AI and Data Science

A 2-week AI Opportunity Audit: we review your data, processes, and goals, then prioritize 3 use cases by ROI and feasibility. You leave with a roadmap, cost estimates, and a quick-win PoC plan — no large upfront commitment.

Both. For most clients we use a hybrid — hosted APIs for speed-to-market, open-weight models (Llama, Gemma, Qwen) for privacy-sensitive or cost-optimized workloads. We pick based on data sensitivity, latency, and TCO — not vendor preference.

Yes. We deploy open-weight models on your VPC or on-prem (vLLM, TGI, Ollama) so no data leaves your environment. Common for healthcare, finance, legal, and government clients.

We use RAG with grounded citations, structured outputs, guardrails (input/output validation), evaluation harnesses, and human-in-the-loop for high-stakes decisions. We also monitor production quality with offline evals and alerting.

Most PoCs land between ₹4–12 lakh (US$5k–15k) and run 3–6 weeks. The deliverable is a working, demoable system plus a productionization plan with cost/ROI projections.

Yes. We build the full pipeline — feature store, training, registry, deployment, monitoring, and drift detection — using tools like MLflow, LangSmith, and your existing CI/CD.