Given this momentum, the ecosystem is larger and more challenging to navigate than ever.

Our annual AI 100 list is designed to cut through this noise and highlight the next wave of AI winners, with a focus on early-stage players that are showing strength in terms of market traction, investor quality, and talent.

Leveraging CB Insights datasets such as deal activity, industry partnerships, team strength, investor strength, patent activity, and our proprietary Mosaic Scores, we selected 100 winners out of a cohort of 17K+ companies. We also analyzed CB Insights’ exclusive interviews with software buyers and dug into Analyst Briefings submitted directly to us by startups.

Below, we map out the winners, categorizing them based on their core offering. Key trends and category definitions follow. Customers can track activity of all of these companies in this watchlist. 

Please click to enlarge. Data as of 4/23/25.

CBI customers: Get data on the winners

Key takeaways on the AI 100

1. AI agents dominate the conversation. These applications, which automate tasks and processes for human users, are the next wave of genAI. Having made their way into virtually every horizontal and enterprise function, AI agents are also coming for infrastructure and verticalized applications. AI agents and supporting infrastructure make up 21% of this year’s companies, and the investors we spoke with consistently cited this space as a priority. 
2. ML security has become table stakes. The need to secure AI applications has grown in lockstep with the proliferation of genAI and agentic AI. 46% of strategy team leaders point to security as the primary barrier to genAI adoption, according to a recent CB Insights survey. Machine learning security companies are hardening AI algorithms and foundational models like LLMs, while also defending against increasingly sophisticated AI-powered attacks. 
3. AI observability and governance are critical gaps. Widespread use of AI is exposing the technology’s cracks — hallucinations, lack of orchestration, and output inaccuracies. It’s clear that AI ubiquity can’t exist without robust monitoring. Companies are rising to meet this need. Startups in this year’s list cover areas like observability and governance, while a small cohort also monitors AI agents to ensure reliability and compliance.   
4. The future is physical. Looking ahead, AI will evolve beyond software AI agents to a physical state. Advances in disparate areas of AI development — including robotics, multimodal image and voice models, edge computing, synthetic data, and spatial intelligence — provide the scaffolding for physical AI, which pairs AI software with hardware to take action in physical environments. Industrial humanoids represent an early manifestation of this, while future permutations could include fully autonomous defense drones, home companion robots, and more.
5. Vertical applications are exploding. In 2024, the horizontal companies in this AI 100 cohort received more funding than their vertical and infrastructural counterparts — $1.6B compared to $1.2B each for infrastructure and vertical. But in 2025 so far, the funding picture looks very different: Vertical winners lead the way with $1.1B in funding raised.

Category breakdown

AI INFRASTRUCTURE 

On the foundation model front, infrastructure newcomers are rapidly releasing models that rival industry leaders, signaling a maturing market where technical excellence and novel approaches increasingly compete with raw computing power. We identified winners across large language, edge, reasoning, small language, and multimodal models. 

Meanwhile, as AI applications — particularly agents — become more autonomous and widespread, the need for robust monitoring, governance, and cybersecurity solutions has grown in lockstep. 

We’ve heard this in our conversations with AI investors, as well. Mozilla Ventures, a lead investor in Credo AI, views governance as a strategic imperative. Mohamed Nanabhay, Managing Partner, notes: 

 “…We think that the AI governance sector itself will take on a crucial role of creating value for enterprises, allowing companies that leverage governance to deploy AI faster through the reduction of risk with a greater competitive advantage as a result.”

Category definitions:

DATA

• Synthetic data: Artificially generated or altered information that mimics real-world data without privacy concerns. Aaru uses a multi-agent approach to create population simulations for predictive decision-making applications like consumer behavior and electoral modeling.  
• Data preparation & curation: Tools and platforms that clean, transform, label, and organize data to make it suitable for AI training and deployment, encompassing data cleaning and specialized data processing. Unstructured, for instance, helps organizations capture unstructured data from various documents and convert it into AI-friendly formats such as JSON to train LLMs.
• Vector databases: Solutions that provide enterprises with an easy way to store, search, and index unstructured data at a speed, scale, and efficiency that current relational (and non-relational) databases cannot offer. For example, Qdrant provides an open-source vector database that allows developers to build production-ready applications that use nearest neighbor search functionality.

