We Built Something That Didn’t Exist. Today, We’re Sharing It With the World.

I’ve spent my career building AI systems that actually work: Vertex AI at Google Cloud, anti-money laundering systems for the financial industry, infrastructure that quietly powers decisions affecting billions of people. I’ve seen up close what serious AI looks like when it’s done right, and I’ve seen the enormous gap between what enterprise leaders need AI to do and what the technology has actually been capable of delivering.

That gap kept me up at night. So two years ago, I founded Lovelace to do something about it.

Today, Lovelace is coming out of stealth. We’ve spent two years heads-down building something we believed had to exist. This is the story of what we made, why we made it, and why I believe it changes something fundamental about what enterprise AI can accomplish.

The Real Problem Nobody Was Solving

There’s a quiet frustration in the C-suites of the world’s largest banks, defense agencies, and logistics companies. Leaders keep hearing that AI will transform their organizations. They invest. They pilot. They get tools that summarize documents, answer chatbot queries, and deliver 5–10% productivity improvements. And then they’re left wondering: is this it?

It isn’t. But the problem isn’t the AI models. The problem is what those models are being asked to work with.

AI doesn’t work properly when it needs to do serious investigations that pull together thousands of clues. Today’s enterprise AI agents are being pointed directly at raw, fragmented, siloed data: oceans of documents, databases, feeds, and filings that were never designed to talk to each other. When you ask an agent to reason across all of that, you get incomplete answers, hallucinated connections, and costs that scale ruinously with the volume of data you’re trying to interrogate. For industries where a wrong answer doesn’t just cause an inconvenience but can put billions of dollars, lives, or national security at risk, that’s simply not acceptable.

The industry has a term for what agents need but don’t have: context. Specifically, a structured, trusted, verifiable layer of context that sits between the chaos of raw enterprise data and the intelligence of a large language model. Building that layer at global scale, with the accuracy required for mission-critical decisions, is one of the hardest engineering problems in AI today.

It’s the problem we set out to solve.

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