Practices
Capabilities
Five practices, one standard: the system must run in production, under audit, at a cost you control. Each page below expands into who sponsors the work, the engagement shapes, and what you hold at the end.
- 01Lead practiceProduction Agentic SystemsEnterprises have finished the demo cycle. What they will fund now are agents that process documents, close tickets, and run multi-step workflows with audit trails: in production, under an owner, with a way to fail safely.
- 02Lead practiceAI Factory and Compute PlatformThe 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.
- 03Lead practiceBFSI Decision IntelligenceBanks, NBFCs, and insurers have budget, measurable P&L, and a reason not to move fast with ChatGPT. They need classical decisioning and generative systems that pass RBI, IRDAI, and model-risk review.
- 04Lead practiceGCC and AI CoE: Build, Operate, TransferIndia's GCCs are becoming the enterprise's AI command centre. Parents fund a veteran operator to stand up the team, platform, and governance and, in full BOT mode, to run the pod until a scheduled transfer puts people, platform, and playbooks on your side of the ledger.
- 05When the plant is readyIndustrial Applied AIManufacturing is one of the few Indian sectors already funding dedicated plant AI. The work that holds is classical: inspection, yield, and predictive maintenance. Generative agents come after the data plane is stable.
Tell us which workflow is stuck.