

• Financial services & insurance (e.g., investment research, loan servicing & collections, compliance workflows) 
• Healthcare & life sciences (e.g., revenue cycle management, clinical documentation, patient access)
• Industrials (e.g., manufacturing optimization, supply chain, construction)
• Retail & hospitality (e.g., shopping agents, website personalization, restaurant & hotel) 

FURTHER READING 

• The AI agent bible
• The AI agent tech stack
• The AI agent market map: March 2025 edition
• State of AI Q3’25

The post The AI agent market map appeared first on CB Insights Research.

Early signals in hiring and partnerships show the compute mix starting to split. Lead times, cost, and concentration risk are pushing teams to add a second path. Availability of compute remains tight, and this is driving capacity to widen beyond a single vendor.

Anthropic is an early test case. The company is helping to develop Amazon’s Trainium AI chips, including plans to use these chips to train its next Claude model, and is expanding its use of Google’s TPU chips. These moves add capacity beyond Nvidia and lessens its reliance on Nvidia hardware.

Using CB Insights customer sentiment interviews, hiring insights, investments, and partnership activity, we analyze:

• Why Nvidia dominated and what’s different now
• Anthropic as an early example of diversification
• Beyond Anthropic, signals of a multi‑chip market

Why Nvidia dominated and what’s different now

Nvidia’s historic dominance in the AI chip space stems from its software and hardware ecosystem, and while that foundation remains strong, alternatives have improved enough to enable hedging.

CUDA — Nvidia’s software platform and programming model for running AI on its GPUs — and its supporting toolchain compress time to adopt and reduce delivery risk. For most teams, swapping to another supplier means rewriting code, retraining people, and revalidating models, a tax few companies can absorb, especially when developing new AI solutions.

CB Insights customer sentiment interviews echo this lock-in. For example, one founder at a software engineering company mentioned “since all of our infrastructure is built on CUDA … the main challenge in switching … is the infrastructure and software ecosystems. The learning cost … is unknown, and that uncertainty is a barrier.”

Check out more Nvidia customer sentiment interviews

And because CUDA software requires specialized knowledge, this lock-in shows up in AI company hiring data. For example, Baseten is hiring a GPU Kernel Engineer to implement custom CUDA kernels across current‑gen Nvidia GPUs. That is a direct signal of Nvidia‑specific optimization work that raises near‑term switching costs.

But parts of this story are changing. AMD’s software has materially improved since late 2024. Issues that once blocked deployments are less frequent, and fixes land faster. Parity is not universal by workload, but the friction gap is narrowing enough to allow some teams to start diversifying.

Anthropic as an early example of diversification

Against this backdrop, Anthropic is the early test of real diversification.

Anthropic is the notable exception to Nvidia’s investments across leading AI companies. The two have recently clashed over export controls, signaling a strained relationship between Nvidia and Anthropic that continues to this day.

See Nvidia’s latest AI investments here

The company’s relationship with Amazon further signals a path away from a sole reliance on Nvidia. This relationship began in September 2023, when Amazon announced a $4B investment in Anthropic and, in turn, the company named AWS its primary cloud provider. This meant distribution of Anthropic’s Claude models through AWS’s Bedrock service, as well as access to the Trainium AI chips that Amazon has been developing since 2020.

Since then, the two appear to be working in tandem to diversify away from Nvidia chips. AWS, for instance, recently said more than half of its Bedrock service now runs on its custom Trainium AI chips. In addition, Anthropic has recently committed to training the next generation Claude model on Trainium chips through Amazon’s Project Rainier data center deployment.

And Anthropic isn’t just relying on Amazon to help it move away from Nvidia chips.

In October 2025, Anthropic announced it will expand its use of Google TPUs — Google’s specialized AI chips — to up to one million units. Until then Google kept most TPU capacity for internal use; opening it at Anthropic scale signals both Anthropic’s push to add non‑Nvidia supply and Google’s bet that more buyers want alternatives. That shift on both sides sets up the broader signals of a multi‑chip market.

Beyond Anthropic, signals of a multi‑chip market

As software improves and compute remains hard to source, adopting alternatives is easier than a year ago. Signals include earnings calls, hiring, and partnerships.

In recent earnings calls, Amazon has highlighted persistent AI capacity constraints resulting in a strategic push to develop its Trainium chips. The company notes that AI demand is outpacing supply, opening up opportunities to develop its own capacity through Trainium chip supplies. But these supplies are still limited, forcing buyers to hedge delivery risk by lining up other non‑Nvidia capacity. For Anthropic, that likely meant adding Google TPUs alongside Trainium.

Monitor Amazon’s Trainium chip development through Earnings Insights

Hiring signals also show the trend gaining traction with other AI startups:

• Nscale, a data center hyperscaler, is hiring core infra roles that require “experience with Nvidia and AMD.” Nscale is positioning itself as a primary EU compute supplier, targeting data‑local workloads and a diversified supply chain that may shift some regional AI compute away from Nvidia.
• TensorWave, on the other hand, uses explicit AMD‑powered positioning (“building the Infrastructure for AMD‑Powered AI Computing”) and mentions that the company has deployed North America’s largest AMD GPU cluster. The company is projecting $100M in ARR by year’s end, signaling the market’s appetite for non-Nvidia compute.
• Groq, an infra provider developing its own learning processing unit (LPU) chip to compete with conventional Nvidia/AMD GPUs, is still recruiting to “support evolving Nvidia and AMD GPU architectures,” indicating cross‑vendor software support even as it promotes its own LPUs.

Beyond hiring and capacity, anchor deals are pushing diversification. OpenAI is lining up multi‑GW compute across Nvidia, AMD, and Broadcom, with Broadcom focused on custom, jointly designed chips. Oracle — a key OpenAI compute partner — plans roughly 50,000 next‑gen AMD accelerators starting in Q3’26 with expansion through at least 2027, reinforcing the move away from a single‑supplier model.

For the market overall, as reliance on Nvidia eases at the margin, compute supply spreads across Nvidia, AMD, and cloud custom silicon. That expands access, improves tokens‑per‑second and latency, and reduces lead‑time risk for startups. Where throughput and latency improve, usage rises and spend follows — especially for teams already maxed on capacity.

As compute comes from multiple vendors, expect a larger market for software that abstracts chip differences so teams can focus on product rather than implementation.

If you are a venture investor and want to submit data on your portfolio companies to allow us to better score you in the future, please reach out to researchanalyst@cbinsights.com.

RELATED RESOURCES FROM CB INSIGHTS:

• All 100 AI unicorns since ChatGPT launched
• State of Tech Exits H1’25
• The top angel investors in AI
• Are AI unicorns starting to move beyond hype?
• Book of Scouting Reports: Enterprise AI Agents

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To create the Tech IPO Pipeline, we scored companies across CBI datasets including Mosaic scores, hiring insights, revenues, exit probabilities, business relationships, and more.

Check out key highlights across the Tech IPO Pipeline below.

CBI customers: See these companies ranked by Mosaic and revenue

Combining CB Insights’ proprietary data and AI, scouting reports provide insight into each company’s:

• Funding history
• Headcount
• Key opportunities and threats
• IPO prospects
• Mosaic score

For customers, get the full book of 100+ scouting reports using the download button on the lefthand side.

MORE CB INSIGHTS RESEARCH:

• State of Venture Q3’25 Report
• Future Tech Hotshots: 2025: 45 emerging tech startups poised to make an outsized impact
• State of AI Q3’25 Report

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Combining CB Insights’ proprietary data and AI, scouting reports provide insight into each company’s:

• Funding history
• Headcount
• Key takeaways (including opportunities and threats)
• Product/tech focus
• Mosaic score

Want to see more research? Start your free trial.