DEVELOPMENT & TRAINING

• Foundation models: Pre-built AI algorithms and architectures that can be deployed, fine-tuned, or integrated into applications, spanning general-purpose foundation models and specialized domain-specific models. This category includes large language, edge, reasoning, small language, and multimodal AI models. For instance, Archetype AI‘s Newton model processes multimodal sensor data and natural language to provide insights and predictions about physical environments.
• Agent building & orchestration: This category covers AI agent development platforms for building, orchestrating, and monitoring agents. Companies like LangChain provide a framework for building context-aware reasoning applications with tools for debugging, testing, and monitoring app performance across the entire application lifecycle.
• Computer vision & spatial intelligence: Technology that enables AI systems to understand, interpret, and interact with physical spaces and 3D environments, including mapping, navigation, and spatial data processing capabilities. Notably, World Labs develops Large World Models (LWMs) that enable AI systems to perceive, generate, and interact with both virtual and real 3D environments using spatial intelligence.

OBSERVABILITY & EVALUATION

• AI observability platforms: These platforms monitor, measure, and assess AI model performance, reliability, and outputs, including tools for testing, benchmarking, and continuous improvement of AI systems. For instance, Arize’s platform allows teams to monitor, diagnose, and improve the performance of AI models and applications in production through tools based on open-source standards that integrate with existing AI infrastructure.
• Governance: Solutions that establish policies, processes, and controls for responsible AI development and deployment, covering risk management, compliance, ethical oversight, and transparency requirements. For example, Credo AI offers a platform that automates AI oversight, risk management, and regulatory compliance while providing AI auditing to ensure system integrity and fairness.
• Machine learning security (MLSec): Technologies that protect AI systems from vulnerabilities, attacks, and data breaches, including techniques for securing model training, inference, and data pipelines. Solutions developed by companies like Zama enable computation on encrypted data, allowing for privacy-preserving machine learning across industries that require data privacy and security.

ACCELERATED COMPUTING & HARDWARE

• Edge: Platforms that provide the infrastructure and models to operate AI on “edge” devices such as tablets, IoT, autonomous vehicles, or smartphones. For example, EdgeRunner AI constructs an ensemble of small, task-specific models that work together to solve complex problems locally on devices, ensuring data privacy and security for heavily regulated industries.
• Photonics: Solutions that use light (photons) instead of electrons for data processing, with the potential to significantly increase computing speeds. Companies in this category provide memory, interconnects, and system architecture. Xscape Photonics develops bandwidth-efficient photonics solutions to support AI/ML infrastructure. 
• Quantum: Companies providing novel techniques like model compression and hardware to support quantum commercialization. Multiverse Computing provides AI model compression technology to enable quantum AI workloads and processing.
• Chips: Traditional chips, in addition to chips to support new AI technologies. Etched develops chips designed specifically for transformer inference, capable of processing extensive data for applications such as real-time voice agents and content generation.

HORIZONTAL AI

This category includes industry-agnostic solutions across visual media, text, code, audio, and interfaces. These function-specific solutions address common business needs regardless of industry, offering specialized intelligence that complements both vertical applications and foundational infrastructure.

AI agents in particular are beginning to upend the way in which enterprises think about software. Decibel Partners, a lead investor in multi-agent platform Dropzone AI, sees a movement toward productizing agents as full systems. Jéssica Leão, a Partner at Decibel, articulates this vision further:

“…We’re going to see the software world change because, again, you’re selling agents almost as if you’re selling back-end software.”

Horizontal AI solutions are increasingly tailored to serve distinct business functions while remaining broadly deployable. Startups in this category are developing sophisticated AI systems that excel in capabilities like content generation, customer support, process automation, and software development — all of which can be applied across industries. 

