Machine Learning Startups funded by Y Combinator (YC) 2025

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.

Velum is an open-source firewall for content-level access control across documents, databases, and applications. Admins define policies, and we apply them in real time across your entire stack to control what each recipient sees. Our firewall sits between your data sources and consumers (LLMs, users, vendors), enforcing policies on every request. We understand meaning in text, documents, and databases to detect sensitive information based on your policies, then apply recipient-specific controls without blocking workflows. Create policies in natural language. Teams write rules that define what's sensitive and who should access it, and Velum enforces them consistently across all systems. When authorization changes, we can reveal tokenized data just-in-time for approved recipients, maintaining both security and utility. We integrate directly into your existing infrastructure. For AI workflows, we intercept prompts, responses, and retrievals to prevent sensitive information from leaking into or out of models. For enterprise systems like Salesforce, SAP, or Workday, we extend policy enforcement to exports, reports, and syncs. Velum is designed for scale and compliance. We provide encryption and structure-preserving tokenization for high-throughput pipelines, with multi-cloud deployment options and configurable data residency per tenant to meet your compliance requirements.

Efference builds the eyes and visual cortex for robots. Instead of treating depth as a hardware problem, we use software — inspired by how humans see — to generate rich and reliable 3D information. This lets us deliver higher performance and greater ease of use at a lower price than existing stereo cameras.

Sciloop is building an AI Co-Scientist that automates experimentation and analysis for machine learning researchers, allowing them to test ideas far faster than today’s tools allow. We are Bilal and Osman, International Physics Olympiad medalists and the only Pakistanis admitted to MIT in our respective years, with several years of hands-on ML research experience inside MIT CSAIL. We have built large-scale training pipelines, worked with terabyte-scale datasets, and seen the research bottleneck firsthand while contributing to scientific ML projects. We left MIT to solve this problem directly and are now building a system that can eventually accelerate scientific discovery itself. https://sciloop.dev

We help oncologists and radiologists extract insights from CT scans to support tumor characterization and treatment. The hospitals we work with include Stanford Hospital, Cedars-Sinai (the largest hospital in California), UCI Health, and Weill Cornell.

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 (YC F25) gives AI agents memory. It connects to tools like Slack, Gmail, Notion, and Drive so agents can recall, reason, and remember across company knowledge. Developers use Hyperspell’s API to turn disconnected agents into AI coworkers that understand their work. Conor and Manu founded Hyperspell after building their own workplace agent and seeing how limited AI is without memory. They have over 15 years of experience in machine learning and have scaled API-based products to $30M ARR.

AI researchers should be pushing the boundaries of what's possible with new architectures and training methods. Instead, they waste weeks configuring cloud infrastructure, debugging distributed systems, and optimizing their GPU code. We know because we lived it: While training our own models across thousands of GPUs earlier this year, we spent more time fighting our infrastructure than doing actual research. That why we're building two things. First, Elastic Cloud: a managed platform that automatically finds the cheapest GPUs across all providers, handles spot instance preemption, and cuts compute costs by up to 80%. Second, automatic kernel optimization that makes training code run faster by modeling hardware topology, often beating hand-tuned implementations. The problem is that getting high performance across different hardware is genuinely hard. NVIDIA's CUDA moat exists because writing fast kernels requires deep expertise. Most teams either accept vendor lock-in or hire expensive kernel engineers. Our goal is to break the CUDA moat. The compute bottleneck is the biggest constraint on AI progress. NVIDIA can't manufacture enough GPUs, and their monopoly keeps prices astronomical. Meanwhile, AMD, Google, and Amazon are shipping capable alternative hardware that nobody uses because the software is too hard. We're breaking that moat. If we succeed, anyone will be able to train state-of-the-art models without thinking past their PyTorch code.

Fixpoint is Uber for human data annotation. We’re building the network of experts and the platform to automate sourcing, vetting, and deploying them on AI training projects. It used to be a manual process to staff people on AI training data or eval projects - but Fixpoint leverages AI to staff people and create data better, cheaper, and faster. This includes a network of pre-vetted experts, AI-automated vetting, and annotator management + payroll.

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. We 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. 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 taps idle GPUs from cloud providers and enterprises, giving startups and researchers cheaper compute while letting companies monetize unused capacity. Fully automated with Kubernetes integration.

DeepAware builds an AI-driven automation system for GPU-intensive data centers. Our reinforcement-learning scheduler, real-time market integration, and unified dashboard slice energy waste by up to 30%. Coming soon: autonomous “robot-hand” inspections and maintenance to enable 24/7 operations with minimal staff. More info: https://deepawareai.com/ Jobs/Internship: https://www.deepawareai.com/careers

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.

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!

We make automation accessible to businesses with an affordable, general-purpose robot platform designed to automate repetitive, stationary tasks.

A fully open-source, performant and actionable Weights & Biases alternative that saves you money. Check out our story here! https://www.reddit.com/r/mlopai/comments/1kkc9jp/mlopai_an_efficient_free_and_opensource/

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.