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The conflict in Ukraine demonstrates the significant impact of these technologies — think low-cost drones destroying $5M tanks — driving European investment in domestic defense systems and supply chains.

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While deals fell in Q3’25, billion-dollar rounds to AI infrastructure players continued to drive the funding surge. But the activity isn’t limited to the largest players: investors are cutting bigger checks across every stage, signaling both conviction in AI’s potential and the high costs of AI development.

Among emerging opportunities, AI agents are a key focus for VCs and enterprises alike, with agent markets leading deal and M&A activity in the quarter.

Below, we break down the top stories from this quarter’s report, including:

• AI deal activity softens, but massive rounds support continued funding boom
• Consolidation remains in full force in the AI market
• Tech market deals highlight AI agent applications, rise of GEO
• The talent premium: AI companies valued at up to ~$100M per employee

Download the full report to access comprehensive CB Insights data and charts on the evolving state of AI across geographies.

AI deal activity softens, but massive rounds support continued funding boom

Deals to private AI companies globally fell 22% quarter-over-quarter in Q3’25, but funding remained above $45B for the fourth consecutive quarter.

Taken together, these trends indicate how top-heavy the AI venture funding landscape has become. 

The average deal size in 2025 YTD is $49.3M — up 86% from 2024. In the last 4 quarters, mega-rounds ($100M+ deals) have accounted for 75%+ funding. The average since 2021 (up to Q3’24) is 53%. 

At the same time, check sizes are trending bigger at the median across every stage this year. For example, the median early-stage deal is $3.4M in 2025 YTD, up from $2.5M in 2024. 

Investors are funneling capital into fewer, larger bets on perceived AI winners, driven by the massive infrastructure costs and competitive dynamics of foundation model development.

In Q3’25, there were 6 $1B+ rounds alone. The top 3 deals went to LLM developers — Anthropic ($13B, Series F), OpenAI ($8.3B, PE), and Mistral AI ($1.5B, Series C) — reflecting the high cost of frontier model development. While OpenAI hit $12B in annualized revenue in July 2025, it’s projecting roughly $8B in cash burn this year per reports. 

Other infrastructure players like Nscale (AI data centers, $1.1B Series B) and Groq (AI inference processors, $750M, Series E) were also in the top 10. The raises are indicative of the growth and attention technologies enabling AI are receiving, with earnings call mentions of data centers hitting record levels in Q3’25 and AI training & inference chips on track for record equity deal & funding activity this year.

Consolidation remains in full force in the AI market

The AI market is a hotbed for M&A activity. 

Q3’25 marks the second highest quarter on record for AI startup M&A (172 deals), following Q2’25 (181 deals). The US continues to gain share, with startups based in the country accounting for 59% of total exits, the highest share since Q2’21. 

Three of the top 5 exits in the quarter were related to AI agents: 

• Workday acquired Sana Labs, a company focused on enterprise workflows, for $1.1B. 
• NiCE acquired customer support startup Cognigy for $955M. 
• Atlassian acquired The Browser Company, the maker of the Arc and Dia AI browsers, for $610M. 

The activity signals enterprise software incumbents are looking to buy their way into accelerating their AI roadmaps. Workday was the second most active acquirer in the quarter with 3 acquisitions (behind Salesforce, with 4 acquisitions). The HR & finance software company also picked up agent builder Flowise and AI-powered recruiting platform Paradox.

Meanwhile, Meta made its first publicly disclosed acquisitions since 2022, acquiring voice AI startups Play AI and WaveForms AI.

Other notable top exits include AI security companies Lakera (acquired by Check Point for $300M) and Prompt Security (acquired by SentinelOne for $250M-$300M). Generative AI is expanding attack surfaces, driving large cyber players to opt for M&A to more quickly integrate AI security features into existing offerings.

Both Lakera and Prompt Security were founded less than 5 years ago, far “younger” than the average time to exit of 9.7 years in the quarter, underscoring how rapidly AI security has become mission-critical.

Review the AI security startups that are ripe for acquisition next in this brief.

Tech market deals highlight AI agent applications, rise of GEO

Among the 1,500+ tech markets that CB Insights tracks, those in the chart below saw the greatest number of AI deals in Q3’25 (note: companies may appear in multiple markets).

Industrial humanoid robot developers and coding AI agents & copilots remained at the top, while LLM developers also climbed back up in the rankings from Q2’25.  

One notable rising market is generative engine optimization (GEO), which refers to tools that help brands optimize their visibility in AI search platforms like ChatGPT and Perplexity. This emerging category (the most nascent in the list based on CBI Commercial Maturity scores) addresses the shift toward shopping and discovery happening on top of LLM interfaces.

OpenAI’s September 2025 launch of in-platform shopping capabilities in ChatGPT underscores this trend, establishing AI platforms as new commerce channels requiring specialized optimization strategies.

Using CB Insights’ Mosaic score — which measures private company health and predicts likelihood of success — we analyzed more than 20 GEO companies, ranking them by 1-year Mosaic score growth to identify the fastest-rising vendors. 

See the GEO partners best positioned to help brands win in AI search here. 

The talent premium: AI companies valued at up to ~$100M per employee

AI companies with lean headcounts and breakthrough potential are attracting sky-high valuations.

Humanoid robotics developer Figure leads the pack in Q3’25 at $104.3M per employee on a $39B valuation, despite reporting no revenue last year (though projecting $9B by 2029). Cognition follows with $98.1M per employee, based on its $10.2B valuation. While the coding AI agent startup has $150M+ in ARR (following its acquisition of Windsurf), this indicates a lofty revenue multiple of ~68x. 

Others topping the quarter’s valuation-per-employee list span the AI model (Anthropic, Mistral AI, Decart, Harmonic), infrastructure (Baseten), and application layers (OpenEvidence,  Sierra, Irregular). 

Whether these valuations prove prescient or overextended will largely depend on whether these companies can deliver on ambitious revenue projections in the years ahead.

RELATED RESOURCES FROM CB INSIGHTS:

• State of Venture Q3’25 Report
• State of Fintech Q3’25 Report
• State of AI Q2’25 Report
• State of Digital Health Q2’25 Report
• State of Tech Exits H1’25

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Space cybersecurity is now a necessary defense, protecting satellites, ground stations, and mission operations from cyberattacks throughout their lifecycles. The threat is intensifying as large satellite constellations expand the attack surface and quantum computing shows potential to break current encryption standards.

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As global competition intensifies and supply chains fracture, the ability to deploy capital into technologies that make the US more self-reliant, adaptive, and secure has become a national priority. 

JPMorgan Chase’s initiative is designed to meet that moment, representing both a financing bonanza for US-based companies across the 27 strategic sub-areas identified by the banking giant and a capital deployment challenge. 

To help, we’ve used CB Insights’ predictive signals, including our outlook score for company success, Mosaic, to shortlist 79 companies for JPMorgan Chase to consider investing in. 

We map them all below, categorized across all but 4 of the strategic areas identified by JPMorgan Chase due to a lack of promising startups matching our criteria — full methodology available at the end of this report. Key recommendations follow.

Find our success and exit predictions for the full list of featured companies here

Key recommendations

Invest in AI as the backbone of manufacturing sovereignty 

A new class of industrial AI startups is aiming to rebuild the nation’s productive capacity across manufacturing, materials, and mobility. 

Companies like Skild AI (robot foundation models), Charge Robotics (factory automation), and Cartken (autonomous mobile robots) are reducing the cost gap for onshore production by automating physical work and logistics. 