Category definitions:

• Content generation: AI systems that create text, images, video, and other media forms — spanning automated content production and multimodal generation. For example, Moonvalley’s genAI video model helps filmmakers by enabling prompt adherence, motion generation, and physics simulation using cleaned, fully licensed data.
• Customer service: AI agents that autonomously handle customer service tasks or augment human agents. Sierra‘s platform, for instance, provides intelligent agents for customer support that engage in personalized interactions and integrate with existing call center technologies.
• Cybersecurity: AI-powered solutions that detect, prevent, and respond to digital threats, vulnerabilities, and attacks, covering network security, threat intelligence, and automated incident response. Companies like Binarly use AI to detect and remediate vulnerabilities in firmware and software supply chains.
• General-purpose humanoids: AI systems embedded in robotic bodies that mimic human capabilities, enabling physical interaction through perception and manipulation. For example, Figure develops autonomous humanoid robots that combine human-like dexterity with AI to perform a variety of tasks across industries like manufacturing, logistics, warehousing, and retail.
• Process automation: Intelligent systems that autonomously handle repetitive business workflows, increasing efficiency by eliminating manual tasks. Orby AI offers a platform that observes enterprise processes and generates executable automations — particularly for complex, data-heavy operations in industries like tech and finance. 
• Software development & coding: AI solutions that assist with software development, code generation, debugging, and programming tasks, including automated code completion tools. For instance, Poolside offers foundation models and APIs that can be fine-tuned using a company’s own codebase and documentation to support internal dev teams.
• Video security: Technologies that enable real-time analysis of video feeds, supporting faster detection and response to security threats. Coram AI develops cloud-based security camera systems with features like real-time AI alerts and natural language video search, allowing businesses to monitor properties remotely without extensive hardware replacements.

VERTICAL AI 

Vertical AI is on the rise, with this year’s vertical winners surpassing the other category winners to capture over $1B in combined funding in 2025 YTD. They span 10 industries that represent a convergence of high-value problems, rich data availability, and regulatory momentum.

Some of the VCs we spoke with see specialization as the way of the future. Lila Tretikov, Partner and Head of AI Strategy at New Enterprise Associates (a lead investor for Twelve Labs, World Labs, and Orby AI), told us:

“We believe that there is going to be specialization, even within the model layer. And there’s going to be innovation in this layer, especially as we look at verticalization for specific use cases.”

The most well-represented verticals on this year’s list are healthcare (8 companies) and life sciences (6 companies). The healthcare industry as a whole is seeing breakthrough applications across multiple AI modalities — from agentic AI systems that can augment clinical workflows, to advanced machine vision for medical imaging analysis, to AI-accelerated drug discovery platforms that dramatically reduce R&D timelines.

This year’s cohort also saw significant representation in gaming & virtual assets (5 companies), finance & insurance (4 winners), and aerospace & defense (4 winners). 

Category definitions:

• Aerospace & defense: AI solutions designed for aerospace engineering, aviation operations, military applications, and defense systems, including autonomous navigation and threat detection technologies. For instance, Quantum Systems creates eVTOL unmanned aerial systems that serve critical defense applications, most notably in Ukraine. 
• Auto & mobility: AI applications for autonomous vehicles, transportation optimization, fleet management, and mobility services. Companies like Wayve are developing AI systems that use LLMs to provide real-time natural language explanations of driving decisions, helping improve users’ confidence.
• Energy: Platforms that optimize energy production, distribution, and sustainability, including battery intelligence and AI assistance for electric grids. For example, Liminal leverages ultrasound and machine learning inspection solutions to improve battery cell quality, cost-effectiveness, and safety while enabling confident scaling of production. 
• Finance & insurance: AI solutions for financial services, banking, investment, and insurance sectors, covering payments, risk assessment, and portfolio monitoring. Skyfire’s financial stack enables AI agents to perform transactions without credit cards or bank accounts, allowing businesses to monetize their products, services, and data through AI agents.
• Gaming & virtual assets: AI technologies that enhance gaming experiences, virtual environments, digital asset management, and immersive entertainment, including content generation and NPC (non-player character) intelligence. Altera‘s platform creates digital human beings that can interact with users and perform tasks autonomously, bringing empathy and human-like traits to digital interactions.
• Healthcare: AI applications focused on clinical care delivery, medical operations, and patient management, including tools for clinical documentation automation, medical imaging analysis, decision support systems, remote patient monitoring, and healthcare supply chain optimization. In the dental field, Overjet provides an AI platform that enhances clinical care through radiographic analysis and optimizes claims processing for providers and payers.
• Life sciences: AI solutions for pharmaceutical research, drug discovery, protein engineering, biological data analysis, and therapeutic development, including platforms for multiomics analysis, antibody design, foundation models for biology, and scientific experiment automation. Lila Sciences has developed a platform that integrates AI with autonomous laboratories to design, conduct, observe, and redesign experiments for scientific discovery.
• Legal: AI tools for legal research, document analysis, contract management, compliance, and legal workflow automation, including case management, due diligence, and contract review. AI-powered tools like Eve help law firms streamline the full case lifecycle from intake to litigation by automating case intake, drafting legal documents, and managing discovery processes.
• Manufacturing: Technology that optimizes industrial processes like factory automation, using virtual development and simulation. PhysicsX applies machine learning to physics simulations that optimize design and engineering processes across industries including aerospace, medical devices, and electric vehicles.
• Supply chain: AI solutions that enhance logistics and supply chain operations, including warehouse management and route optimization & visibility. Dexory combines stock-scanning robots with a digital twin platform to provide real-time inventory and warehouse analytics for logistics and supply chain operations.

