Founded by Richard Ewing

Richard Ewing is an independent AI Economist who audits engineering spend and exposes the capital risks that metrics don't show. Published in Foundry and Built In, Richard has identified millions in misallocated R&D spend across Series C platforms and B2B SaaS companies.

I'm a product guy, not a machine learning engineer. I don't have a Stanford AI lab pedigree, and I didn't set out to build deep AI infrastructure. I built Exogram because I was trying to actually use AI to build real things, and the systems kept driving me absolutely crazy.

At first, the experience felt like magic. These frontier models are absolute miracles of cognition. They could scaffold projects and reason through complex logic at insane speeds. But the minute I tried to step back and give these agents real autonomy in my workflows, they lost their minds. They would forget context, contradict themselves, recreate bugs they had just fixed, and hallucinate operational decisions.

Exogram is my solution to that nightmare. It sits right between the AI's brain and the actual execution controls. Exogram operates across four distinct layers: persistent context, dynamic governance, state mutation control, and an auditable ledger. Instead of asking if the AI is smart enough to do a task, Exogram acts as a strict, deterministic bouncer that asks if the AI should be allowed to touch the controls at all.

Everyone is racing to make AI smarter, faster, and more autonomous. The engineers are building brilliant brains, but almost nobody is building the safety net for when these systems actually start running our lives.

What the industry currently calls "memory" is basically just chat history. That is fundamentally inadequate. Autonomous execution requires an auditable ledger. Right now, the industry's idea of a guardrail is just using one unpredictable AI to babysit another unpredictable AI. That works fine if you are building a customer service chatbot. It is a total disaster if that AI is running enterprise software, financial systems, or real-world infrastructure.

The biggest problem we face is that we are giving AI the keys to the car without building the brakes. We need a definitive, verifiable way to enforce operational boundaries before these systems cause real damage.

I am genuinely terrified that we are going to lose a shared sense of reality. AI is making it entirely too easy to generate infinite amounts of persuasive, synthetic garbage. If we do not build systems to verify what is real, what is a hallucination, and what is actually allowed to execute, the internet just becomes a massive noise machine. When that bleeds over into how physical infrastructure and human institutions operate, things get very dangerous very quickly.

When the early internet started handling real money and sensitive data, we had to invent new security protocols to make it safe for the real world. AI is at that exact same tipping point right now. I want to make autonomous intelligence persistent and verifiable so we can drive this technology as fast as possible without dying in the process.