Meanwhile, Earth AI and Periodic Labs are applying AI to accelerate materials discovery and secure domestic access to critical minerals and battery inputs. Material development platforms have recently captured investor attention as AI and quantum computing unlock new capabilities, becoming one of the hottest emerging markets (i.e., with a Commercial Maturity median of 3 or below) in the manufacturing sector. 

Source: CB Insights Markets Index (as of 9/9/2025)

Even legacy-heavy domains like shipbuilding are seeing AI-enabled autonomy from firms such as Seasats and Blue Water Autonomy, which aim to reduce costs in a historically high-cost, labor-intensive industry. 

Together, these companies are forming the digital-industrial layer for intelligent reshoring, using AI to make domestic manufacturing faster, adaptive, and globally competitive again.

Bet on autonomous defense systems to redefine deterrence

Modern conflict is evolving faster than traditional defense procurement can adapt, primarily driven by the proliferation of drone warfare with the invasion of Ukraine. A new generation of startups is filling the gap with software-defined, low-cost autonomy. 

Companies like Fortem Technologies (drone detection and interception) and Allen Control Systems (Counter-unmanned systems) are deploying scalable unmanned systems across air and land. Paired with Picogrid’s defense connectivity networks and Rune Technologies’ predictive logistics AI, these platforms create adaptive, sensor-rich meshes that can respond to threats in real time. 

The result is a rapidly emerging autonomous defense ecosystem that strengthens deterrence through speed, intelligence, and distributed resilience.

See the hottest defense tech markets ranked by equity raised over the past year here

Smarter and cleaner energy production and distribution will help maintain AI lead

The AI boom has made energy an ever greater strategic resource, making diversified, digitally managed generation essential to both national security and computational progress. 

Investing in startups focused on nuclear innovation, solar automation, and AI-driven grid resilience will help build a smarter, sovereign energy base. 

Companies like Pacific Fusion and Realta Fusion are redefining what clean baseload power can look like, while Aalo Atomics and Natura Resources advance modular fission systems that can be rapidly deployed near data and industrial centers. 

Paired with Antora and Rondo Energy’s storage systems and David Energy’s automated grid orchestration, these technologies form an adaptive, distributed energy network capable of self-balancing demand spikes from AI compute and industrial electrification. 

See the most promising startups helping manage demand spikes from AI here

Investing in this ecosystem builds not only clean power capacity but also strategic autonomy, ensuring the intelligence revolution runs on domestically controlled, resilient, and low-cost energy.

Support edge intelligence to make AI truly ubiquitous

The frontier of artificial intelligence is shifting from centralized data centers to the physical world, where latency, cost, and privacy demand on-device autonomy. 

Startups like EnCharge AI (compute-in-memory chips) and MemryX (edge inference for automotive and robotics) are re-architecting silicon for real-time decision-making, while Modal and Fireworks AI deliver serverless deployment layers that make model execution as elastic as cloud functions. 

On the software side, Together AI (an AI 100 2025 winner) and Liquid AI are advancing compact, efficient small language models that can run locally, enabling intelligence in drones, sensors, and industrial systems. 

Combined, these technologies are creating a distributed AI fabric that embeds cognition into every node of the economy, from factory floors to vehicles and handhelds. The result: lower latency, greater resilience, and unprecedented reach, as intelligence moves from “in the cloud” to everywhere work gets done.

Track emerging genAI startups, shifting markets, and breakout opportunities in real-time here

Finance the intelligent age security: AI, data, and infrastructure

As AI becomes the backbone of critical systems, the attack surface expands exponentially. 

A new cohort of startups is fortifying this frontier by integrating AI-native security, quantum-safe encryption, and infrastructure hardening into the fabric of the intelligent economy. 

Companies like TXOne Networks and Xage Security are protecting industrial and energy assets with zero-trust architectures built for operational technology, while TrustLogix and Concentric AI safeguard sensitive enterprise data through granular policy enforcement and autonomous monitoring. 

TrustLogix is among the AI security startups most likely to be acquired next, according to CB Insights Predictive Intelligence, as large cyber players have been on a M&A spree to seize this opportunity and integrate AI security features into existing offerings.

See the top AI security startups ranked by their M&A probability

Quantum Xchange ensures secure data transmission with quantum-resistant encryption, and HiddenLayer defends AI models themselves against poisoning, inversion, and adversarial attacks.

Together, these technologies create a secure AI infrastructure stack that spans digital, data, and physical domains, ensuring that as intelligence scales, trust scales with it. Investing in this layer isn’t just about cybersecurity; it’s about protecting the nervous system of the modern economy.

Methodology

We used CB Insights’ predictive intelligence to analyze thousands of startups and select the most promising ones that align with JPMorgan Chase Security and Resiliency Initiative’s 4 key areas and 27 sub-areas.

We looked across our 1,500+ Markets to identify the hottest areas based on equity funding raised over the past year and selected companies with the highest Mosaic scores (min 550) operating in these markets. We focused on private, early to mid-stage (Series B max), US-based startups. Data is as of 10/23/2025.

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This cohort cuts through the noise to spotlight 45 companies we expect to have an outsized, lasting impact over the next 5-10 years — from AI infrastructure that powers autonomous enterprise systems to vertical AI applications in healthcare, financial services, and manufacturing that solve operational problems.

Using CB Insights’ proprietary data — including Commercial Maturity, Mosaic, patents, business relationships, and funding — we identified 45 emerging players most likely to have a strong exit in the next 5-10 years.

See every startup in the Future Tech Hotshots 2025 Expert Collection

Key takeaways

• Agent infrastructure is the new frontier. The cohort reveals a decisive bet on agentic AI, with startups like Coval (AI agent testing), Questflow (multi-agent orchestration), and Syncari (agentic master data management) building the foundational tools that enable autonomous AI to operate reliably at scale. These companies are positioned for outsized impact because they’re creating the critical layer to embed AI into workflows — just as cloud infrastructure enabled SaaS, agent infrastructure will enable the next wave of autonomous enterprise software.

• The 45 hotshots have collectively formed over 110 business relationships since 2024. LLM data preparation company LlamaIndex leads the pack (18 partnerships), having partnered with incumbents like Microsoft and Databricks, while blockchain infrastructure API startup Crossmint has forged partnerships with Visa (to enable AI-driven on-chain payments) and Moneygram (to power new stablecoin cross-border payment experience). As these startups scale over the next 5–10 years, this early validation with enterprise incumbents will become harder to displace as customers build workflows around their products.

• Industrial AI is the most promising area, with companies in this space having experienced the highest Mosaic score increase over the last 6 months. This includes GIS platform Felt (+71 points in 6 months) and humanoid developer Persona AI (+57). This momentum reflects investor and customer recognition that industrial AI creates defensible moats through domain-specific datasets that take years to build. Unlike horizontal tools, this vertical expertise can’t be easily replicated, positioning these companies as prime acquisition targets for industrial incumbents seeking AI capabilities over the next 5-10 years.

• Elite management teams cluster in enterprise infrastructure. Top Management Mosaic scores concentrate in enterprise tech, with Lineaje (962/1000; software supply chain security platform), Maven AGI (956/1000; customer service AI agents), ProRata.ai (950/1000; AI-powered search and advertising), and Harmonic (876/1000; mathematical superintelligence) all led by executives hailing from incumbents like Google, Robinhood, and Stripe. These companies signal that the most experienced founders see enterprise infrastructure — not verticalized or consumer AI — as the category where technical depth and execution create the most competitive advantage.

Methodology

We used CB Insights data to analyze hundreds of VC-backed private tech companies with Mosaic scores of 600+ and an early commercial maturity score. 

Our scoring model factors in signals like investor quality, business relationships, Mosaic scores, key people data, and patents. We excluded companies with fewer than 100 employees. Data is as of 9/29/2025.

