Top 10 AI Startups to Watch in Berkeley, CA in 2026

Table of Contents

• Introduction to Berkeley’s AI Hub
• Letta
• Periodic Labs
• Arize AI
• DeepScribe
• Ambi Robotics
• Hayden AI
• Nova Intelligence
• Biostate AI
• Unsloth
• AO Labs
• Frequently Asked Questions

Letta

Today’s most advanced AI agents are brilliant but forgetful, their reasoning trapped by short context windows. For enterprises needing agents to manage complex, multi-day workflows, this “memory bottleneck” is a critical failure point, blocking the transition from simple chatbots to persistent, learning assistants.

Founded by UC Berkeley Ph.D. Sarah Wooders – a 2026 Forbes 30 Under 30 honoree – and the team behind the seminal MemGPT project, Letta builds a “memory layer” for LLMs. Its virtual memory management system allows AI agents to retrieve and store information indefinitely, granting them long-term, persistent recall. As researchers from the UC Berkeley Sky Computing Lab have highlighted, this is key to creating agents that get smarter with every interaction.

Letta has already attracted enterprise heavyweights like Accenture and Nokia as clients. Positioned at the white-hot center of the agentic AI wave, the company is solving a foundational infrastructure problem. Watch for Letta to become the default memory backbone for enterprise AI agents, making it a prime candidate for a strategic acquisition by a cloud giant or a major IPO as the agentic market matures.

Periodic Labs

Discovering new, high-performance materials – like room-temperature superconductors – has historically been a slow, trial-and-error process reliant on serendipity. This bottleneck in materials science delays breakthroughs across energy, computing, and manufacturing.

With a staggering $300 million seed round backed by Jeff Bezos and Andreessen Horowitz – detailed in a report on elite AI alumni ventures – Periodic Labs is arguably the most well-funded “AI for Science” company. Founded by former OpenAI VP Liam Fedus and Google Brain’s Ekin Dogus Cubuk, it applies frontier LLM reasoning to physical chemistry, using “AI scientists” to model and discover novel materials at an unprecedented pace.

The sheer scale of its funding signals investor belief in a paradigm shift. Periodic Labs exemplifies the East Bay’s strength in marrying deep academic research from institutions like UC Berkeley and Lawrence Berkeley National Laboratory with Silicon Valley’s scaling capital. Its success could redefine entire industries, from energy to semiconductors, making it a future unicorn with the potential to go public or spawn new industrial conglomerates.

Arize AI

As enterprises move AI models from pilot projects to production in 2026, they are hitting a wall of complexity. Models fail silently due to drift, bias, or degraded performance, costing millions and eroding trust – a problem that makes robust monitoring non-negotiable.

Arize AI provides the essential “observability” platform needed to troubleshoot and monitor models in production. Having raised over $61 million, its focus on “embedding-based” observability allows engineers to visualize high-dimensional data to pinpoint issues traditional metrics miss. As cited by industry analysts at Wellows, Arize is a leader in the critical MLOps space.

Positioned in Berkeley, Arize benefits from the local talent pipeline and the intense market demand for AI reliability tools. Its deep traction and specialized focus position it as a foundational, must-have tool for any serious AI engineering team, with a clear path to following the Datadog playbook as an independent, publicly-traded pillar of the AI infrastructure stack.

DeepScribe

Physician burnout is at an all-time high, with doctors spending up to two hours on administrative paperwork for every hour of patient care. This inefficiency cripples healthcare systems and diminishes the quality of care, creating a massive market for automation.

Based in downtown Berkeley, DeepScribe uses proprietary ambient AI to listen to natural patient-doctor conversations and automatically generate structured clinical notes – no wake words or prompts needed. A graduate of the Berkeley SkyDeck accelerator, the company has raised over $50 million and is already used by thousands of clinicians, reducing charting time by up to 80%.

DeepScribe is a prime example of “Vertical AI” – deeply specialized applications that deliver immense, tangible value. As healthcare systems desperately seek efficiency, DeepScribe’s proven traction and strategic Berkeley location, surrounded by top-tier medical and technical talent, make it a highly attractive acquisition target for major electronic health record providers like Epic or Cerner, or a candidate for an IPO as it expands its clinical AI suite.

Ambi Robotics

The explosive growth of e-commerce has left logistics giants scrambling to sort and handle millions of unique, unpredictable items in warehouses – a task too variable for traditional robotic automation and a major bottleneck in supply chain efficiency.

Spun out from the lab of UC Berkeley Professor Ken Goldberg in Emeryville, Ambi Robotics uses AI-powered simulation-to-real (Sim2Real) training. Its “AmbiOS” enables robots to learn and adapt, handling a vast array of shapes and sizes with human-like dexterity. The company, part of the vibrant Berkeley Startup Cluster, has raised $90M+ and holds multi-million dollar contracts with global leaders like Pitney Bowes.

