AI (Artificial Intelligence) Startups funded by Y Combinator (YC) in the San Francisco Bay Area 2026

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.

Checkr builds people infrastructure for the future of work. And we believe everyone should have a fair chance to work. That's why we've designed a faster—and fairer—way to screen job seekers. For more information on our mission and products, visit https://checkr.com.

Mirrors provides regression testing for AI agents. Teams shipping agents constantly change prompts, models, and tools, and there's no way to test any of it. Agents call bespoke internal tools and APIs no test framework can reproduce, so most teams ship on vibes. Mirrors rebuilds the tools your agent calls from your own production traces, then replays real sessions against every change before it merges. You see which tool calls changed, which arguments moved, and where state landed differently. And when something fails, we tell you whether your agent regressed or something upstream moved. Built for teams whose agents take irreversible actions. Refunds, record updates, outbound email. Runs in your CI on every PR.

Shepherd Robotics builds general purpose robots for skilled physical work across AI infrastructure and advanced manufacturing. Our robots are powered by robot foundation models and use modular, flexible hardware designed to adapt to different environments and tasks. We train our systems on a combination of real world and simulation data so they can learn complex manipulation skills and generalize across workflows. We are starting with tasks such as data center construction and operations, cable and wire manufacturing, wire harness assembly, kitting, and factory packaging, where traditional automation struggles with variability, dexterity, and changing environments.

Sona8 is a voice agent that talks to every employee in a company about how their work gets done. Each employee gets a link and talks to the agent for about 20 minutes. The agent asks follow-up questions, for example why a step is still done by hand or who else is involved. From the conversations we build process maps and a list of the problems people described, sorted by site and team. That is the start. We then stay on through the implementation. After each initiative is rolled out, the agent goes back to the employees involved and asks whether it was implemented and what changed in their daily work. The transformation team sees this after every rollout step. The result is a project management office that has talked to every employee. That context did not exist before, because nobody had the time to collect it. Consulting firms use it to gather ideas at the start of an engagement instead. Transformation and corporate development teams use it to guide the execution of strategic transformation.

ByteAsk is an AI coding agent built for C and C++, the code that runs trading systems, cars, chips, and operating systems. It does not stop at a diff that looks right. ByteAsk builds, debugs, and tests every change with the real toolchain until the change holds. If the compiler, the debugger, or the tests can catch it, then ByteAsk catches it too. It runs from the terminal or inside VS Code, Neovim, and Emacs, and is available fully on-prem.

Build agents that research accounts, find prospects, run campaigns, update your CRM, qualify inbound, and more. Connect them to the systems you already use and let them work alongside your GTM team. Engineer your revenue factory on Frontrunner.

We design and collect audio datasets for training speech models.

Every instance of intelligence occurring in nature is embodied. Scientific intelligence is no different. We are building general-purpose robotic experimentalists: systems that unite scientific reasoning and physical action to iterate toward discovery by observing, hypothesizing and experimenting.

Dreamscale Labs is building the fastest cloud inference platform for robotics. As complex physical AI models become difficult to fit on expensive on-robot GPUs, we let users offload inference to the cloud and serve them with real-time latency. So far, we've hit 40-50% lower latency than existing neoclouds.

Our customers use Hopper today for two things: replacing GPT-4.1 with Gemma-4 (6x faster), and replacing Elevenlabs with qwen3-tts (8x cheaper).

OnePatch is a forward-deployed AI SRE. It instruments your services, watches how they actually behave in prod, catches incidents, and drives each one to resolution. Think Datadog, except it doesn't stop at telling you something broke: it keeps going until the fix is shipped.

Intelligent execution layer for AI workloads

Workers IO verifies your entire system across millions of interleaving, faults, and timings to find the exact conditions that break the software properties you care about. Built for teams where 99.99% uptime is table-stakes.

Nobody's built truly proactive AI, because it requires running inference on your entire life. In the cloud, that's insanely expensive and a privacy nightmare. But not on your own chip. Every night, Sentient's on-device LLM wakes your Mac and understands what's new in your life (email, messages, files, Granola transcripts...), entirely locally, and distills it into a knowledge base. You wake up to your work already prepared: the reply you forgot, drafted from your own context; the subscription you never use renewing tomorrow, caught; the report you promised in the group chat, ready to send. Everything is one click from firing through computer use, and nothing fires until you click. Or click your Mac notch anytime and say "finish this for me." Sidekick takes over the thing you were doing with its own cursor while you move on. On-device inference at this scale was supposed to be impossible: devices too slow, and models too dumb. So we built the stack ourselves: a custom LiteRT-LM fork (KV-cache reuse, speculative decoding, custom k-quants) that runs nightly on 8 GB Macs. The user's hardware does ~90% of the compute, and their own ChatGPT or Claude subscription does the rest, so our marginal cost is ~$0, and our servers store nothing. We soft-launched late July with one Reddit post: top of r/macapps, 2,000+ users in 48 hours, $0 marketing. Free and open source for consumers; the same engine on the work stack (Slack, Granola, Linear, Notion) becomes the enterprise business. Sentient OS raised ~$1M in pre-seed, backed by YC, Afore, and the a16z Speedrun scout fund.