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The post AI 100: The most promising artificial intelligence startups of 2025 appeared first on CB Insights Research.

While AI copilots have already made inroads across industries, the next evolution — autonomous agents with greater decision-making scope — is arriving quickly. AI agent startups raised $3.8B in 2024 (nearly tripling 2023’s total), and every big tech player is already developing AI agents or offering the tooling for them.

Implications for enterprises will be far-reaching, from altering workforce composition (with new hybrid teams of humans and AI agents) to maximizing operational efficiency through full automation of routine tasks. 

Below we identify 170+ promising startups developing AI agent infrastructure and applications. 

CBI customers: Track 250+ AI agent companies

We selected companies for inclusion based on Mosaic health scores (500+) and/or funding recency (since 2022). We included private companies only and organized them according to their primary focus. This market map is not exhaustive of the space.

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. 

Outlook on AI agents

Fully autonomous agents remain limited due to issues pertaining to reliability, reasoning, and access. Most agent applications today operate with “guardrails” — within a constrained architecture where, for example, the LLM-based system follows a decision tree to complete tasks. 

Agents featured on this map include some combination of the following components: 

• Reasoning: Foundation models that enable complex reasoning, language understanding, and decision-making. These models evaluate information and form the cognitive core of the agent.
• Memory: Systems that store, organize, and retrieve both short-term contextual information and long-term knowledge.
• Tool use: Integration capabilities that allow agents to interact with external applications, APIs, databases, the internet, and other software. 
• Planning: The agent’s architecture for breaking down complex tasks into more manageable steps, reflecting on performance, and adapting as necessary.  

We expect more startups to move up the scale of autonomy as AI capabilities advance. Improvements in reasoning and memory will enable more sophisticated decision-making, adaptability, and task execution.

For example, in September 2024, legal AI startup Harvey announced that OpenAI’s o1 reasoning model, supplemented with domain-specific knowledge and data, was enabling it to build legal agents. The company, which raised $300M at a $3B valuation in February 2025, has doubled its sales force in the last 6 months, indicating rising market demand.

While the above market map highlights the private landscape (with a focus on enterprise applications), tech giants and incumbents are also launching agents. We predict big tech and leading LLM developers will own general-purpose AI agents, but there are many opportunities for smaller, specialized players. 

Looking ahead, watch for new form factors outside of the copilot/chatbot interface that will push the boundaries of what an “agent” is. Early indications of this include “AI-native” workspaces — tools and platforms built from the ground up around AI capabilities, rather than layering AI features on top of a traditional product. For instance:

• Eve’s legal platform aims to automate aspects of the whole case lifecycle (from case intake to drafting). 
• Hebbia’s Matrix product builds spreadsheets that mine information from files (in rows) and deliver answers to questions (in columns), proactively discovering, organizing, and surfacing data.
• With its Dia product, The Browser Company is exploring web browsing interfaces that can summarize content, automate repetitive web tasks, and even anticipate next actions.

