Bolting an assistant alongside a product gives you two products. We build the feature into the one you have: in your repository, behind your feature flags, through your release process, with the streaming and failure states that decide whether anyone trusts it. Your team keeps ownership, and there is no separate thing to maintain.
01 / IN_PRODUCT
For teams who have a product and want AI inside it. The feature and the surface around it, built in your codebase and your release process.
Usually shipped with
Not a bundle to buy. Whichever you start from, the engagement covers what the build actually needs.
02 / Scope
03 / Outcomes
04 / Deliverables
05 / Stack
06 / Why us
The alternative is a second product to maintain and a handover that never quite happens. Code lands in your repo, behind your flags, through your release process.
On AnyTeam, the real-time surface had to degrade cleanly mid-meeting. Interruption and partial-result handling was the difference between a rep trusting it and closing it.
We write down why, not only what. A team that inherits reasoning can change the feature; a team that inherits code can only maintain it.
Week 1
We work in your repository, your conventions and your review process. The first week is spent understanding what exists rather than proposing a parallel stack.
Week 1-3
One capability, behind a flag, in front of real users. A feature nobody has used is a feature nobody has validated.
Week 2-4
Waiting, partial results, interruption and outright failure. These decide whether people trust the feature far more than the model does.
Week 4-8
Your team owns it. We write down the reasoning, not just the code, so the next change does not need us.
SalesIn production
A real-time meeting-intelligence stack for account executives. It detects a live meeting, transcribes it on-device, guides the rep during the call, and writes a structured, CRM-ready summary the moment it ends, with no audio ever leaving the laptop.
Fund operationsIn production
An institutional fund and portfolio operations platform. It ingests statements and brokerage data from every custodian, reconciles them automatically, and presents one operational view with the audit and compliance trail regulated finance depends on built in.
Sales coaching
A voice-first sales coaching simulator. A real-time voice agent runs lifelike roleplay calls, throws objections and counter-questions, and an analysis stack turns each session into structured, rubric-scored feedback, on the same runtime we reuse across sales, insurance, and real estate.
Production-proven
Built by engineers who've already shipped this in production.
Yes. That is the normal shape of this work: our engineers in your repositories, taking direction from your leads like any other member of the team.
Then you take it over. The handover documentation is written as we go rather than at the end, precisely so this is possible at any point.
Both, which is the point. The split between the two is where most AI features are lost, and it disappears when one team owns the whole path.
Yes. We build to your components and your tokens; nothing here requires a visual rewrite.
The product surface around your model, for teams who have the AI but not the app.
View serviceMulti-step agents for workflows where a wrong action is expensive to undo.
View serviceEvals, routing, and audit trails for teams whose demo cannot survive real traffic.
View serviceTell us where you are and what you're trying to ship. We'll come back with a concrete plan, the right engineers, and a path to production, not a generic pitch.