For information on reprint rights or other inquiries, please contact reprints@cbinsights.com. 

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AI-powered coding has become one of the fastest-growing enterprise use cases for LLMs. Startups like Anysphere (maker of Cursor), Replit, and Lovable have crossed $100M in ARR in record time, with adoption surging across professionals and amateurs, small companies and enterprises alike.

Satya Nadella says that as much as 30% of Microsoft’s code is now written by AI. But this breakneck adoption is exposing new pain points for enterprises: volatile costs and emerging security risks as AI-generated code volumes surge.

As agentic AI transforms software development, tools that deliver shipping speed, cost certainty, and de-risk AI outputs will separate sustainable enterprise adoption from expensive experiments.

Using CB Insights’ predictive intelligence on private companies, we mapped 90+ companies offering AI-powered solutions across 8 software development markets to reveal how agentic AI is collapsing the development stack, identify which capabilities set winners apart from losers, and uncover which features matter most to enterprises.

Note: Our market map includes private AI startups operating in the software development space that have received funding in the last 2 years. Companies may operate in multiple markets. This market map is not exhaustive of companies operating in the space.

Explore these companies sorted by their Mosaic score here

Key takeaways

Agentic AI is collapsing the software development stack — orchestration will separate winners from losers

The future is agentic. End-to-end agents are not just speeding up coding, they are combining planning, implementation, testing, and deployment into unified workflows, pushing the market from many fragmented tools toward fewer intelligent platforms. Adopters are feeling the same way.

3 out of 4 companies offering solutions for the software development lifecycle (SDLC) now have semi- or fully autonomous agentic features, including multi-agent capabilities. Companies offering agentic features also average a Mosaic score of 653, a 100 points higher than the average for companies without agentic features, signaling stronger overall health and growth potential.

Agentic AI is expediting code creation and enhancing developer workflows better than traditional coding tools, but reliability issues and enterprise requirements, such as compliance, governance, security, and quality assurance, will continue to drive demand for specialized agents at each SDLC phase.

Winners will provide unified orchestration layers that integrate specialized, best-in-class agents for each software development phase with governance and human oversight built in.

To operationalize AI, enterprises should evaluate vendors on integration with existing development infrastructure, governance controls that enable oversight and auditability, and observability into agent performance and associated costs.

Inference cost management is emerging as a key feature as AI coding bills soar

Reasoning models have inflated output token volume by 20x, pushing vendors away from seat-based pricing toward usage-based and hybrid models.

In response, cost control solutions are emerging as critical infrastructure. Larridin tracks AI costs and measures tool effectiveness, Cline is building cost controls directly into its offerings, while Paid reached a $100M valuation, pioneering outcome-based billing.

Enterprises should prioritize vendors with built-in cost governance. Look for solutions that offer cost-monitoring dashboards, usage forecasting, and budget controls as token volumes surge.

As costs shift from predictable seats to variable usage, expand AI governance beyond engineering to include stakeholders across finance, operations, and marketing. Accessible vibe coding tools are democratizing software development, enabling these non-technical teams to become builders and making cross-functional cost oversight essential.

For AI adoption, enterprises should look beyond traditional billing models. Consider vendors like Paid, Alguna, or Orb which offer results-based, AI-native billing to align costs with business value rather than infrastructure consumption.

Enterprise selects agentic AI providers based on security, integration depth, ability to ship software faster, and value-based pricing

End-to-end agents demonstrate strong market validation with an average Mosaic score of 745, indicating robust health, reliability, and growth potential.

We analyzed buyer interviews from the highest-valued agentic AI providers to identify enterprise selection criteria:

• Security compliance for AI-generated code 
• Ease of use for non-technical users and the ability to ship end-to-end solutions faster, 
• Deep and seamless integration with existing systems
• Transparent pricing justified by measurable value

As enterprises scale AI adoption, prioritizing vendors with native integrations and robust AI code security is key to reducing implementation friction. Check our AI security market to identify vendors leading in security and derisking AI.

Beyond immediate selection criteria, specialized coding small language models (SLMs) are an emerging trend to watch. Relace.ai’s recent $23M funding signals SLMs’ ability to improve coding efficiency at lower costs, a key enabler for value-based pricing models.

The blended AI coding market is temporary — expect separate markets for consumer and enterprise

There is a democratization of AI developer tools. Non-technical and professional use cases are hard to distinguish and startups are currently willing to blend the 2. The total addressable market (TAM) is no longer one or the other as companies can sell into both.

While vibe and enterprise coding markets are currently intertwined, we expect two distinct markets to emerge, targeting individual vibe-coders and professional developers, respectively. For example, Replit recently switched from targeting professional developers to non-technical users and gained momentum as a result.

Companies that can successfully pivot or maintain dual product lines will be better positioned. This suggests looking for teams with the flexibility to adapt their positioning as the market matures.

With an enlarged TAM, this offers investors a chance to diversify portfolios across both enterprise-focused and vibe-code-oriented AI coding tools.

Early traction metrics can mask user composition risks. Investors should verify if startups are winning with their intended customer by checking out the vibe-coding startups with the highest Mosaic scores.

Market descriptions

Software design tools

The software design market provides tools that create blueprints, technical specifications, and user experience designs for applications before development begins. Key activities include high-level design (system architecture and UI/UX structure), low-level design (data models, database schemas, API specifications, and component interfaces), and interface design (wireframes, mockups, prototypes, and design systems). Some platforms use AI to accelerate diagram generation, architectural planning, and technical documentation. These tools enable teams to visualize and validate both system architecture and user experiences before writing code.

Code generation & assistance

This code generation and assistance market offers tools and platforms that use AI to generate code, provide autocomplete suggestions, explain code snippets, debug issues, and assist with writing functions or modules. Solutions are delivered as IDE integrations, native IDE features, standalone applications, or self-hosted platforms. They accelerate coding by reducing boilerplate, catching errors, and offering intelligent recommendations. Some platforms include partial agentic actions that automate routine coding tasks. While some companies extend into adjacent phases like testing or deployment, their core offering focuses on code creation and maintenance.

Code review

The code review market provides tools that facilitate the systematic examination of code by developers or peers to identify errors, bugs, vulnerabilities, and adherence to coding standards. These platforms automate code analysis, provide static analysis capabilities, and offer collaboration features for reviewing and discussing code changes. Solutions range from standalone review platforms to integrations within version control systems and CI/CD pipelines. They help teams enforce consistency and quality across codebases and catch issues before they reach production. Some vendors incorporate AI to suggest improvements, detect security flaws, or automatically flag non-compliant code patterns.

Test automation software

The test automation software market provides platforms and tools that automate software testing processes across web, mobile, API, and desktop applications. Solutions enable QA teams and developers to design, create, execute, and manage automated tests through scripted frameworks, low-code/no-code interfaces, and AI-powered test generation. AI capabilities include automatic test case generation, failure prediction, self-healing when applications change, and test suite optimization. These platforms integrate with CI/CD pipelines, version control systems, and development workflows to enable continuous testing throughout the software delivery lifecycle. Both traditional and AI-driven solutions reduce manual testing effort and increase test coverage through automated validation.

Release automation software

The release automation software market provides platforms that automate and orchestrate the deployment of software releases from development to production environments. These solutions manage release planning, deployment automation, environment coordination, and progressive delivery strategies including feature flags and rollback capabilities. Platforms enable teams to schedule releases, coordinate deployments across multiple environments, manage dependencies, and ensure controlled rollouts to production. Solutions integrate with CI/CD pipelines, version control systems, and monitoring tools to provide end-to-end visibility into the release process. The market serves software development and operations teams seeking to standardize release processes and reduce deployment risk.

