Senior AI engineers, working inside your team
Most teams arrive here at the same point. A pilot worked, everyone agreed it should ship, and then it became clear nobody in-house had the time or the specific experience to productionise it.
How it works
Embedded means embedded
Our engineers work in your repository, your ticketing system and your review process. They join your standups. Code goes through your review, not ours. After a few months the work should be indistinguishable from work your own team did, because in every practical sense it was.
That is a different thing from an outsourced project delivered over the wall. It is slower to produce a finished artefact and considerably better at leaving capability behind.
One signal raised against the baseline
The point
Teaching is part of the job
We pair, we write things down, and we explain the reasoning rather than just the change. If your engineers cannot run and debug the system after we leave, the engagement failed whatever shipped.
Choosing between models
Embedded is not always the right answer
If you need one system built and have no intention of maintaining AI capability in-house, a fixed-scope build is cheaper and cleaner. Embedded earns its cost when you are building a practice.
- Discovery sprint2 to 4 weeksYou do not yet know whether to build
- Build partnership3 to 9 monthsOne defined system, delivered to production
- Embedded teamOngoingYou are building durable in-house capability
Questions
Frequently asked
- What is the minimum engagement?
- Embedded work is ongoing by nature and needs a few months to be worth anyone's time. For anything shorter, a discovery sprint or a fixed-scope build fits better.
- Do your engineers work in our timezone?
- We are in Mumbai and overlap comfortably with Europe, the Middle East and Asia. Overlap with the Americas is partial, so we agree working hours before starting rather than discovering the gap later.
- Can we hire the engineers permanently?
- Talk to us about it. We would rather have that conversation openly, and building your in-house capability is the stated point of the model.
Also from us
- AI consulting servicesDeciding whether to build, what it will cost and what your data can actually support.
- AI integration servicesConnecting a model to your existing stack without breaking your permission model.
- AI agent developmentTool-using agents with real boundaries, human approval where it matters and a full trace.
Need AI engineers who will still be useful in month six?
Tell us what your team is building and where the gap is. We will be straight about whether embedded is the right shape for it.