Question 1

We have lots of data but no AI strategy. Where do we start?

Accepted Answer

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.

Question 2

Do you use OpenAI/Anthropic or build on open-source models?

Accepted Answer

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.

Question 3

Can you build AI that runs on our own infrastructure (private)?

Accepted Answer

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.

Question 4

How do you make sure AI doesn't hallucinate in production?

Accepted Answer

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.

Question 5

What does an AI PoC cost?

Accepted Answer

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.

Question 6

Do you help with AI/ML Ops (MLOps)?

Accepted Answer

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.

AI and Data Science

What's included

AI Chatbot Development

RAG Applications

Agentic AI

AI Workflow Design

Machine Learning

Computer Vision

NLP & Text Analytics

Business Intelligence

Other Services

Questions, answered

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