Machine Learning Startups funded by Y Combinator (YC) 2026

Flock Safety provides the first public safety operating system that empowers private communities and law enforcement to work together to eliminate crime. We are committed to protecting human privacy and mitigating bias in policing with the development of best-in-class technology rooted in ethical design, which unites civilians and public servants in pursuit of a safer, more equitable society. Our Safety-as-a-Service approach includes affordable devices powered by LTE and solar that can be installed anywhere. Our technology detects and captures objective details, decodes evidence in real-time and delivers investigative leads into the hands of those who matter. While safety is a serious business, we are a supportive team that is optimizing the remote experience to create strong and fun relationships even when we are physically apart. Our flock of hard-working employees thrive in a positive and inclusive environment, where a bias towards action is rewarded. Flock Safety is headquartered in Atlanta and operates nationwide. We have raised $150M in our Series E led by Tiger Global at a $3.5B valuation.

Scale accelerates the development of AI within organizations of any size to deliver critical business insights and operational efficiency. Its data-centric infrastructure platform leverages RLHF (Reinforced Learning with Human Feedback) to help organizations build the strongest AI models that supercharge their business, with customers across industries including Meta, Microsoft, U.S. Army, DoD’s Defense Innovation Unit, Open AI, General Motors, Toyota Research Institute, Brex, Instacart and Flexport.

Lyon builds private foundation models for banks, insurance companies, and fintechs. Each model learns from the company’s proprietary transactions, payments, clicks, and customer interactions to predict credit, fraud, collections, income, churn, and lifetime value; and runs inside the company’s cloud, so its data and intelligence remain under its control. Lyon is already working with a major insurer and trained a model on 28B transactions for a fintech serving tens of millions of active users, identifying premium-card converters 4x more precisely than its existing rules; the model is now being deployed for credit.

Dream makes AI cameras that catch damage teams miss. Car dealerships, equipment rental companies, fleets, and more move expensive assets in and out all day, and undocumented damage is a costly leak. Dream cameras are simple to set up and work automatically. Each vehicle or machine that passes is photographed from every angle, identified, and reconstructed into a complete condition record. On return or intake, Dream instantly flags new dents, scrapes, and changes your team might miss, giving one source of truth for every asset's condition over time. Ryland worked at X/xAI and built aerial damage-detection vision at Vexcel Imaging. Alexander worked at AWS and trained edge-camera models deployed in the wild across Africa and India to detect poachers.

PRINCEPS is the first purpose-built insurance company for the trillion-dollar compute buildout. We underwrite SLA, GPU residual value, and outage risk, and price the coverage that unlocks cheaper financing for data centers and neoclouds.

Mentlio an applied research lab focused on optimizing AI ROI for the modern enterprise. We help engineering teams draw a line between their AI spend and delivery, per engineer. We then cut ~30% of AI spend via Mentlio's custom ML-based intelligent routing, context optimization, and prompt compression.

Physical AI — robots and world models — is bottlenecked on one thing: diverse, real-world data at massive scale showing how humans actually do work in the physical world. It was never written down, and you can't buy it, because no one has ever captured it. Only real local experts, the people who do this work every day, can produce data this authentic and valuable. DeepReach builds the network that reaches them. We design and manufacture wearable stereo capture devices, then place them with local data partners who run collection as their own business — in warehouses, workshops, farms, kitchens and repair shops. They own the local relationships; we supply the hardware, the software platform, the quality pipeline and the payments. Anyone can run a data business. That structure is what produces diversity. Instead of thousands of hours inside one environment, we deliver hours spread across thousands of environments — which is what actually makes robot policies generalize. 475 devices in the field and 100+ data partners are live across 7 countries, adding new partners every week. They've collected nearly 150,000 clips in the past three months, with volume roughly doubling month over month. Our data is in production with multiple frontier model and robotics companies, and we're scaling to 10,000+ devices across 50 countries.

hiloop helps teams train agents for tasks where general models are not good enough. Give us a task, your current agent or model, and an evaluation. hiloop runs an autoresearch campaign across data, SFT and other post-training methods, continual learning, prompts, tools, harnesses, and systems, then returns the best verified improvement. It runs hosted or in your cloud. We provide the research system around models: persistent memory, full experiment lineage, compute orchestration, and statistical verification. We’re starting with agent and model training, continual learning, and optimization. Reach out to us for early access at founders@hiloop.ai.