Code documentation & knowledge management

The code documentation and knowledge management market provides platforms that help development teams create, maintain, and preserve technical knowledge throughout the software development lifecycle. Solutions automatically generate documentation from code, maintain technical wikis, create API references, and answer questions about codebases using AI. These platforms capture what code does and why decisions were made, preserving organizational knowledge as team composition changes. The market serves software development teams seeking to reduce onboarding time, and maintain up-to-date documentation.

Code modernization & technical debt management

Code modernization and technical debt management market encompasses solutions that help organizations refactor, upgrade, and maintain legacy codebases while systematically addressing accumulated technical debt. It includes tools and platforms that automatically analyze and assess legacy code quality, dependencies, and technical debt, migrate applications from legacy languages to modern frameworks and automate code translation, modernization, and cloud migration efforts.

End-to-end software development AI agents

The end-to-end software development AI agents market offers AI agents that function as virtual software developers and engineers, independently handling end-to-end and full-stack development tasks with zero to minimal human supervision. Agentic AI understands requirements, writes code, debug, test, and deploy – operating across the entire software development lifecycle (SDLC) autonomously.

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This summer, the accelerator that spotted OpenAI, Airbnb, and Stripe before they became household names focused its funding on the production-ready AI layer that incumbents will soon race to acquire or replicate.

For strategy teams, Y Combinator represents both a roadmap of where the venture landscape is heading and a curated list of potential acquisition targets, partners, and competitive threats. 

Using CB Insights, we mapped the 165+ companies in the Y Combinator’s 2025 Summer batch across 11 different categories. Then, we analyzed the cohort to make predictions about what this means for the future of enterprise AI. 

Please click to enlarge.

Note: Categories are not mutually exclusive. For more, see the Y Combinator Summer Batch 2025 Expert Collection here. 

Track the 165+ startups in Y Combinator’s newest cohort

Key Takeaways  

• Voice AI is expanding into regulated industries. 16 companies in the batch are building specialized voice AI systems across use cases. Beyond just consumer assistants (April, Blue), startups in this batch are producing enterprise-grade systems managing complex, regulated interactions, notably in financial services (Altur, Veritus Agent, Qualify.bot, Wayline) — where compliance barriers are highest. Meanwhile, startups like Liva AI and Panels are building voice training data, as proprietary datasets that general-purpose models can’t replicate, creating defensive moats for companies employing voice AI.

• Startups are pushing deeper into software development with specialized solutions. With 20 software development companies in this summer’s Y Combinator batch, the category represents the largest, with coding agents still showing the strongest revenue traction among all AI agent types. However, new entrants are expanding beyond the code generation that enterprises already use. For example, Stagewise (frontend agents for codebases) and Interfere (autonomous de-bugging) are moving beyond just code generation to handle the complete development lifecycle, from writing production-ready code to testing on physical hardware. 

• Y Combinator is betting on the full agent stack, signaling the technology has moved from experimentation to implementation. Nearly 50% of YC’s Summer 2025 cohort offers AI agents, with 14 of those companies focusing specifically on agent infrastructure needed for deployment — spanning agent evaluation (AgentHub), de-bugging (Fulcrum Research), and monitoring (Mohi). Meanwhile, startups like Nozomio Labs (building context augmentation layers for agents) and Imprezia (creating AI-native ad networks) are pushing beyond traditional tooling into novel applications. As agents become table stakes for enterprises, infrastructure tools will become increasingly critical for building and managing reliability and performance at scale.
• The AI infrastructure focus is shifting from capability to efficiency. Companies like Stellon Labs (tiny frontier models for edge devices), Herdora (low-latency GPU inference for voice AI), and DeepAware AI (AI data center energy optimization) signal that deployment constraints — not model performance — are now the primary barrier to AI adoption. Solutions focusing on efficiency constraints like latency, energy costs, and edge deployment are critical for commercial AI deployment.

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The surge was fueled by outsized mega-rounds to new decacorns — companies with $10B+ valuations — and the continued dominance of AI, which accounted for 51% of all funding and 22% of deals in Q3’25.

However, funding growth was far from uniform across sectors. Retail and healthcare saw quarterly declines, while fintech remained flat. The data suggests that investors are pulling back from traditional industries and doubling down on emerging technologies, especially AI.

Below, we break down the top stories from this quarter’s report, including:

• Funding surpassed $90B for the 4th consecutive quarter
• AI is on track to capture over 50% of total annual venture funding for the first time 
• Decacorns raise record funding, as quarterly tech mega-rounds reach a new high
• Humanoid robots captured the most deals for the 2nd quarter in a row
• Exits are rebounding despite companies staying private longer

Let’s dive in.

Download the full report to access comprehensive data and charts on the evolving state of venture across sectors, geographies, and more.

Top stories in Q3’25

1. Funding surpassed $90B for the 4th consecutive quarter

Venture funding exceeded $90B for the fourth consecutive quarter, reaching $95.6B in Q3’25. The year-to-date total surpassed $310B, marking the highest annual figure since 2022.

Deal count, however, fell to its lowest point since Q4’16, underscoring an ongoing trend: investors are writing bigger checks to fewer companies. This pattern has persisted for over a year.

AI maintained its stronghold on the venture market, capturing $47.8B in Q3, for 50% of total funding and 22% of deals. Both figures represent the second-highest quarterly levels on record, confirming AI as the primary driver of venture strength.

While the largest rounds of the quarter went to leading AI players, such as Anthropic and OpenAI, other standout fundraisers included:

• China Fusion Energy, which raised $1.6B to advance fusion reactor engineering and commercialization.
• DBG Health raised $1B to provide a range of healthcare services.
• PsiQuantum, a developer of photonic quantum computers, raised a $1B Series E round.

Investors are also fueling a resurgence in hard tech — particularly in aerospace, defense, and advanced computing: 

• Aerospace funding reached $14.1B through Q3’25 and is expected to reach $18.9B by year-end — surpassing its 2021 record by 20%. 
• Defense tech raised a record $13.7B, driving the emergence of a new military-startup complex. 
• Quantum computing tripled its previous annual funding record, reaching $3.7B.

The data points to a venture market in transition — one defined by larger checks, fewer deals, and a growing concentration of capital in AI and hard tech.

2. AI is on track to capture over 50% of total annual venture funding for the first time

AI companies are capturing a record share of funding and deals this year, at 51% of funding and 22% of deals. They also claimed 7 of the 10 largest rounds this quarter.

The US is proving especially dominant in AI, attracting 85% of total AI funding and 53% of deals in 2025. Four of the 7 largest rounds this quarter were based in the US: Anthropic, OpenAI, Databricks, and Figure.

Funding to AI-enabled companies is also taking a significant share of traditional sectors:

• Retail tech declined to $5.4B, its lowest quarter since Q3’24, with AI startups raising 36% of annual funding.
• Digital health fell to $4.5B, marking its weakest quarter since Q4’24, with AI startups representing 63% of the sector so far this year.
• Fintech remained flat at $10.9B quarterly, with AI firms accounting for 23% of total fintech funding in Q3’25, its 2nd highest quarter on record.

AI is creating a clear split in the venture ecosystem, with AI startups capturing an outsized share of capital and mega-rounds, while non-AI startups face tighter funding conditions.

The rapid rise in AI valuations raises questions about long-term sustainability, as many companies are priced for winner-take-all outcomes across categories, particularly in saturated markets like coding agents & copilots, where dozens of similar startups compete as margins tighten.

The current environment reflects a flight to quality. While AI continues to drive momentum and capital concentration, the market is gradually shifting toward fundamentals — where execution and efficiency, not just promise, will determine which companies justify their valuations.

