AI Factory and Compute Platform
Cost, reliability, and residency, not another model bake-off.
The bottleneck has moved from models to serving. GPU sizing, hybrid inference, LLMOps, and FinOps decide whether AI stays a line item or becomes an ungoverned bill. We design the factory so you buy the right topology, not the largest cluster.
A production AI centre of excellence typically spends more on infrastructure than on the models themselves. Over-buying GPUs and under-governing API spend are the two failure modes we see most.
We do not resell silicon. We recommend API versus self-host versus India-region versus on-prem on unit economics and residency, then build the platform that makes that choice operable: serving, evaluations, observability, and FinOps.
That combination of compute judgement plus model judgement is the work most strategy decks skip and most hardware partners will not volunteer.
Engagement shapes
- 01AI factory blueprint: model mix, serving topology, TCO, and DPDP residency.
- 02LLMOps and MLOps platform: serving, evaluations, observability, and cost controls.
- 03Hybrid inference: API, self-hosted, and batch paths chosen on unit economics, not vendor quota.
- 04Platform operations overlay: capacity planning and reliability after the first production load.