AI in productionbuilt to survive launch
Applied AI, retrieval, and fine-tunes — with the evals, guardrails, and monitoring that keep it reliable long after launch day.
A senior engineering studio for seed to mid-market teams. We solve hard problems fast and take them all the way to production — AI, ML, agents, and distributed systems are the toolkit, not the pitch.
Five ways we solve hard problems fast. AI is one of them — not the whole story.
Applied AI, retrieval, and fine-tunes — with the evals, guardrails, and monitoring that keep it reliable long after launch day.
Forecasting, ranking, and classification wired into your product — plus the pipelines that keep them accurate.
Workflow agents that take real tasks off your team — tool use, memory, human oversight. Not chat toys.
Distributed systems engineered to stay fast and stay up as your usage grows.
Senior offshore pods that move while you sleep — the seniority you can't hire fast enough, at a number your CFO will like. On your hours when you need it.
Seed to mid-market teams with hard problems and no time to staff up for them. We embed, deliver, and get out of your way. Here's what each seat at the table gets:
A partner who turns your AI roadmap into product your customers use — without hiring a team you can't afford yet.
Senior engineers who write code you'd be glad to inherit. No body shop, no hand-holding, no mess to clean up.
Senior talent at offshore economics — predictable burn, around-the-clock progress, and value you can defend in a board meeting.
Built by engineers who've shipped production systems at companies you know — across tech, retail, finance, and defense. Team experience, never client claims.
An AI Web3 investment firm needed to vet brand-new projects, but ChatGPT was blind past its 2023 cutoff and nothing did real-time deep research programmatically. So we built it: custom scraping and retrieval feeding the model live data, with verification steps to keep results reliable. A project that took days to research now took minutes — the tool stopped being the bottleneck. What was left was judgment: which projects to investigate, and what each was worth spending to research, with cost controls built in. The firm made investment-grade calls on what it produced. When OpenAI later shipped Deep Research it was a slow, generic UI — ~30 minutes a query. Ours was a purpose-built web app with an automated pipeline behind it: faster, and tuned to exactly what the firm needed.
We turn hard problems into working product — from Miami Beach to Bangkok, on your side of the table.
Tell us what's slowing you down. We'll tell you straight if we can help — 30 minutes, no deck, no pitch.