3. Decacorns raise record funding, as quarterly tech mega-rounds reach a new high

The venture landscape is moving beyond unicorns to decacorns — companies valued at $10B or more. Decacorns raised a record $94.5B through Q3’25, surpassing the previous record of $46.3B in 2024.

However, the number of decacorn deals is almost half as much as it was in 2021 when it reached $45.5B — from 60 deals to 32 this year — revealing the high funding concentration among the very largest companies — primarily leading AI startups.

AI leaders raising at decacorn valuations include developers xAI, Scale, and Perplexity, defense startups Anduril and Helsing, and fintech company Ramp.

Beyond decacorns, $100M+ mega-rounds for tech companies also hit record levels. September saw 52 tech mega-rounds in total, with 70% of capital allocated to companies focused on making AI infrastructure more affordable at scale.

Many AI infrastructure companies that raised mega-rounds in Q3’25 have already generated substantial revenue. Invisible Technologies reached $134M in 2024, Baseten reportedly grew 10x YoY, while Rebellions projected $72M in revenue. This shift separates real businesses from overvalued concepts as scrutiny intensifies.

Decacorns and mega-rounds are defining the current venture landscape. The market is bifurcating not only between AI companies and the rest, but also between decacorns and mega-round recipients vs. everyone else.

We expect the gap between well-funded companies and the rest of the venture ecosystem to continue widening as capital concentrates among market leaders who are building critical infrastructure and enterprise solutions.

4. Humanoid robots captured the most deals for the 2nd quarter in a row

AI markets dominated the most active deals in Q3’25, including AI-powered humanoids, AI software applications, and autonomous driving. 

Industrial humanoid robots captured 17 deals — more than any other market — continuing momentum from Q2’25, when it also led with 23 deals. New humanoid robot unicorns also emerged — Zhiyuan Robot and Unitree Robotics — bringing the total to 4.

Humanoid deal activity extended outside of the industrial sector in Q3. Healthcare humanoid robots secured 7 deals, ranking just outside of the top 10 markets. Figure led both the industrial and healthcare humanoid markets, raising a $1B Series C round at a $39B valuation, making it the 9th most valuable private company globally.

Investor interest in humanoid robots is driven partly by physical AI enabling new robotics capabilities, giving humanoids commercial promise that was not previously possible.

But despite deal activity and future potential, humanoids remain years away from widespread deployment. Developers still face fundamental challenges with inference, dexterity, reliability, and cost, which limit initial use cases to structured environments like factories and warehouses with a controlled and predictable set of tasks.

Autonomous driving showed particular strength among markets powered by physical AI. Both autonomous trucking systems and autonomous driving systems captured 8 deals each, ranking among the most active markets by deal count, alongside prominent AI categories such as coding AI agents, AI agent development platforms, and LLM developers.

5. Exits are rebounding despite companies staying private longer

Exits are recovering, but the numbers also reveal a fundamental shift in how long startups remain private before going public or getting acquired.

M&A and IPO activity both rebounded in Q3’25, partly driven by maturing AI startups that created more exit opportunities. M&A deals rose 8% from last quarter to 2,324 — the highest total since Q3’22. AI M&A activity remained elevated at 172 deals, contributing to the increase.

Fintech M&A contributed heavily to the rebound, rising to 249 deals — its highest level since Q1’22. Healthcare M&A also hit its strongest level since Q1’23, with 3 of the top 10 M&A transactions going to healthcare companies.

IPO activity climbed 45% from 95 to 138 — the highest quarterly total since Q3’23. AI and fintech contributed to the uptick, but software companies dominated the largest offerings. The biggest IPOs went to Figma and Klarna. The only hardware exception was China-based Best Semi, a semiconductor equipment manufacturer.

The Q3 exit rebound reflects improving conditions and suggests a broader recovery ahead, especially if interest rates continue to decline.

Despite increased exit activity, companies are staying private longer, with the time to exit rising from 12.2 years in 2015 to 15.9 years in 2025.

The ability to raise at decacorn valuations while staying private removes the pressure to go public for capital. Companies can now scale to a massive size, hire top talent through liquid secondary markets, and maintain founder control — all without the quarterly earnings pressure or regulatory burdens associated with going public.

Exit levels are recovering, suggesting that the market is normalizing, but the structural shift toward longer private tenures is likely to remain. The venture lifecycle is undergoing a fundamental change, with companies now possessing viable paths to scale privately that did not exist a decade ago.

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RELATED RESOURCES FROM CB INSIGHTS:

• State of Venture Q2’25 Report
• State of Fintech Q3’25 Report
• State of AI Q2’25 Report
• State of Digital Health Q2’25 Report
• State of Insurtech Q2’25 Report
• State of Tech Exits H1’25

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In September, private companies globally raised 1,400+ early-stage rounds (noting this total will rise as more deals are published retroactively). Over 25% of startups that raised rounds are building AI-enabled products and services.

Download the full report to access comprehensive CB Insights data on early-stage activity, including top investors & deals, valuation data, and our predictive signals. Below, we highlight notable trends to watch.

Emerging trends & categories to watch

Click the links to see underlying deal activity. Categories are not mutually exclusive. 

Review the full deal dataset

AI agents

Similar to last month, companies targeting AI agent applications raised over 50 deals (out of 1,485). Key trends to note include: 

• “Smart Money” is all in on AI agents: The top 25 VCs identified by CB Insights backed 13 AI agent startups in September. This represents nearly 20% of all of the early-stage activity from these VCs in the month. Focuses include security (Akto, Fabrix Security, Terra Security) and governance, risk, and compliance (Geordie, Zania), indicating enterprise adoption and risk management are key investment priorities. 
• Customer service is one of the most established use cases but is still seeing early-stage traction: AI agents handling customer service, support tickets, and user interactions represent one of the largest early-stage agent categories in September (8+ deals). Support operations have clear unit economics, high volume repetitive tasks, and direct cost savings compared to human agents, driving continued activity here. Top companies to watch based on Mosaic scores include Doo (Mosaic: 747) and Rauda AI (Mosaic: 687). 
• Emerging voice AI sector: Voice and phone agents are attracting dedicated investment (6 deals, 11% of agent activity) as investors bet on solutions that can tackle the unique technical challenges of voice interactions (i.e., real-time latency requirements, natural speech processing, emotional intelligence, etc.). Confido Health and Prosper, for example, are focused on healthcare applications. Meanwhile, Vida and Vaani Research are building infrastructure to develop voice AI/phone agents. Review the voice AI development platforms market to compare 30+ vendors in the space.

Robotics

Companies building robots, and the systems that power them, raised over 70 deals in the month. 

Within robotics, defense & security applications led early-stage activity (17 deals, 24% of total robotics activity), reflecting geopolitical tensions driving investment in autonomous defense systems and surveillance.

Other notable traction is in foundation models and operating systems for robots, as investors bet on horizontal platforms (4 deals, 6%):

• The leading September round in this category went to DYNA, which raised a $120M Series A. The company is ranked as a “challenger” in our robot foundation model developers market. 
• Another highlight is InOrbit, which focuses on robot orchestration and was founded by Florian Pestoni and Julian Cerutti, who have experience at a range of large tech companies, including Google, Atlassian, Meta, and IBM. The founders’ strong pedigree is reflected in the company’s CB Insights Management strength score, which ranked the highest of all the companies raising early-stage deals in September.

Management strength score

CB Insights’ Management strength scores (out of 1,000) the founding and management team’s prior achievements and likelihood of achieving future success, like a high-value exit. 

Especially at the earliest stages of the startup lifecycle, the strength of the management team serves as a key signal of potential. 

