No open-ended retainers that drift. A clear sequence with checkpoints, defined deliverables, and a bias toward shipping.
We map your data, workflows, and commercial goals before writing a line of code.
The technical blueprint that makes the system survivable in production.
Out of the sandbox and into your real environment.
Start with an assessment, commit to a sprint when the path is clear, and keep us on retainer when AI becomes core to how you operate.
Know exactly what to build — and what not to — before you spend.
Put one real system into production, end to end.
A fractional AI architect embedded in your team.
No. Messy, scattered data is the normal starting point — structuring and securing it is part of what we do in the architecture stage. The assessment tells you exactly how much work that is before you commit.
Yes. We often act as the AI architect layer on top of an in-house team — designing the system, setting the guardrails, and handing off clean, documented work your engineers can own.
Most agencies hand off the moment a demo works. We own the build through production and stay accountable for whether it actually runs — combining operational experience with hands-on engineering rather than one or the other.
Then we tell you, and you've saved a much larger spend. Part of the value is defining what not to build. We'd rather lose a sprint than ship you something that fails in production.
No. We work with established SMBs automating operations just as much as deep-tech startups productising a POC. The discipline is the same; the starting point differs.
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