Studio is a simulation lab studying how humans and societies respond to new ideas, and is building the first software models that can accurately predict that response. Instead of creating digital twins of survey respondents, Studio builds live market models of interconnected audience segments from real, traceable consumer-behavior data. Our simulations match live KPI outcomes like revenue and purchase behavior with over 97% accuracy for consumer-facing enterprises, higher than any synthetic panel, simulation lab, or traditional market research.

OpenTag is an AI coworker in your Slack with full context on your company. It answers questions and takes real work off your plate. It also keeps your wiki up to date automatically, so knowledge never goes stale.

Lantern learns what exceptional looks like for your team, pressure-tests the role against the real talent market, then runs the hiring loop: sourcing, inbound applications, interviews, assessments, candidate communication, and offers.

Callbook AI is a collections agency operated by AI. We collect late stage portfolios by finding, contacting and negotiating with borrowers.

Marker is a platform for rebuilding enterprises agent-first, delivered by forward-deployed engineers. It productizes every step from discovery to production, so every agent we ship makes the next one faster.

antimattr builds a voice-first hardware interface for human-agent interaction that includes a ring and a pair of earphones. Designed to make more of your voice, the ring can record and summarise your conversations, capture thoughts on the go, or control your agents. The earphones use proprietary voice isolation technology to block out background noise entirely, so you can take a clear call from a loud pub or dictate with real accuracy. Reserve early access at https://www.antimattr.one/project-mnemosyne [use code YC at checkout]

Touchy is your personal AI assistant that lives on your iPhone. One button press and you're talking to an assistant that sees what you see, remembers what you tell it, and takes action across 1,000+ apps--so you can get back to living in the moment.

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.

Palisade spins up a personal sales agent for every single visitor to your website. Marketplaces like trading card platforms, vintage stores, and short-term rental operators sell more when each customer works with a personal agent that remembers them.

We enable companies monetize their agents' traces.

Mochi is an app to watch and create anime microdramas: fast paced shows with a cliffhanger every minute. We produce originals with AI, collaborating with writers from Marvel, Cartoon Network, and Toei Animation, and our users can also create in app too - with over 500 episodes created each day from 3,000+ creators.

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.

Our first model, Atom 1, pre-trained on the internets video, and fine tuned for physics. It has been action conditioned, so you can simulate robot actions within it. This lets Robot Labs train their robots using GPUs instead of relying on human collected manual data that is slow and expensive. In unseen and hard to replicate environments, this is the only path to getting ANY training data.

Tenor enables domain experts to build, run, and scale AI-native service businesses. The next generation of successful white-collar service businesses will be built and scaled around AI from the ground up. These companies need more than capable AI models. They need a company data layer, governed workflows that AI can execute, and harnesses that continuously improve based on outcomes and decision checkpoints. Tenor is building this infrastructure so millions of AI-native businesses don’t have to build it themselves.

We build simulations of your real users, grounded in your session data, so you can predict how any product change will perform before you ship it.

Engineering is bottlenecked by slow, costly simulations. We're fixing that by building foundation models that learn physics end-to-end and deliver the same results in seconds, at a fraction of the cost.

Enact creates the physical environments to benchmark and hill climb robotics models on high-value tasks. We find where robots fail, and fix them with our in-house training loop.

Mosaic gives teams and their coding agents shared memory across every session, so everyone can understand what happened, why decisions were made, and pick up where someone else left off. We started as a real-time coding environment where multiple people and their agents can work together live. It went viral, defined the multiplayer AI category, and inspired frontier labs like Anthropic. Together, we’re building the infrastructure for people and agents to share context and work together.

Erinys provides the technology, intake, back office, and client acquisition needed to launch and scale an AI-native law firm. Lawyers own their firms and make every legal decision; Erinys handles everything around them.

Marengo is an AI-native engineering firm designing data centers in half the time and half the cost. We have $4M in design partnerships with two of the world's largest developers ($XB). Marengo is backed by YC, Exor, Future Planet Capital, undisclosed $2B AUM family office, and other top-tier investors. Our team includes the former President & CEO of Saipem America ($3B+ P&L), former President of Exyte's Advanced Technology Facilities GBU ($6B+ P&L) and more.

Prodigy is the frontier AI trading research lab. We've trained the world's best foundation model for quantitative finance. We've already achieved more than 100% returns in live trading over the course of our YC batch, doubling our capital while major indices were flat. Our founders previously worked on a trading desk at Jane Street, trained frontier AI models at Google DeepMind and shipped cutting-edge AI at Apple to billions of customers. Join us as we use AI to solve the market.

