Tldr

AI workers must learn how to use our computers, browsers, and software interfaces to deliver real-world value. Today, computer use agents are unreliable and inaccurate. Halluminate is building realistic sandboxes and datasets to train better computer/browser use AI.

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Looking to chat with researchers or founders training computer/browser use agents!

❌ Problem

OpenAI’s Operator and Claude’s Computer Use give us a glimpse into the future where AI can take control of digital interfaces and do real work.

Performance today is inaccurate/unreliable. There are two bottlenecks to performance improvements.

First, reliance on real-world testing: researchers today train/test their browser- and computer-use agents on real-world sites. This is

• Unsafe - agent actions have real consequences and data impact
• Slow - challenging to parallelize
• Difficult to reproduce - the real world is dynamic; the starting conditions cannot be “reset” easily, and data changes
• Lots of noise - proxy, captcha, auth/login, ads, etc,. make it difficult to do clean testing/training

Second, lack of high-quality data: High-quality data provides the basis for evaluations and benchmarking. Producing this at scale is expensive, time-consuming, and logistically exhausting.

💡 Our Solution

Launch YC - Halluminate

At Halluminate, we’re building a suite of products and services to address both these issues.

• Realistic sandboxes – Fully managed, parallelizable environments modeled after popular systems (e.g., Salesforce, Slack, Ticketing Software, websites) for safe and accurate computer/browser use training and testing.
• Datasets – proprietary benchmarks and datasets
• Evaluations - high-quality error analysis powered by expert annotators to identify and prioritize the biggest failure modes for our customers

Our customers see

• Improved browser- and computer-use agent performance
• New and emergent frontier agent capabilities
• Data-driven prioritization leading to exponentially faster development speed
• Increased revenue/sales via marketing from public benchmarks

🌐 Our Mission

Unlock significant advancements in browser and computer use AI capabilities. We believe this is necessary to usher in a new generation of use cases, startups, and productive AI workers.

📜 Backstory

Wyatt and I met while studying CS at Cornell and have been living and working together for over 7 years.

I previously led product/research at Capital One Labs, where I launched one of the first AI agents in banking. Wyatt previously was a Cornell Milstein scholar and did large-scale data engineering for 2 early-stage startups in NYC.

We faced these problems first-hand while building evals for browser/computer use agent companies. We didn’t see any good solutions, so we’re building one ourselves.

We’re in SF for the foreseeable future. Contact us if you wanna grab a coffee, go for a walk, or play pick-up basketball!

Emails: jerry@halluminate.ai, wyatt@halluminate.ai

🙏 Our Asks

• Follow us on socials (Jerry: LinkedIn, Twitter Wyatt: LinkedIn, Twitter)
• Introductions to researchers and founders building computer/browser use agents
• Introductions to experts in RL & post-training
• Introductions to companies that sell training data to large labs