An independent studio designing and building the AI systems that make it all the way into production — past the demo, through the edge cases, into the workflow.
Pragmatic AI work, end to end. Pick a lane or combine them — most engagements touch two or three.
Automating the busywork: workflows, back-office operations, and internal tooling that pays for itself in weeks, not quarters.
Purpose-built agents that plug into your stack and actually do the work — not just chat about it.
Helping leaders figure out where AI belongs in their business — and, just as importantly, where it doesn't.
Wiring AI into what you already run: CRMs, data warehouses, product surfaces, internal tools. Less rip-and-replace, more quiet upgrade.
A demo is not a system. We ship systems — the ones that survive contact with real users, real data, and real edge cases.
The AI space is loud. Most of what you see is a demo that works on a carefully chosen example and falls apart in production.
We're interested in the other ninety-five percent — the messy edge cases, the data that's wrong, the integrations that break, the users who don't read instructions.
That's where real value gets built. It's also where most teams give up.
Most "AI problems" are process problems in disguise. We'd rather lose the engagement than build you something that shouldn't exist — and we have, more than once.
The last 13% of any workflow — the ambiguous, the high-stakes, the weird — is where your team earns its salary. We don't automate that away; we design around it.
Code, weights, prompts, evals, infra — in your repo, in your cloud, in your name. We leave the keys. There is no second invoice.
Short, dated notes from projects. Not case studies — more like margin writing. A few things we've shipped, argued about, or talked a client out of.
Reads four upstream systems — contracts, invoices, Stripe, a warehouse CSV — and reconciles them. Handles 87% of tickets unattended; we kept the remaining 13% human on purpose. The failure mode we were designing around: issuing a refund we shouldn't have. That one gets a second pair of eyes.
The real problem was four internal reports nobody was reading. We fixed that instead — a plain Slack digest, no model required. Engagement ended a month early and under budget. They called us back for the next thing a fortnight later.
If you can't describe what "good" looks like in fifteen test cases, you aren't ready to build yet — and no amount of prompt engineering will save you. Sometimes the most useful thing we can do in week one is say so, and spend week two writing the evals.
A short note is enough. We reply within a day — usually the same afternoon.
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