Category overview

AI agent infrastructure

This segment covers companies building agent-specific infrastructure. (We excluded general genAI infrastructure markets like foundation models and vector databases from the map.)

Development tools

A diverse ecosystem of tools has emerged to support agents’ development. These range from memory frameworks like Letta that enable persistent, retrievable memory across interactions; to tools that allow agents to take action via integration (e.g., Composio), authentication (e.g., Anon), and browser automation (e.g., Browserbase).

Another set of companies is giving agents more utility across payments (which includes companies developing crypto wallets for agents as well as virtual cards) and voice (development platforms and tools for testing AI voice applications as well as speech models).

Meanwhile, demand for simplified, comprehensive deployment options is driving the rise of AI agent development platforms — the most crowded infrastructure market on our map. 

LLM developers including Cohere (with its North AI workspace) and Mistral have launched their own agent development frameworks, while Amazon, Microsoft, Google, and Nvidia all offer AI agent development tooling. With many enterprises favoring established vendors due to lower risk, big tech companies have significant advantages here.

Trust & performance

Concerns around reliability and security have helped establish a market for agent evaluation & observability tools. Early-stage companies are targeting applications such as automated testing (e.g., Haize Labs) and performance tracking (e.g., Langfuse). 

Multi-agent systems, where specialized sub-agents work together to complete tasks, also show promise in improving accuracy. Insight Partners-backed CrewAI’s multi-agent orchestration platform is reportedly already used by 40% of the Fortune 500. 

Vendors are also tackling reliability concerns directly. Based on our briefings with 20+ AI agent startups in Q1’25, companies are using 5 primary methods to build user trust: 

1. Transparency
2. Human oversight
3. Technical safeguards
4. Security & compliance
5. Continuous improvement 

Horizontal applications & job functions

Horizontal AI agent startups make up nearly half of the map and overall landscape. 

This segment primarily features startups targeting enterprises, with industry-agnostic applications across job functions like HR/recruiting, marketing, and security operations. Companies in the productivity & personal assistants market, including OpenAI with its Operator agent, are targeting consumers and employees directly.  

The AI agent markets with the most traction — based on companies’ median Mosaic health scores — are customer service and software development (which includes coding and code review & testing agents). These markets are also among the most crowded due to the value agents bring to well-defined workflows and testable environments. 

We see this reflected in adoption, particularly at the customer service layer: Among 64 organizations surveyed by CB Insights in December 2024, two-thirds indicated they are using or will be using AI agents in customer support in the next 12 months. 

Overall, horizontal AI agent applications are more commercially mature compared to the infrastructure and vertical segments, with over two-thirds of the market deploying or scaling their solutions based on CBI Commercial Maturity scores. 

Vertical (industry-specific) applications

We expect increasing verticalization as startups carve out niches by solving industry-specific customer problems, especially in areas with strict regulatory scrutiny and data sensitivity.

This category features companies catering to industries including: 

• Financial services & insurance: The most crowded vertical category on the map with 11 companies, startups here are targeting a variety of finserv workflows such as financial research (Boosted.ai and Wokelo), insurance sales & support (Alltius and Indemn), and wealth advisory prospecting & operations (Finny AI and Powder). 
• Healthcare: Solutions in this market aim to reduce the volume of manual tasks for healthcare professionals across use cases like clinical documentation, revenue cycle operations, call centers, and virtual triage. Solutions from companies like Thoughtful AI (revenue cycle operations) and Hippocratic AI (staffing marketplace) are targeting end-to-end healthcare workflows. 
• Industrials: These companies look to optimize processes and equipment — including control systems, robots, and other industrial machines — without relying on consistent human intervention. For example, Composabl launched an agent platform in May 2024 that uses LLMs to create skills and goals for agents that can control industrial equipment. Public companies like Palantir are also active in this space. Learn more in our industrial AI agents & copilots market map. 

