A prototype proves the idea. Engineering makes it reliable, observable, affordable and safe to change. We build AI systems that hold up at production volume — and stay maintainable after the team that built them moves on.
The prototype worked on twenty examples. Production has two hundred thousand, a latency budget, a security review, a cost ceiling, an on-call rota and a model provider who will deprecate your endpoint in nine months. That is the gap AI engineering closes.
Chunking strategy, hybrid search, reranking, metadata filtering and retrieval evaluation. Most RAG failures are retrieval failures, and they are measurable long before a user complains.
Training, adaptation and distillation where they earn their cost — with reproducible pipelines, versioned datasets and a clear-eyed view of when prompting is the cheaper answer.
CI/CD for prompts, models and agents. Versioning, staged rollout, canary release, rollback, and reproducible environments so a change can be traced to a behaviour shift.
Golden datasets, task-level metrics, LLM-as-judge with human calibration, and regression suites that run automatically before any prompt or model change reaches users.
Trace-level visibility into every call, tool invocation and token spent, with quality, latency and cost dashboards that operations teams can actually act on.
Model routing, caching, batching, context discipline and quantisation. Unit cost per transaction is engineered deliberately rather than discovered on the invoice.
The architecture is provider-neutral by design. Model choice is a routing decision made per task on cost, latency and quality — not a strategic commitment made once and regretted later.
Evaluation firstBefore a system is built we agree how it will be measured, and the harness is written alongside the first sprint rather than after go-live.
Cost as a design constraintUnit economics are tracked from sprint one and reported in the same review as quality.
Deterministic where possibleNot everything needs a model; the cheapest reliable path wins, and we will argue for it.
Auditable by constructionTraces, versions and decisions are captured because a regulated client will eventually ask, and because it is how you debug.
Handover assumedDocumentation, runbooks and eval suites are deliverables, not afterthoughts.
We build and run our own AI products — DROITai, Devailey and TalentAlign — under the same discipline we bring to client systems. The patterns are proven on our own uptime.
6,000+ person-years of engineering expertise spanning data platforms, application engineering and AI, so the AI layer is not designed in isolation from the systems it must live inside.
Appraised process maturity applied to AI delivery — traceability, review discipline and defect management, adapted for probabilistic systems rather than abandoned for them.
Onshore leadership with AgileSourcing pods in Hyderabad and Pune, or a dedicated GCC under Build-Operate-Transfer where you want the team to become yours.
Usually retrieval first. Fine-tuning earns its cost when you need format control, latency reduction or a narrow domain behaviour that prompting cannot reach reliably. We test the cheaper option before recommending the expensive one.
Routing smaller models to easier tasks, aggressive caching, context discipline, batching where latency allows, and cost telemetry reported per transaction so regressions surface in days rather than at quarter end.
Yes. We start with an engineering assessment — evaluation coverage, observability, cost profile, security and maintainability — and return a stabilisation plan before touching the code.
Talk to an Adroitent AI lead. We will come with a point of view on your estate, not a generic capability deck.
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