AI for scientific discovery & materials development 

Three of the largest early-stage rounds of the quarter went to companies looking to accelerate scientific discovery and materials development with AI: 

• Periodic Labs ($300M Series A at $1B valuation)
• Lila Sciences ($235M Series A at $1.23B valuation)
• CuspAI ($100M Series A at $520M valuation) 

Both Periodic Labs and Lila Sciences are also building “autonomous labs” — with AI designing, conducting and iterating on experiments. All 3 companies are operating at Commercial Maturity level 2/5 (Validating), indicating they’re still testing and refining their products.

Methodology

This report includes equity early-stage financings (convertible note, angel, pre-seed, seed, Series A) to private companies in August 2025. We excluded companies that are later-stage that raised an angel round or convertible note in the month. Categorization based on company descriptions.

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OpenAI‘s September 2025 rollout of in-platform checkout via Shopify and Stripe enables shoppers to search, decide, and buy entirely within ChatGPT. Adobe reports that traffic from generative AI platforms to US e-commerce sites surged 4,700% year-over-year in July 2025, and is accelerating every month.

To compete, brands must establish a strong presence in AI search — or risk losing share in the emerging agentic shopping journey. Generative engine optimization (GEO) is the solution: startups in this space are building tools to track, measure, and optimize brand visibility across ChatGPT, Claude, Google AI Overviews, and other AI-generated answers, creating a new SEO arms race.

Using CB Insights’ Mosaic score — which measures private company health and predicts likelihood of success — we analyzed more than 20 GEO companies, ranking them by 1-year Mosaic score growth to identify the fastest-rising vendors. By examining their capabilities and growth signals, this analysis highlights the GEO partners best positioned to help brands win in AI search.

Explore the generative engine optimization (GEO) market

• Native GEO solutions have the first-mover advantage. Of the 8 GEO companies with the most momentum — each posting more than the market’s average 14% Mosaic growth in the past year — 7 were founded between 2023-2025. All but 2 companies with Mosaic scores of 600+ also launched in the same window. These “native” GEO companies were built specifically for LLM visibility rather than retrofitting or bolting onto SEO tools, giving them advantages in data collection, multi-model monitoring, and content recommendations. 
• Four capabilities have emerged as table stakes, and vendors without this full stack will fall behind. Analysis of the CB Insights market scorecard for companies with the top Mosaic scores (600+) highlights a consistent feature set: multi-platform monitoring across ChatGPT, Perplexity, Google AI Overviews, and emerging engines; competitive benchmarking that tracks share of voice against rivals; sentiment analysis measuring how brands are portrayed; and actionable insights on content gaps and adjustments. Vendors that deliver this complete capability set are the ones that will lead the market. Buyers should prioritize these features when evaluating potential partners.
• Automated content generation is becoming a differentiator. While most GEO tools diagnose visibility, leaders are building autonomous content engines that create LLM-optimized content. Profound offers AI content workflows using preset templates to write AI-friendly material from aggregated citations. Surfer‘s Content Editor suggests LLM-ready content modifications. Scrunch constructs parallel “AI-friendly” website versions optimized for machine consumption alongside human-facing sites. As GEO evolves, the platforms that can both track and directly shape how LLMs read the web will dictate the rules of agentic discovery. Brands’ demands for the most efficient solutions will force pure-play monitoring tools to build or acquire content capabilities.

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Training LLMs at scale is capital-intensive, and large funding rounds create a moat that shields model developers from new entrants.

Yet raw compute is a bottleneck, prompting bets on next-generation inference architecture — specialized chips and model fine-tuning — to bypass spiraling AI costs.

This investment pattern shows that scaling models alone is no longer sufficient. In the next phase of AI growth, performance, efficiency, and accessibility will determine which models and platforms emerge as winners. 

Using CB Insights’ Business Graph, our monthly Book of Scouting Reports offers an in-depth analysis of every private tech company that has raised a funding round of $100M or more. It spotlights where private capital is concentrating, startups gaining momentum, and which companies are becoming tomorrow’s AI disruptors.

Download the book to see all 50 scouting reports.

KEY TAKEAWAYS

• Companies helping control AI costs are gaining momentum: 7 AI inference/performance optimization companies received mega-round funding in September, with over 70% already generating revenue. Baseten achieved 10x revenue growth in 12 months, Rebellions projects $68M for 2025, and Invisible generated $134M in 2024. Their client rosters include LLM and genAI developers like Cohere, Writer, and OpenEvidence), signaling strong demand and proven value.
• Cryptocurrency companies zero in on regulation and banking partnerships, boosting credibility and reach: 3 cryptocurrency companies received mega-round funding this month, and all either facilitate payments or offer digital currencies. 2 boast major banking relationships that validate institutional trust — Kraken partnered with PayPal, Circle, and Mastercard, and Fnality‘s Series C saw participation from 8 major banks, including Barclays, Bank of America, and Citibank. Fnality also received regulatory approval from the Bank of England, and Kraken acquired regulatory licenses in the United States and Europe, signaling that these companies are clearing compliance hurdles that have historically limited crypto’s reach. 
• Investors continue to place big bets on specialized, purpose-built AI ahead of commercial validation: Building on August’s momentum, early-stage bets are flowing to specialized AI companies built for specific industries and domains like robotics, manufacturing, materials science, healthcare, and pharmaceuticals. This month, 60% of verticalized AI mega-rounds occurred at Series A, and over 80% of these companies have Commercial Maturity Scores of 2, meaning their solutions are still being validated. Only robotics developer Auterion has live clients, while Lila Sciences and CuspAI have a single commercial partnership each. The rest — Periodic Labs, DYNA, and UltraGreen — feature strong founding teams or novel tech, suggesting investors are prioritizing talent and product over traction. As falling training costs fuel more vertical AI startups, team quality and product potential will remain critical funding criteria.

Want to submit your company’s funding data? Please reach out to researchanalyst@cbinsights.com.

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Every big tech company and a rapidly growing private market landscape are building agent offerings targeting enterprise use cases and industries from financial services to manufacturing.

For enterprises across sectors, one question is becoming unavoidable: Which AI agent strategies will separate market leaders from those left behind?

Enterprises are under pressure to build and implement agents as these LLM-based systems change how companies operate, hire, and scale.

Across 9 reports, discover where startup innovation is pointing, promising partnership and acquisition targets, and key trends to watch based on CB Insights predictive intelligence. Download the report for free.

This 68-page report covers: 

Foreword from Manlio Carrelli, CEO of CB Insights

Outlook on AI agents

6 AI agent predictions looking into 2026

The AI agent ecosystem — Who are the startups, infrastructure providers, and emerging revenue leaders to watch?

• The AI agent market map
• The AI agent tech stack
• The AI agent revenue race

AI agents make inroads across enterprise workflows — How are agents reshaping coding, customer service, and backend operations at scale?

• Y Combinator’s 2025 Spring batch reveals the future of agentic AI
• Building the agent economy: How cloud leaders are shaping AI’s next frontier
• The summer of vibe coding is over — How reasoning models broke the economics of AI code generation

Industry applications gain momentum — Where are vertical-specific agents gaining adoption and delivering measurable ROI?

• 3 markets fueling the shift to agentic commerce
• The industrial AI agents & copilots market map
• 100 real-world applications of genAI across financial services and insurance

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GenAI and agentic AI are expanding attack surfaces and fueling a booming AI security market. While companies are actively hiring for AI and security talent, as revealed by CB Insights’ Hiring Insights Agent, they have also opted for M&A as a faster way to seize the opportunity and integrate AI security features into existing offerings.