Tracer is an AI research lab building more capable and efficient AI through model coordination. We research adaptive inference, ensembling, and systems in which multiple open-source models work together to produce capabilities beyond any single model. Our work spans reasoning, code, research, and agentic systems, with the goal of making advanced intelligence more accessible and economical.

We're scaling self-improving AI scientist teams to ideate/experiment/discover, generating new knowledge autonomously. Their discoveries reduced ARC-1/2 costs by 10,000× while maintaining state-of-the-art accuracy and yielded material advances on combinatorial ML problems. We believe that AI is bounded not by compute, but ideas. Scaling to millions of sessions of discovery enables leveraging its own discoveries and presents a novel training source capable of ushering a new era of self-reliant foundational models.

Belvedir makes it cheap and easy for anyone to make custom AI. We autonomously collect your data via trace SDK / app MCP / CUA agents and autonomously train custom models + harnesses + routers, privately deploy them, and recursively improve them as they are used. Our customers have seen 10x reductions in inference costs and 2x increases on benchmark scores. Custom AI models are going to win. Belvedir is the infrastructure needed to make trillions of them.

Agnost AI reads every conversation a company's AI Agent has with its users to find where it's silently failing and then turns that same data into custom models that run the agent better, faster & cheaper than frontier models.

Risklytics is the insurance company for frontier tech such as physical AI, robotics, and data centers. Companies answer 6 questions and we bind a tailored policy that other insurance companies won’t.

CoArena is a live arena where anyone can use the world's top computer-use models racing two of them on the same real computer task and judging which one did it better. Every battle becomes something the AI labs can't build themselves, an honest test of their agents on real work, and the data to make them better.

We train human brain cells for compute

Capital allocation is becoming a more important part of individual economic life. Finance is moving toward a global, onchain, 24/7 future with thousands of markets trading at once. The asset management industry is built around the scarcity of financial expertise and the cost of organizing people around capital. Institutions cover global markets by employing analysts, researchers, and quants around the clock. AI turns this organizational capacity into software. Instinct is building the harness for this transition. Instinct is a proactive team of agents that searches thousands of markets, investigates opportunities, and operates 24/7. The individual becomes the portfolio manager, Instinct becomes the investment firm operating around them. As institutional advantages become widely accessible, the edge concentrates in each trader’s worldview, judgment, and bets. The first one-person investment firm managing $1 billion will emerge within two years. It will run on Instinct.

PerfectBit builds learned policies for robots that do real work. We train manipulation and control policies that hold up outside the lab, in the unstructured environments where real tasks actually happen. Founded by two alumni of Meta's Superintelligence org: Péter Vajda led Media GenAI foundation-model R&D (Emu, Movie Gen, image and video editing), and Seiji Yamamoto led teams in the Core Llama group across LLM pre-training and post-training, inference optimization, and computer vision. We're early, deliberately quiet about the details, and hiring the first researchers who will shape what this becomes. San Francisco, on-site.

Datoric develops custom datasets for voice models, robotics, and world models, treating research, collection, verification, and production as one continuous process. We work closely with frontier model teams to turn emerging limitations and model failures into testable data hypotheses, while running our own experiments ahead of customer demand. This allows us to operationalize validated methods into repeatable collection systems at scale. Data is collected through private invite-only applications separated by modality, customer, and trust level. Every submission remains linked to the contributor, device, task, session, consent, rights, and processing history that produced it, giving our internal QA and fraud models the context to detect problems that may appear legitimate in the finished file. Each collection reveals new failure cases and quality signals that improve the systems behind the next dataset. Once a collection method is validated, we scale it up as a solution to the model failure and it also becomes a reusable data recipe for future custom projects or independently collected, rights-cleared data products.

Velum is the operating system for data quality. Velum automatically monitors and enforces data quality across a company's data stack, so bad data never reaches dashboards. We turn data quality from a manual task into infrastructure that runs itself. Data trust you can prove. From the pipeline to the boardroom.

Darwin is building general-purpose humanoid robots for real-world work. Our first robot, Darwin One, will learn from real-world experience and continuously improve as AI moves from the screen into the physical world. First deployments begin in early 2027.

Allus builds next-gen vision foundation models that bring real intelligence to manufacturing. Enabling factories to see, understand, and improve production in real time.