RELATED RESEARCH

• What’s next for AI agents? 4 trends to watch in 2025
• The enterprise AI agents & copilots market map
• The Multi-Agent AI Outlook
• Future of the workforce: How AI agents will transform enterprise workflows

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

These LLM-based systems mark an evolution beyond copilots: AI agents can accomplish complex tasks on a user’s behalf with minimal intervention, from sales prospecting to compliance decisioning. 

In the rapidly growing landscape for agent infrastructure and applications, over half of companies in the market have been founded since 2023. Meanwhile, funding to startups in the space nearly 3x’d in 2024.

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The post What’s next for AI agents? 4 trends to watch in 2025 appeared first on CB Insights Research.

Imagine: your personal AI agent notifies you that a hair dryer you’ve been eyeing is now on sale. The product page highlights benefits tailored to your curly hair, while the agent confirms it will arrive before your upcoming trip.

With your approval, the agent handles the purchase through your secure wallet. Later, it proactively suggests complementary hair care products for the summer season.

This world of autonomous commerce isn’t as far off as it seems. Tech and e-commerce leaders — including OpenAI, Nvidia, Amazon, Walmart, Google, and Apple — are already building AI systems that are steps away from conducting transactions. 

AI agents will impact each stage of the customer journey, streamlining the path to purchase and fundamentally transforming how businesses build relationships with consumers and drive loyalty.

We use CB Insights data on early-stage fundraising, public companies, and industry partnerships to analyze how generative AI — especially AI agents — is transforming the customer journey.

In the 11-page report, we cover 3 predictions that emerged from our analysis: 

1. First-party transaction data will shape the future of AI-driven personalization. As personalization becomes more sophisticated at the awareness and consideration stages, companies with direct access to first-party data will have an edge.
2. Direct-to-agent (D2A) commerce will kill traditional loyalty. With AI agents handling browsing and shopping, traditional loyalty programs will lose effectiveness as agents optimize shopping across a select group of merchants.
3. A few AI agents will own the customer relationship. Companies like Amazon, Google, and Apple — with critical distribution and financial services infrastructure — are well-positioned in commerce.

RELATED RESEARCH FROM CB INSIGHTS

• How generative AI could supercharge retailers’ ad networks
• AI is revitalizing the “virtual try-on” space — this is what’s coming next, including interactive Google ads
• The enterprise AI agents & copilots market map
• 15 tech trends to watch closely in 2025
• Generative AI — e-commerce search
• AI-optimized e-commerce UX
• Generative AI — customer support operations

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The post The future of the customer journey: AI agents take control of the buying process appeared first on CB Insights Research.

Prefer to listen in? Check out our discussion of the report here: 

An empowered digital workforce would reshape industries as we know them. The implications would be enormous, changing how companies hire and scale, as well as what they can achieve with a small headcount. 

That future isn’t too far off. 

The idea of autonomous AI agents — LLM-powered bots that can independently reason and execute tasks — caught on like wildfire in 2023, marking an important evolution beyond chatbots and copilots. 

OpenAI CEO Sam Altman has described agents as “AI’s killer function” as recently as May 2024.  

While much of the tech remains limited in its ability to execute tasks reliably, use cases are gaining traction in horizontal enterprise applications like customer support, sales, and engineering.

We mined CB Insights startup, financing, business model, and buyer interview data to map the evolving landscape and analyze its future. 

In the 28-page report, we cover: 

• The state of AI agents: Investment is surging to companies in the space, but limitations — most notably, agent reliability — remain. 
• Leading horizontal applications and impacts: The landscape of VC-backed agent startups is dominated by a focus on horizontal applications — across sales, customer support, and other enterprise and general productivity workflows.
• Emerging industry applications and opportunities: While few agentic companies focus on single industries, companies are emerging to target workflows across financial services, industrials, and more. 

Download the full report to get all of the data and analysis.

The post Future of the workforce: How AI agents will transform enterprise workflows appeared first on CB Insights Research.

Brands and retailers are increasingly investing in tech-enabled customer service solutions that can help support and convert customers — either online or in stores — in a timely and cost-efficient manner.

Using CB Insights data, we examined tech markets across customer service for consumer & retail leaders and ranked them across two metrics — market momentum and industry leader activity — to help companies decide whether to monitor, vet, or prioritize these technologies.

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