Capitol AI

A quick look inside the Capitol Composer — watch workflows run, reasoning unfold, and results flow wherever you need them.

Capitol is AI for consulting, and we think it should produce more than answers. It should produce the work behind them: research you can audit, repeat and put in front of a client. Here’s a public example. Five companies will spend the equivalent of 2.5% of US GDP building AI this year, America’s biggest bet since the Louisiana Purchase. So we asked Capitol how America’s big bets have turned out, from the Erie Canal to the Interstates, and published what it found. Some paid off fast. Some took decades. Some overshot. Every number traces to its source. The method is the same for every bet. And it’s designed to be read. That’s what Capitol builds for consulting. See the numbers: https://lnkd.in/eSGwmGf2

We’re proud to be building with EY. Together, we’re encoding the way complex M&A and private equity work actually gets done: the workflows, expertise, standards and decision logic behind it. That means turning deep enterprise knowledge into governed systems that can be used, adapted and improved across real work. This is exactly the kind of problem Capitol AI was built for, and we’re excited about where it’s going. More from our work with EY soon. https://lnkd.in/dz7S2PVH

Your AI strategy is a very long prompt. We should probably fix that. Capitol AI encodes your company’s knowledge, rules, and systems into workflows teams can design, test, and reuse. How much of your company are you explaining every time?

Buying access to a frontier model does not automatically make your organization smarter. It is table stakes, but something else must be present. The missing element is STRUCTURE. Think about how AI usually works at an institution. I.E one SME with a lot of experience can produce an impressive artifact in an afternoon. But their prompt sits in their chat history, so another team can't easily take advantage of it. Also, you don’t always know if the numbers it produced are accurate and defensible. You rented intelligence and got a few wins but it does not make a company-wide impact. Why? Intelligence inside a regulated institution moves through people who have to trust it, data that has to stay inside the perimeter, and deliverables that someone has to sign off on. Here is how to think about the structure that will compound the value of agentic AI. You need: → Governed workflows that run the same way every time → Claim-level traceability, so any number traces back to its source → Human approval at exactly the point where you need it → Model independence, so you use the right model for the job Don’t be misled into thinking that renting the model will solve all your problems. Own the structure and THEN unleash the power of AI. Full breakdown in our new article linked in the comments 👇

Most enterprises adopting AI right now are making an implicit trade. They employ capable AI agents but have to hand their sensitive data to a model running on external infrastructure and hope their data is safe. For a regulated institution this is not a viable long term option. Leading banks and agencies are now investing in "Sovereign AI". → Your data: on a single-tenant, inside your perimeter → Your models: so you can swap providers / models without re-building → Your process: fully governed, auditable, reproducible What does sovereign AI require and should you look into it for your organization? Full breakdown in the comments. 👇