Most of these acquisitions are early-stage end-to-end platforms that combine offensive capabilities (red teaming, vulnerability management) with defensive features (incident response, alerts). Over half of the targets were Series A or earlier, 75% raised under $60M, and only one was generating revenue.

This points to a future where AI security becomes a table-stakes feature embedded in broader cyber offerings, rather than a standalone platform — driving large players to continue their acquisition spree.

Using CB Insights’ predictive signals, including Mosaic company health scores and M&A probability, we’ve identified the AI security startups that are ripe for acquisition next. 

See the top AI security startups ranked by M&A probability here

Key takeaways

• Securing the data pipeline will be a new frontier in AI security. Companies with the highest M&A probability in this group focus on securing AI data pipelines, including TrustLogix (with a 56% chance of being acquired in the next 2 years) and Protecto (with a 64% chance). Both are early-stage and have not received funding past Seed. Their combination of strong Mosaic (more than 1.5X the overall average of 370) and low funding positions these companies as low-cost acquisition targets that could add value in industries with highly sensitive data.
• Partnerships with large tech firms signal product validation, with 5 out of the 8 interest zone companies having partnered with large tech or cyber firms. 2 companies partnered with Snowflake for data privacy, 2 partner with Amazon, 1 integrates with OpenAI ChatGPT, and 1 has an ex-Google CISO on board. This market is nascent, with all companies either validating or deploying solutions and having raised at most Series A funding. In a market that’s so early-stage, partnerships are a proxy for market validation to potential acquirers.
• C-suite tech expertise is a talent acquisition opportunity: 50% of founding teams  come from large tech companies. The CEO of HydroX AI spent time at Meta, HP, and LinkedIn, the C-suite of Protecto comes from Microsoft, Apple, and Oracle, and the founding team at Lasso Security comes from SAP and Check Point. This expertise at the highest levels makes these companies potential targets for acquirers looking to bring AI talent in-house. As Check Point CEO Nadav Zafrir noted in a recent earnings call, “in this AI world, it’s all about talent and the critical mass of talent that you can bring into the organization.” 

Want to submit your company’s profile data? Please reach out to researchanalyst@cbinsights.com.

The post The AI security gold rush: who will be snapped up next? appeared first on CB Insights Research.

For professional services firms, this momentum poses an existential question: What happens to the traditional consulting model when clients have direct access to AI-powered expertise? 

The industry is already pivoting. McKinsey has deployed around 12,000 AI agents internally to support consultants and enable leaner project teams. Meanwhile, Accenture announced in June that it would merge five units into a new “reinvention services” business line aimed at helping clients overhaul their operations with AI.

Across the sector, leading strategy and tech consulting firms have collectively pursued more than 100 AI agent-related partnerships, investments, and acquisitions since 2023, per CB Insights data.

Note: Graphic features publicly disclosed or otherwise confirmed relationships announced since 1/1/2023, focusing on AI agents & related infrastructure. It is not intended to be exhaustive. Click to enlarge.

The stakes are clear: with momentum building but enterprise pilots often stalling, the firms that move beyond advice to building and orchestrating will shape the future of the AI economy.

Drawing on CB Insights predictive intelligence and interviews with senior AI and data executives, this report offers a forward-looking roadmap for how professional services firms can capture value over the next two years and beyond. Our analysis points to four key strategic opportunities: 

1. Orchestrate the AI agent tech stack: Guide clients through implementation, integration, scaling, and governance of AI agents across fragmented infrastructure, from model selection to trust and performance management.
2. Activate proprietary data for intelligent agents: Turn firm and client data into fuel for more capable, context-aware agents — differentiating through domain-specific IP, data infrastructure, and strategic partnerships.
3. Turn services into scalable AI products: Evolve from custom consulting engagements to platform-based delivery, industry-specific solutions, and new pricing models.
4. Build the human-AI workforce: Redesign internal talent models to work alongside AI agents while shaping offerings to help enterprises adopt and manage AI, from agents to robots. 

Download the full report to discover leading firms’ strategic moves, where startup innovation is pointing, and promising partnership and acquisition targets — based on CB Insights predictive intelligence on the world’s companies and markets.

Table of contents

• Orchestrate the AI agent tech stack
  • Core consulting workflows see AI agent disruption
  • Firms are already leaning in to build the stack
  • An AI ecosystem model is emerging
  • Private market momentum points to key orchestration opportunities
  • Agent reliability failures drive demand for governance layer
• Activate proprietary data for intelligent agents
  • Leading firms build out the data layer for agentic AI
  • Firms create data moats around core practice areas
• Turn services into scalable AI products
  • Platform strategies replace project-based delivery
  • Strategic startup investments reinvent the consultant’s toolkit
  • Shifting service delivery drives pricing model experimentation
• Build the human-AI workforce
  • Professional services firms face a workforce restructuring
  • AI-native startups preview the future efficiency of enterprise workforces
  • Managing AI becomes a new competitive moat

How we built this report: Over the last 9 months, we conducted in-depth interviews with senior leaders at leading professional services firms to understand the impact of AI agents on the future of the industry. To map out where the landscape is headed, we used CB Insights’ Market Intelligence Agents to interpret signals from across CB Insights data, including the following datasets and primary research: 

• Industry partnerships, deal activity, earnings transcripts, and proprietary scores for company momentum
• Surveys with enterprise leaders and briefings with AI agent startups

Get full access with your free CB Insights trial: https://www.cbinsights.com/trial-signup/ 

Scope of professional services: This report focuses primarily on strategy and technology consulting firms (including the Big Four). While the industry extends to legal, engineering, and specialized advisory services, our analysis centers on firms that directly compete with or complement AI agent capabilities in enterprise transformation and technology implementation.

Note: Named firms did not review the data contained in this report. 

In upcoming research on the future of professional services, we’ll dive deeper into competition in the “service-as-software” market and firms’ evolving talent models.

The post The Future of Professional Services: How firms will capture value in the AI agent era appeared first on CB Insights Research.

Since 2022, the share of legal tech equity deals going to AI-focused companies has climbed from 25% to 63% in 2025 YTD. So far this year, over 95% of equity funding going to legal tech startups has gone to AI-focused companies.

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Since we published our first agent revenue ranking in July, the sector has continued to gain significant momentum: Harvey reached the $100M revenue threshold, category leaders like Sana Labs are exiting (Workday’s $1.1B acquisition), and startups like Sierra are raising mega-rounds ($350M Series D at a $10B valuation).

Using CB Insights revenue data, we identified the top private startups generating $10M+ in revenue that offer AI agents as their primary offering and analyzed their revenue performance across categories (see the graphic below). 

If you are an AI agent startup and want to submit your company’s revenue data, please reach out to analyst@cbinsights.com.

See the top AI agent startups ranked by revenue here

Key takeaways 

• Coding AI agents are racing ahead in commercialization, with 6 software development agents making the top rankings, including market leaders like Anysphere’s Cursor ($500M in ARR) and Replit ($150M). These startups have demonstrated that they’re the most capital-efficient category, averaging $1.4M revenue per employee (compared to $594K per employee across all top agent categories). We recently identified the leaders of coding AI by market share — read more here.
• Customer service AI agents command the highest valuation premiums, averaging 219x revenue multiples (compared to 80x across all top revenue-generating AI agents). This valuation gap reflects investor confidence in the sector’s applicability and the expectation that businesses will rapidly replace human support teams with AI agents.
• These revenue leaders average just 3.8 years old. Despite their youth, CB Insights’ Commercial Maturity data shows the majority are already deploying or scaling their products, demonstrating the industry’s compressed timelines from startup to commercial success.

The post The AI agent revenue race — September’s top earners show coding dominates commercialization appeared first on CB Insights Research.