Interruption Economics
I architected, built, and operate my company's internal AI platform — solo, end-to-end. It began as one thesis, and every part of the system is an argument for it.
In any growing company, the real bottleneck isn't a lack of data — it's that the answers live in people's heads, and getting them means interrupting an expert. The data lead pings the engineer. The community manager pings the data lead. Every interruption is a context-switch tax paid twice: once by the asker who waits, once by the expert who stops. The tax scales with headcount, and it never shows up on a balance sheet.
The platform's job is to make that tax optional. Ask it in plain language; it plans, queries live production systems, reads the results, checks its own work, and answers with citations — or tells you honestly when it can't. The interruption doesn't happen. And because one system serves every discipline at once, each new domain it learns raises the value of every other — the return compounds instead of adding up.
How it works
It's an agentic system, not a retrieval template — it reasons about what evidence it needs, gathers it from live systems, and synthesizes an answer that it then verifies before sending.
Built like a product, run like a platform
The safety and cost guarantees are enforced in code and pinned by tests — not merely requested in a prompt. A tripwire fails the build if a customer-facing capability is ever added without review; a model change without its migration fails the build, so production can't silently break. It's exposed as a standard tool-server so other assistants can consult it, and it runs on the same rigor I'd give any shipped product: a full test suite including a safety-focused "trust-spine," continuous integration, and honest failure when a source is down or coverage is thin.
Why it's the work I'm proudest of
Not because of the model — models are a commodity — but because it's a platform designed, built, safety-modeled, cost-governed, and rolled out across an organization by one person, and because it serves the whole company rather than one team: leadership, data, ops, engineering, trust & safety, community, and product. The next frontier is longitudinal memory, proactive "what changed / what's at risk" digests for leadership, and a multi-tenant version — a company's operating memory as a product.
This describes the platform's architecture and thesis at a general level; specifics, internal names, and data are omitted deliberately. Happy to go deeper in conversation.