VIM connects to the entire cell: PLCs, robots, CNC machines, sensors. When a cell faults, VIM finds the cause, stages the fix, and the operator approves with one click. When a part drifts, VIM catches it seconds after the cut, inside the machine, and recommends the correction. Recovery drops from 30 minutes to under two, saving more than $15,000 per fault in large factories.

Hebbian Robotics builds APIs for robotics data teams to verify data quality for model training. Our customers use us to build quality control pipelines in just a few lines of code, with custom models 10x faster and 8x cheaper than general foundation models. https://hebbianrobotics.com/ https://github.com/Hebbian-Robotics/hflow

Qlo builds AI agents to run commercial insurance underwriting. We start by fully automating the operational work around risk decisions, become the system of record, and earn autonomy workflow by workflow toward the autonomous AI underwriter. We’re rebuilding underwriting infrastructure from the ground up so human underwriters and AI agents can share the same accounts, rules, documents, permissions, and workflow state.  We believe every commercial insurance program will eventually be built and operated end to end by AI agents on Qlo.

We are building robots that can work autonomously in existing wet labs. Unlike traditional lab automation, which requires dedicated equipment and fixed workflows, our robot moves between workstations, manipulates existing scientific equipment, transports lab materials, and executes experiments using the same lab infrastructure as a human technician. We make the robots easy to manage through natural interfaces like Slack, email, and phone. Our goal is to make deploying robots in wet labs as easy as onboarding a new employee, and to accelerate scientific research!

An agent is LLM + harness: We work purely on that second component. We do research and engineering to produce the most capable and efficient harness. Our harness has 20k stars on , and has $1m/month of inference spend

An AI platform that connects commercial debt advisors with mandate matching private credit funds.

Click builds useful services that meet users right inside ChatGPT and Claude. We're starting with a research service that provides external context that the built-in web search misses - like data from professional & social platforms, marketplaces, financials, and more. It makes research on people & companies even deeper with specialized, source-backed context beyond built-in web search, while enabling new tasks like comparing live flight fares right inside the chat.

Agent FM is a desktop app that gives engineers one group chat to hear and steer their coding agents. Instead of manually bouncing between different claude code and codex sessions, and combing through walls of text to monitor agents, engineers just tune in to Agent FM. Agents share important progress updates, blockers, assumptions, and decisions in the group chat. Engineers get live text and audio updates and respond right from the chat.

Intelligence will be the most valuable asset a company owns. Today, almost every company rents it. Companies pay a handful of labs one token at a time. Each dollar buys an answer, not an asset. They spend more, but own nothing more. Riften reverses that. Change one environment variable and Riften becomes the gateway for a company's AI traffic. We route each request to the lowest-cost model that can do the job, serving most work on open-weight models we host. Customers save money immediately without rebuilding their products. The gateway is the entry point. Over time, Riften turns the company's work into private models built on open weights and shaped around the business. What begins as cheaper inference becomes an intelligence stack the company owns, runs on infrastructure it controls, and uses to power its most important applications. We call this earned intelligence. It is created by the company's own work, controlled by the company, and accumulated over time. The next generation of companies will not rent their intelligence. They will earn it, own it, and compound it. Riften is how they get there.

Models are trained on the entire internet, but most of what happens on Earth is never written down. We turn those signals into training data so labs can make models better at science

Omanta is a personalized research lab for patients who have exhausted standard options, want to prepare proactively for recurrence, or are living with an unresolved diagnosis. We direct the full operational infrastructure, personnel, expertise, and technology of a large research program toward understanding and addressing the challenges of individual patients across oncology, rare disease, and undiagnosed conditions. For clinicians, we act as a collaborative partner, providing a living roadmap for ongoing patient care built on a rigorous and evolving scientific evidence base. We fully operationalize patient-specific research programs, covering tissue logistics, diagnostic follow-up, data integration, multiomic analysis, therapeutic strategy development, and more. Omanta brings context and clarity to complex cases, helping clinicians see more of the scientific picture as they decide how best to care for their patients.

We builds foundational AI models for chemistry. Almost nothing you buy is a single chemical. Products like datacenter coolant, GPU thermal paste, paint, and glue are formulations, which are mixtures of around ten ingredients each, and demand for them has grown quickly because of AI and EVs. Designing a new formulation is essentially a search problem, because there are more possible mixtures than anyone could ever test, and none of them can be predicted in advance. As a result, bringing a single formulation to market today takes three to five years of trial and error in a lab. Our models predict how a mixture will behave far faster than the standard methods used today, which lets us design new formulations in weeks rather than years, at a fraction of the cost. To prove this, we used them to invent three chemicals that never existed before, including a PFAS-free coolant for datacenters, and we have since synthesized and patented all three. We are already working with some of the largest manufacturers to design formulations for them, and we are building our own lab to generate the data that will train our next models.