Questions we hear across the site.

You describe the problem and we ask about it for an hour. Not a qualification script — we are trying to establish what you are trying to move, what you have already tried, where you sit on your own roadmap, and where the process around the work is leaking. It helps enormously if someone who does the work day to day is on the call alongside whoever owns the budget. You do not need to prepare anything, and there is no deck from our side.

No, and this is the most common shape of a first call. A large part of what the report says is usually which candidates are worth doing at all and in what order — and for perhaps a third of the organisations we talk to, the honest first answer is that one or two of their problems are not AI problems and would be better solved another way. Hearing that in week one is worth more than a roadmap that has you starting in the wrong place.

Both, and deliberately so — separating them is how a lot of AI programmes stall, with a strategy document on one side and nobody accountable for making it real on the other. The advisory work produces the sequence, the cost model and the benchmarks; the delivery work is our engineers building it alongside your team. You can buy the first without the second, and it is priced separately, so a recommendation is never a route into a build contract.

From your side of the business, in the second call, before anything is built. A useful benchmark is almost always a number you already report and already argue about — how long a case takes to close, how often something has to be reworked, what proportion of a queue clears without a person touching it. Model-level scores are a diagnostic we use internally, but they are not what we ask you to judge the engagement on, because they can move without anything in your business moving at all.

Usually, and often substantially — but we would want to check it is the right thing to fix first. The first production version of an AI workflow is nearly always more expensive than it needs to be: oversized context, the expensive model used everywhere, no caching, and no attribution of spend to the workflow that caused it. That is straightforward work. What we look at alongside it is quality, because a system that returns the wrong answer often enough costs far more in checking and rework than it ever costs in tokens, and cutting the model bill while that is true makes the real problem harder to see.

Yes. The same workflow deploys three ways — inside your own cloud tenant, on-premise on hardware you own, or fully isolated with no external network at all. We currently run seven fully isolated deployments. What changes between them is where the model runs and what, if anything, crosses your boundary; the access controls and the audit record are identical in all three. If your procurement or security team wants the detail behind that, we will walk them through a real one rather than describe it.

Practitioners on the first call, not account managers. The engineers who would do the work are the ones who scope it and the ones who write the report, and they stay on the engagement afterwards. Nobody is handed over to a delivery team they have never met after the contract is signed.

No. AI engineers lead, with full-stack, DevOps, data and QA engineers alongside them as the work needs, up to a complete AI project team.

Yes. We can scope and deliver an AI project end to end at a fixed price, paid by milestone, or start with a dedicated team and move to delivery once the scope is clear.

We do. We run the hiring and quality checks, and stay the employer for payroll, HR and compliance. You get the engineer you need, working in your team.

Yes. Our AI leadership works with your pre-sales team: we shape the use case, size effort, cost and time to production, and support the proposal. Once you win, we can staff the delivery team.

No. Every engineer we place is our own permanent staff, experienced in production AI and recruited from the IITs and NITs.

You interview before you accept, and there is no substitution after sign-off. The engineer in the profile is the engineer on your access list.

Inside it: your repository, your standup, your review process and your definition of done. Knowledge transfer happens as they work, not in a document at the end.

Yes. Much of our own delivery is air-gapped for Indian defence and public-sector customers. Background checks and NDAs are standard, and clearance is arranged where the site requires it.

Anything from a quarter around a launch to a multi-year embedded team. Every engagement has a planned end, and ramping down is part of the plan.

Both, and modernisation is the more common engagement. The approach is incremental: strangling the legacy system route by route while it keeps serving traffic, rather than a parallel rewrite with a switchover date. Big-bang rewrites of systems that still earn money are the highest-risk option available, and usually the one proposed first.

AI applied to the engineering process itself: code generation and review, test authoring, migration work and documentation, where it measurably shortens the loop. It is used as a delivery tool rather than a talking point, and it does not change who is accountable for the result.

Yes, and we prefer to. CI/CD, observability, SRE practice and on-call response are part of the engagement rather than a handover document, because the run layer is where a product's real cost of ownership is set.

Embedded rather than adjacent. Our engineers work in your repositories, your ceremonies and your review process, and the intent is that your team can carry the system afterwards, which means the knowledge transfer is continuous rather than an event at the end.

Yes, that is what the scenario configurator exists for. Instructors build and vary drills themselves, including environments, injected events and failure conditions. A training system that requires a vendor round-trip to change a drill stops being used within a year.

Yes. The simulation engine, scenario library, instructor station and analytics all run inside your network, and the system is built for estates where a cloud-hosted trainer cannot be accredited. No scenario data, session footage or performance record leaves the site.

A per-trainee timeline of the run: the sequence taken, where they hesitated, which SOP gates were satisfied, and where an incorrect branch was chosen and what followed from it. The point is evidence of competence rather than a record of attendance.

Yes. Multiplayer role assignment puts a whole crew in one scenario, each in their own role, so coordination and communication are part of the exercise rather than assumed. The instructor can inject events mid-run to test how the crew adapts.