One central brain for your company
Everything your company knows, in one place your team and your AI can ask. SOPs, contracts, client history, the decisions nobody wrote down. Cited answers, not the one person who remembers.
Learn moreWe map how your business actually runs, build AI into the workflows you already use, and stay on it after it ships. All at startup speed.
*the mess
AI promised to make your operation more scalable. What you've lived is messier than ever.
*one machine
AI doesn't fix a workflow… It amplifies whatever system it lands in.
Drop it into a broken one and you get the same mess, faster.
Most AI firms start from the model. We start from the work: how your people scope it, deliver it, check it, and report on it, and where your margin goes along the way.
*the mess
AI promised to make your operation more scalable. What you've lived is messier than ever.
*one machine
AI doesn't fix a workflow… It amplifies whatever system it lands in.
Drop it into a broken one and you get the same mess, faster.
Most AI firms start from the model. We start from the work: how your people scope it, deliver it, check it, and report on it, and where your margin goes along the way.
Build one workflow in isolation and you create two new problems next to it. We map your whole operation first, so the first thing we build fits the second, and the third. You can start small. It just won't stay a one-off. That's how you go from AI sprawl to AI systems.
A good tool saves hours. A good system moves your margins.
Everything your company knows, in one place your team and your AI can ask. SOPs, contracts, client history, the decisions nobody wrote down. Cited answers, not the one person who remembers.
Learn more→Hours, delivery, and margin by client and engagement, in one view that updates itself. You see which work makes money now, not at quarter close.
Learn more→Scoping, first drafts, QA, reporting. Agents that do the delivery work inside your process, with your people on the hard cases, not a chatbot next to it.
Learn more→Every output checked against your standards before a person sees it. Full population, not a sample. So you know it's right, not just fast.
Learn more→The GPT someone wired up in March that the business now depends on. We audit it, make it real, and put it under maintenance.
Learn more→Live KPIs in one screen. The whole team looking at the same numbers, in real time.
*how it works
We learn how your operation actually runs, then hand you a ranked list: where AI pays, where it doesn't yet, and what to leave human. Yours to keep, whether or not there's a day 31.
You pick the bet. We build it into the systems you already use and ship in increments, so your team is working with it inside the first few weeks. Instrumented against your KPIs from day one.
We host it, monitor it, and evaluate it. When the workflow shifts or the model changes, we make the change. No change order, no new scope.
In 30 days we interview your key people, go through every tool and workaround you run on, and hand you a report your CFO can actually read: what's breaking, what it costs you, what to build in what order, and what to leave human.
book a call*the difference
AI made building cheap. That's why so much of what gets built doesn't matter. The hard part is knowing what to build, what to leave alone, and whether the output is right.
ElsewhereA pod you meet after the pitch
HereThe founders who pitched you, on every call
ElsewhereUse cases, function by function
HereOne system, mapped end to end
ElsewhereBilled by the sprint
HereA fixed assessment, then one monthly license
ElsewhereA handoff when the work ships
HereWe stay on it, and own that it keeps working
ElsewhereFast
HereFast, and checked before anyone relies on it
Founders, not a bench. Alexandra Bonetti and Emily Sanford lead every engagement personally. Between us: operations at scale, venture-backed companies, AI products, and the systems that grade AI's own work.
*the stack
No new platform. No rip and replace. We build into the systems your team already logs into every day — and most of them, we've been inside before.