An enterprise-grade rollout track — governance, compliance, security guardrails, and picking the right vendor between Bedrock, Vertex AI, and Azure.
For: Enterprise architects, CIOs, and platform teams
Follow the steps in order. Each link opens an existing guide in the Learn Hub.
How to build and scale an AI CoE in Indian enterprises
Stand up an AI CoE before deploying at scale.
Open guideEnterprise AI adoption methodology: Validate, Architect, Upskill, Lock Down, Transform
A transformation framework for AI at the enterprise tier.
Open guideCompliance guide for AI in regulated Indian enterprises
Compliance in India — DPDP, sector guidance, audit.
Open guideLLM guardrails, monitoring, data redaction for compliance
Security guardrails for production LLM workloads.
Open guidePII redaction, audit trails, and compliant prompt patterns
Prompting patterns for regulated industries.
Open guideEnterprise AI platform comparison with India pricing
Vendor selection — the three major hyperscaler stacks.
Open guideEnterprise Bedrock setup with Mumbai region and pricing
Hands-on Bedrock setup.
Open guideEnterprise VertexAI setup with India region and pricing
Hands-on Vertex AI setup.
Open guideIndian enterprises adopting AI in 2026 are past the proof-of-concept phase — they're choosing between Bedrock, Vertex AI, and Azure OpenAI, drafting governance policies, and answering DPDP Act and RBI compliance questions. This path is the structured run for platform teams, CIOs, and enterprise architects: pick the right hyperscaler, set up guardrails, document the policy, train the org, and report up. It's not a marketing tour — it's the actual playbook.
Lightly — the focus is hyperscaler-led deployment because that's what 90 % of Indian enterprises are doing. For fully on-prem, see the Local AI for Indian Developers path for the technical layer; the policy and governance content here still applies.
It's a strong starting point. You'll still want a domain-specialist legal review before going live, especially for regulated sectors (banking, insurance, healthcare). The path tells you exactly what to bring to that legal review.
Typical: 3-6 months from kickoff to first production workload, 9-18 months to broad org adoption. The path doesn't accelerate that timeline — it stops you from making expensive backtracking mistakes.
The path explicitly covers when in-house fine-tuning makes sense (rarely, and only after specific conditions are met) versus when buying is the right call (almost always at first). It saves teams from premature platform projects.
Done with this path? Try another one.
See all 10 learning paths