Ambi is at the intersection of two massive trends: AI and physical automation. Its close ties to UC Berkeley’s world-leading robotics research provide a continuous pipeline of innovation. Watch for Ambi to expand from parcel sorting into other complex manipulation tasks, potentially following a path to acquisition by a logistics titan like Amazon or FedEx as demand for flexible automation soars.

Hayden AI

City transit systems are inefficient and underfunded. Bus lanes and stops are routinely blocked, causing delays, frustrating riders, and undermining public trust – a problem that demands a data-driven, scalable solution beyond manual enforcement.

Hayden AI, with offices in Berkeley and Oakland, deploys mobile computer vision systems on public buses to autonomously enforce transit lanes and bus stops. Having raised over $120 million in Series C funding, it’s the only fully operational solution of its kind, with massive deployments in cities like New York. It’s a stellar example of “Smart City AI” born from the local ecosystem, having graduated from the SkyDeck accelerator.

As municipalities worldwide seek automated solutions for urban management, Hayden AI’s proven model is highly scalable. The company is positioned to become the operating system for municipal mobility enforcement, with a clear path to an IPO as it expands its sensor network and data services, turning the passive observation of city streets into actionable intelligence.

Nova Intelligence

Legacy enterprise codebases, often written in outdated languages like COBOL, represent a trillion-dollar “technical debt” trap, locking financial institutions and governments in costly, risky systems that are nearly impossible to modernize with traditional methods.

Founded by UC Berkeley alum Sam Yang, Nova Intelligence uses generative AI specifically trained to analyze, understand, and refactor these massive, undocumented legacy systems into modern code. Unlike general coding assistants, it specializes in the high-stakes migration of core banking and government infrastructure, attacking a massive market that general tools like GitHub Copilot cannot effectively address.

Nova’s deep technical moat comes from its specialized training on archaic code patterns. If it can reliably automate the modernization of these critical systems, its value proposition is enormous. Watch for it to secure major contracts in finance and defense, potentially making it a compelling acquisition target for large systems integrators like Accenture or IBM seeking to dominate the enterprise transformation space.

Biostate AI

Traditional drug discovery is a slow, costly, and failure-prone process, ill-suited for the era of personalized medicine where treatments must adapt to individual biological signatures and dynamic health data.

Emerging from a multidisciplinary team at UC Berkeley and Stanford, Biostate AI builds generative models that treat biological beings as “dynamic systems.” The goal is to use individual patient data to create personalized biological models, radically accelerating target discovery and therapeutic design. The company has grown to over 45 employees and already provides RNAseq services to pharmaceutical partners, demonstrating early commercial traction.

The Bay Area is the epicenter of the AI-biotech convergence. Biostate’s deep research roots within the UC Berkeley ecosystem give it direct access to top computational biology talent and cutting-edge science. Its success hinges on forming powerful partnerships with big pharma; a major drug discovery milestone from its platform could trigger a significant up-round or position it as a strategic buyout target for a pharmaceutical giant seeking next-generation AI capabilities.

Unsloth

Fine-tuning large language models for specific tasks is prohibitively expensive and slow, creating a significant barrier for startups, researchers, and enterprises that lack the computational resources of tech giants.

This Y Combinator (W2026) startup provides blazing-fast, open-source training kernels for LLM fine-tuning, claiming to achieve 30x faster training and 90% less memory usage than standard libraries. As highlighted in a profile of notable AI startups, Unsloth has achieved remarkable organic traction, with over 10 million monthly model downloads and 40K GitHub stars, by solving this universal pain point for developers.

Unsloth embodies the Berkeley ethos of creating elegant, efficient infrastructure. If it can maintain its performance lead, it will become deeply embedded in the global AI development stack as the standard for efficient LLM adaptation. This makes it a classic “picks and shovels” play, likely to be acquired by a major cloud provider like Google or AWS wanting to optimize and lock in their AI developer ecosystems.

AO Labs

While individual AI agents are powerful, orchestrating teams of agents to work together seamlessly on complex, multi-step tasks is a new and unsolved engineering challenge. This coordination layer is what separates simple automated scripts from truly intelligent, autonomous workflows.

AO Labs is building an “AI agent library” focused specifically on this orchestration layer – managing how multiple agents interact with each other, make decisions, and call external APIs. As the market shifts from simple chatbots to “agentic workflows,” this middleware becomes critical infrastructure. The startup is part of the burgeoning Berkeley ecosystem focused on this next frontier of AI.

As an early-stage startup tackling a nascent but essential component of the agent stack, AO Labs represents a high-risk, high-reward bet. Its success depends on developer adoption and the ability to set a de facto standard for how agents collaborate. If the agentic trend explodes as predicted by UC Berkeley experts, AO Labs could evolve from a promising library into a key infrastructure player or an attractive acquisition for a platform company building out its own agent ecosystem.

Frequently Asked Questions