Engineers who have already shipped it.
Recruited from the IITs and NITs, experienced in production AI, and embedded inside your team — your repository, your standup, your definition of done.
Not a bench, and not a CV database. People with a running system behind their name.
The skill you are missing, the length you need it for, and whether the work sits inside a perimeter.
Three ways this normally goes wrong
You have been burned before.
Augmentation fails in three predictable ways, and every one of them lands on the person who signed for it. Here is what we do about each, stated plainly enough that you can hold us to it.
The profile you approved is not who joins.
Every engineer is our permanent staff, not a subcontractor sourced against your requirement. You interview before you accept, and there is no substitution after sign-off.
The ramp eats the engagement.
They arrive experienced. The ramp is your codebase and your domain — not the discipline of running AI in production, which they brought with them.
They built AI but have never operated it.
The engineers we place have carried production systems: the pager, the regression gate, the release that did not go. Notebooks are not the reference.
Hiring for your role
When the role needs someone new, you see every round.
Most of the engineers we place have production deployments behind them already — for enterprises and for startups — and go straight into your team. When the role needs someone we do not have yet, we hire for it, and we run three rounds.
- 01Screening
Communication, and what they want next
Whether they can explain their own work to someone who was not in the room, and whether this role is actually the job they want. An engineer who wanted something else leaves in month four, however well the technical round went.
- 02Technical interview
Depth in the work the role really involves
Not puzzles. What they have built, where it broke, and the calls they made — pressed hard enough that a memorised answer comes apart and a real one does not.
- 03Working session
A real task, with AI coding tools allowed
They build something close to the work you need, using the tools they would use on the job — AI assistants included. Testing an engineer without the tools they will actually have tests the wrong thing; how someone directs a model is now part of the skill.
Then we hand you all of it.
The scorecard from every round, the notes behind each one, and what they actually produced in the working session. Not a summary and not a shortlist — the same material we used to decide, so you can reach your own conclusion rather than take ours. Whatever we concluded, the decision that counts is yours.
Where they are strongest
Six disciplines, each with a system behind it.
The platforms named against each one are ours — built, deployed and operated in-house — so the reference for the skill is a running system rather than a line on a profile.
Hybrid retrieval and cross-source correlation over material that is neither tidy nor allowed to leave the building.
Exercised on Intfuzon and NOSTRA
Agents that act in live systems, with the tool boundary, the retry path and the cost ceiling designed before the happy path.
Exercised on Intelligent Document Processing and voice agents in production
Training, serving, monitoring and the retraining loop, including where the estate has no route out to a hosted API.
Exercised on Bhaasha and PlugSafe
Vision, document and speech pipelines running against real-world input quality rather than a curated evaluation set.
Exercised on PlugSafe and Amara
Golden sets, regression harnesses, confidence scoring and the inference trail a reviewer will want years after the decision.
Exercised on Jatayu and Intfuzon
Shipping inside perimeters with no external calls, no telemetry and no remote session to fall back on when something breaks.
Exercised on HoloMap, NOSTRA and Bhaasha
How the engagement is shaped
Three shapes, and how each ends.
You are choosing how much of the problem you keep. All three end — that is the design, not a concession — and the ending is written down before the start.
One engineer against a named gap
- What you direct
- Your backlog, your priorities, your review process. They take work from your board like anyone else on the team.
- What we carry
- Employment, statutory compliance, background verification, and a technical line back into our own platform teams when they hit something unfamiliar.
- How it ends
- They finish the gap and leave. What they built is in your repository with your team's names on the reviews.
A pod around a launch
- What you direct
- The outcome and the date. Sequencing inside the pod is theirs to argue about, not yours to run.
- What we carry
- The pod's internal coordination and the standards it holds itself to — evaluation, observability and the handover artefacts, built as the work happens rather than at the end.
- How it ends
- The pod winds down after the launch stabilises. Your engineers keep the system, because they were in the reviews the whole way through.
A cleared team inside a perimeter
- What you direct
- Access, the estate's rules, and what may be discussed outside it.
- What we carry
- Clearance and vetting, on-site working discipline, and engineers who have already shipped into estates with no route out — so the perimeter is routine rather than the first surprise.
- How it ends
- Offboarding runs against your access list: named individuals, accounts and devices closed off on a record you keep.
The operational facts
What procurement will ask before they let you proceed.
- Payroll, statutory compliance and employer obligations sit with us. Every engineer is our permanent employee, not a subcontractor found against your requirement.
- Background verification completes before day one, not during the first sprint.
- Named individuals go on your access list. Nobody is rotated in behind the badge without your sign-off.
- Offboarding is auditable — accounts, access and devices closed against a list you hold, not a promise you receive.
Rates, notice, minimums and conversion are a conversation with a scope in front of both of us, not a number on a landing page. We would rather say nothing here than publish something you would later find was not true of your engagement.
When this is the wrong call
Two cases where you should not hire people from us.
You want someone accountable for the outcome, not a person you direct.
Then this is the wrong shape. Augmentation puts capacity under your management; if the result is what you want to buy, buy the result.
AI for enterprisesThe priority order is not settled yet.
Adding engineers to an unsettled backlog produces more argument, faster. Settle what is worth building first, then decide who builds it.
AI consultingTell us the gap on your team.
The skill you are short of, how long you need it for, and whether the work sits inside a perimeter. That is enough for us to say who we would put on it — and to say when we would not.
AI talent — common questions
No. Every engineer we place is our own permanent staff, experienced in production AI and recruited from the IITs and NITs. We also run an in-house AI training academy, so the bench is deepened deliberately rather than assembled in a hurry against your requirement.
You interview before you accept, and there is no substitution after sign-off. The engineer named in the profile is the engineer who appears on your access list, and offboarding runs against that same list.
Inside it. Your repository, your standup, your review process and your definition of done — not a separate backlog behind a handover document. The intent is that your team can carry the work afterwards, so knowledge transfer is continuous rather than a document at the end.
Yes. A significant share of our own delivery is air-gapped for defence and public-sector customers, so this is routine rather than exceptional. Background checks and NDAs are standard, and clearance is arranged where the estate requires it.
Anything from a quarter around a specific launch to a multi-year embedded team. Ramping down is treated as a normal part of the arrangement rather than a renewal problem — the point is to add capacity when it is needed, not to become permanent by default.