Hyperspell is your Company Brain. AI agents are brilliant and clueless. They ace any test and still have no idea how your company works. Hyperspell connects your tools and synthesizes documents and conversations into a live, permissioned context graph. Any agent can read from it and write back to it like a filesystem, with every fact traced to source.

Amika is infra to build your software factory. We give your team sandboxed AI coding agents in the cloud: the same infrastructure Ramp, Coinbase, and Stripe built in-house. Kick off work from a web UI, CLI, Slack, or API. Use your preferred coding agent (Claude Code, Codex, etc.). Each agent understands your codebase, runs autonomously, and ships real PRs with live previews of the apps it changed. Use it as your daily driver or plug it into your own code-gen pipeline via API.

Brickanta – agentic AI for society builders. Hundreds of construction-specific AI agents for project analysis, insights, tenders, procurement, and more. Combine the latest AI technology with your organization's project data, templates, and workflows to identify risks and opportunities early and produce better decision support. Brickanta has raised $8 million from leading AI, construction, and real estate investors behind companies like OpenAI/ChatGPT, Airbnb, Klarna, Spotify, and Plangrid-Autodesk, as well as star investors such as Mario Götze, Anton Osika, and Northzone (see movie and press). The founding team has been building and implementing AI in construction and industry since 2018 at companies such as ABB, Fabege, Husqvarna, IKEA and Konecranes.

Bezel helps fashion brands create virtual photo/video shoots with AI. Upload images of clothes, select the human you want to model it, and Bezel generates pictures and videos that rival a full studio production. Every detail of the clothing is rendered flawlessly. Try it for yourself.

OnDeck is the infrastructure layer that makes Vision Language Models accessible and scalable for enterprise. Our model, Perception-0, let organizations instantly find any object, behavior or event, in any footage, without needing to train a model or collect any training data. The Pain: Creating vision models usually takes months: collecting training data, training, then deployment. Worse yet: + it’s often impossible to get enough data for a specific task, and + even the best cv models struggle to generalize across diverse camera setups, workflows and environments. To overcome these blockers, we bet early on the power of VLMs and built a vision engine that can generalize across any task and doesn’t need any training data. We published a NeurIPS workshop paper showing our new methods with VLMs beat traditional CV even at niche tasks. Now, our model Perception-0 beats the best VLMs including Gemini at long video understanding. Our current customers include: - National Defense Organizations - Robotics Research - Security cameras - Behaviour analysis for port monitoring - Off-shore oil & gas monitoring

We are a full-service sell-side residential brokerage that lists and markets your properties. We also screen and schedule tenants showings with our AI agent for a third of the normal commission.

Lilac sells flexible GPU contracts and runs the software layer on top: monitoring, bare-metal provisioning, and managed Kubernetes and Slurm. We also run this layer for GPU cloud providers as their named operations and support partner.

DeepAware (Robotics Center of Silicon Valley, https://roboticscenter.ai/) is the fastest way for enterprises and researchers to get robots and robotics parts in the US — 72-hour delivery or Bay Area pickup. Beyond hardware, we help teams collect teleoperation data, build reinforcement learning environments, and deploy robots into production. Customers include AI labs, industrial operations, research teams, and event producers.

Kairos closes the last-mile reliability gap in AI deployments. We bring frontier techniques to companies in critical industries, deploying specialized agents that encode operator expertise and reliably automate their most manual workflows.

Kashikoi is a simulation engine to benchmark AI agents. We generate CPU friendly world models that autonomously interview agents and generate deep behavioral assessments. We built a similar technology at Moveworks which was used to ship 250+ enterprise agents to customers daily.

Photonium is building software to automate optical system design. We supercharge optics experts with intelligent tooling to reduce costs and deliver faster. We handle the full design stack — from optimization, verification, sourcing, to prototyping — for AR/VR, quantum, biotech, metrology/chip fab, LiDAR, and more.

Theorem is training models that make program verification 10,000 times faster. Using verification as a feedback loop, developers have found zero-days in GPU accelerated code and cryptography implementations, and sped up code migration in legacy systems. If you have complicated code that needs to be correct and secure, sign up for our beta!

A fully open-source, performant and actionable ML model training platform

Plexe builds predictive ML models from a problem description. It connects to data sources, conducts experiments, evaluates and deploys the models to an API endpoint.

AI models are terrible in non-English languages because it's nearly impossible to find training data in other languages. So, we're building the world's largest and highest-quality multilingual data library.