Artificial Societies uses AI to simulate large groups of people and how they interact. Companies like Anthropic and 11x are using Artificial Societies to predict how their marketing and content will perform in a simulation of their target customers, before launching in the real world. This is a cracked team: James left rural China at age 14, got into Cambridge, turned down a CS PhD to do startups, and - in his spare time - lead the largest ever study of an LLM society. James is joined by Patrick, an applied behavioural scientist who has run over 200 real-world experiments for businesses including Fortune 500 companies. We joined forces through a shared frustration that no one really understands large scale human behaviours: traditional market research is not only slow and expensive, but fundamentally flawed - it misses how people influence each other. Humans are not isolated individuals, we are social animals. We began by simulating a network of 1,000 investors, and used it to raise a pre-seed and get into YC. Since releasing this publicly as “Wave”, startup founders have run over 3000 simulations. We have since released "Reach", where anyone can simulate their own LinkedIn audience and test how their posts will perform before posting for real. Artificial Societies is doing for simulations what ChatGPT did for LLMs - making them accessible to everyone. As AI advances, we will create representative models of entire human societies. Our vision is a world where all content, products, and policies are first simulated in an Artificial Society. We are on a mission to create Artificial Collective Intelligence - humans innovate in collectives, we want to scale collective innovation with ACI.

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.

Karmen is an AI assistant for construction project managers. We integrate with their emails, project management software and ERPs to automate admin tasks like invoice processing and approvals. One construction company we are working with faced a project delay cost of around $50,000 a day from an invoicing mistake. Supplier management issues like these account for 20% of project delays.

We are building state-of-the-art SLAM, perception, and spatial understanding models that leverage vision alone to map, interpret, and interact with the world.

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.

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 web agents that companies use to integrate with legacy portals. Customers use Simplex to dispatch freight shipments, download customers’ invoices, fetch websites’ internal APIs, and more.

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 APIs (https://cloudglue.dev) makes it easy to transform video and audio into structured data for your LLM/RAG/AI application. Our developer-friendly APIs makes it easy to integrate video chat into your AI assistant with minimal heavy-lifting.

MinusX is a chrome extension that adds a side chat to your analytics apps (Jupyter, Metabase, Grafana, Tableau, etc). Given an instruction, our agent operates your apps - by clicking & typing, just like you do - to analyze data and answer queries. We believe an AI Data Scientist is a scientist, not yet-another-new-analytics-platform. MinusX interoperates with you in tools you already love and use, and as a matter of philosophy, gets out of the way.

At Anglera, we're developing a suite of AI agents to help e-commerce companies run their operations more efficiently. Our flagship agent helps our customers onboard, enrich, and manage their product data, reducing time per product from 15 mins down to 5 seconds. We previously developed ML to automatically enrich millions of products at Uber Eats, and we're now on a mission to automate the most common manual workflows for every e-commerce business.

Superunit deploys AI voice and email agents to complete employment verifications for background check companies—faster, cheaper, and more compliant than internal teams or outsourced call centers. Our agents call and email employers, collect verification data, and log every interaction for audit and compliance. Customers are replacing manual workflows with Superunit to scale without adding headcount or compromising quality. We work with several background screening organizations and are processing thousands of verifications per month.

At Undermind, we're building a search engine that can handle extremely complex questions. It’s geared at experts, like research scientists and doctors, who need to find very specific resources to solve high-stakes problems. We’ve rebuilt search from the ground up to address this. Our new approach employs high-quality LLMs to adaptively explore a database, mimicking how a human researcher carefully discovers information. This approach dramatically outperforms (by 10-50x) traditional keyword search and other modern AI-based retrieval methods. Our first target users are the 50 million researchers searching for scientific literature on PubMed and Google Scholar every month. We’ve have paying users across fields like medicine, ML, biotech, finance, and more.

Quantum computers will allow humanity to understand the world at its most fundamental level, enabling the acceleration of drug discovery and the development of new materials. Currently, quantum engineers spend days, sometimes weeks, manually getting their silicon chips to operational conditions to realize two qubits. A qubit is the information-carrying unit of a quantum computer, analogous to a bit in a classical computer. We need billions of qubits to make a useful quantum computer. Therefore, automation software will be vital to realizing this goal. Conductor Quantum will develop AI software to remove the human from the loop, enable the scaling of silicon quantum technology and build a silicon-based quantum computer.

Oway is an AI-driven freight platform that turns America’s empty truck space into a new category of freight shipping. Every year 50% of U.S. truck space goes unused, a $100B+ inefficiency. Oway uses AI to automatically price, match, and sell that space in real time, giving businesses freight shipping that is dramatically faster and cheaper than traditional options. We're building the largest decentralized network of America’s empty truck space - a transparent infrastructure layer any shipper, broker, or carrier can plug into. For shippers, this means freight costs that are up to 50% lower than traditional LTL (shipments from 1 to 12 pallets), along with faster delivery windows because freight moves directly from dock to dock without the slow handoffs, warehouses, or cross-docking infrastructure that LTL depends on. For carriers, Oway’s integration can increase annual revenues by up to 30% with minimal impact on existing schedules. They simply earn more by monetizing space that would have gone unused. Our mission is to transform empty truck space into an asset that strengthens U.S. competitiveness. By lowering the cost of goods sold, we help businesses grow. By improving carrier margins, we make trucking more sustainable. And by digitizing and decentralizing freight, we are laying the foundation for a resilient, transparent logistics layer that can support America’s reindustrialization in the decades ahead. Oway makes freight shipping as simple as ridesharing - click a button, book a truck, and move goods at the true cost of available capacity.

Cartage is the future of freight coordination. Transparent, tech-driven and eliminating the need for human coordinators.

FINNY is building a new operating system for independent financial advisors, and starting by helping them grow. Our goal is to empower the best financial advisors (the independents!) to serve all Americans who want financial advice. We are starting with growth, which is the most existential problem advisors face. The organic growth process is highly inefficient and full of noise. Advisors often waste 60 hours of business development to convert 1 new client. At FINNY, we are certain we can do better with AI. With our tool, advisors can: - Identify prospects within their target niche, aggregating thousands of data points per lead - Prioritize prospects based on their predicted likelihood of converting, a score unique to each advisor and prospect pair - Automate the outreach and meeting scheduling with their high priority prospects In essence, doing all the work for the advisors, and letting them focus on what actually matters, the client relationship.

We're building humanoid robots to do most of what you find boring or tedious. We have an open-source design which we are releasing to the public, which is capable of walking, talking and manipulating objects.

Ryse is the only marketplace where investors who want to buy leases can trade with real estate operators who want to sell leases.

Ellipsis will help your team merge code 13% faster. Ellipsis is an AI developer tool that automatically reviews code and fixes bugs on pull requests. It uses LLM agents to catch logical errors, security issues, and can even enforce a team's style guide. The coolest part is that after Ellipsis identifies an issue, developers can tag @ellipsis-dev to have Ellipsis implement the fix. Internally, Ellipsis actually executes the code it generates, just like a human does. As a result, we allow developers to generate working, tested code directly from GitHub/GitLab.

Ocular AI is the data annotation engine for Generative AI, Computer Vision, and Enterprise AI models. We help you transform unstructured, multi-modal data into golden datasets to power generative AI, frontier models, and computer vision. Ocular Foundry is the most intuitive, data-centric, and fastest platform that lets you label, annotate, version, and deploy your data for training models. It also orchestrates your annotation jobs, improving collaboration with members and annotators. With Ocular Bolt, shift from humans in the loop to experts in the loop to supercharge your data labeling and annotation projects. Our global expert workforce ensures fast, accurate results—no matter the scale or complexity of your data. Companies spend huge amounts on training data, but Foundry and Bolt are AI-native tools that lower costs, reduce manual effort, and accelerate high-quality data collection. We’re replacing outdated, clunky, and expensive data software!

Only 2 out of 10 ML models make it from experiment to production. Preloop helps automate the process of deployment, helping companies realize more value from their machine learning teams, while focusing teams' attention on science instead of engineering.

Safety from humans in the loop is a mirage. We evaluate, research, and apply AI control in our own real-world deployments of autonomous organizations. We are building the Safe Autonomous Organization. We iteratively launch and scale autonomous organizations, while bridging AI control research with real-world testing.

Sourcepulse is the internal status page for software delivery, alerting teams before costly delays. We collect anonymous feedback from project members to identify exactly what to fix, just like how Sentry captures system details for production errors.

Casca is an AI-native platform transforming small business lending by enabling banks and lenders to originate 10x more loans with 90% less manual effort. Our solutions streamline the entire loan process—from customizable application forms and secure applicant portals to AI-powered assistants, automated document collection, KYB checks, and underwriting. We’ve found product market fit and are scaling our team very quickly. Small businesses are the heart of the American economy. Many banks shy away from providing funding because of the manual effort in pursuing those deals. With us, that changes. We unlock affordable, quick bank funding for the 30M+ small businesses in the US who would otherwise be subject to the high interest rates from predatory online lenders. We are a world-class team of banking & AI experts from Stanford, MIT & Y Combinator. We like to win and we know that the only thing between us and the title is our own ability to improve every day.

PropRise is building the AI operating system for commercial real estate investing. We automate the entire workflow from sourcing to closing: our AI finds deals nationwide that match your criteria, then populates your Excel models with extracted data in 90 minutes instead of 4 hours. CRE teams currently waste 80% of their time on manual tasks and miss profitable deals because they can't analyze fast enough. Major firms like Colliers and Public Storage already use us to analyze 7x more deals. We're creating the first end-to-end platform that acts as the system of autonomy, intelligence, and record for the $20 trillion CRE market.

Preventive heart health. Don't die of a heart attack -- use Empirical Health to measure 100+ biomarkers, predict your future risk of heart disease, and create a customized prevention plan with a doctor. We AI to deliver preventive health at scale. We're licensed, registered, and insured to deliver real medical care in 30+ US states covering 200m+ people.