Nitrode is a research company focused on improving game development with AI. To advance game development, we believe AI models firstly need to be judged against robust evaluation frameworks that reflect the actual complexity of the field.

Osmosis is a post-training platform that helps companies fine-tune language models using reinforcement learning. We work with fast-growing AI companies to train task/domain-specific models that beat foundation models on performance, cost, and latency. Our platform handles compute orchestration, reward modeling, and training run observability as a CLI-based product usable by developers and agents.

Mecha Health builds foundation models to automate x-ray analysis for radiologists. We take medical images and process them using proprietary models to produce accurate draft medical reports. Our first model was built in less than two months, and beat Microsoft, Google, and OpenAI on clinical accuracy metrics. On top of that, it’s two orders of magnitude smaller and trained with a quarter of the data. We are partnering with the largest privately owned radiology practice in the US and a multinational tele-radiology company to provide them with their own foundation model, enabling their radiologists to go from reading 1 scan per hour to 1 scan every 5 minutes. By charging on a per scan basis, x-ray report generation represents a 40B+ market opportunity.

Metreecs helps retailers plan, buy, and allocate products using AI-demand forecasting. We prevent overstock and out-of-stock situations, allowing clients to eliminate waste, free up capital, and drive higher sales.

We're building Matcha, the faster, smarter way to hire clinical talent—helping hospitals find active, qualified candidates at a fraction of the time and cost of job boards or headhunters. Matcha engages candidates at scale, matching them to your organization by qualifications and cultural fit. Recruiters save time, hospitals save money, and candidates get a better experience—a radically better hiring model for everyone involved.

Archil transforms S3 buckets into a 30x faster, unlimited, local disk. Archil enables AI, analytics, and serverless applications to instantly access massive data sets without waiting for data transfer. Researchers use Archil for shareable, local storage of data set and model versions that never runs out of capacity.

With AI increasingly automating away code generation, software engineers will spend more time reading, judging, and architecting code rather than writing it. Storia is building an open-source copilot that knows a company's codebase and its context. We are starting with Sage, a Perplexity-like agent for helping developers understand, judge, and generate software. Given an existing codebase, developers can ask Sage questions such as: 1) Given my project’s SLA and latency constraints, what is the appropriate underlying vector database to use? How would I incorporate it into my existing codebase? 2) Why should I pick Redis over Milvus as my underlying vector store? 3) Does this codebase in our organization still work and what steps are required for a complex integration with another library? Sage’s answers are directly supported by documentation and external references like GitHub, Stack Overflow, technical design documents, and project management software, preventing hallucinations. Today, Sage has up-to-date knowledge about open-source repositories (indexed daily). Tomorrow it will have a deep understanding of every line of code on the Internet. For teams, Sage will know about your private codebase too. No group has yet solved how to build an AI system that comprehends a codebase and its context and can empower every developer to architect better code, faster. This requires new research advances because vanilla RAG and out-of-the-box LLMs aren’t going to cut it. We have 20+ years of software engineering and AI research experience. Julia worked on precursors of Gemini using contextual neural techniques before they were called “RAG” (and applied it to products like Google Keyboard and Pixel phones). Mihail built the earliest LLMs at Amazon Alexa and launched the first contextual deep learning conversational AI system in production at Alexa.

Simplex builds AI agents to handle provider enrollment end-to-end. We automate pulling provider data from CAQH, filling out enrollment forms + uploading licenses via payor portals/email/phone, updating applications when returned for corrections, and performing effective date follow-ups until a final determination is made, all autonomously.

We’re building the next generation of warehouse robotics. In the US today, retailers spend approximately $10B per year paying human laborers to pick up and move cardboard boxes in warehouses. Existing solutions for automating this are expensive and difficult to install, which is why manual operation is still so prevalent. Our solution is different. We make swarms of small mobile robots that install into existing warehouses to provide a low-cost and robust automation solution for case picking and mixed-SKU palletization. Our novel technology allows these robots to be installed and operate at significantly lower cost than existing solutions while being both flexible and robust.

Cloudglue is the video context layer for AI. We make it easy for your AI to understand video. - Tinycloud - your AI agent for video, now open for beta: https://tinycloud.cloudglue.dev - Developer API Platform: https://cloudglue.dev

MinusX is a state-of-the-art data agent that can build the best dashboards for your data, and notify you when something's up. You can interrogate all your data on our open source BI platform via Slack or MCP. Connect your data, and put agents to work.