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AI and data programs built for enterprise team upskilling.

Review the two flagship programs side by side, with course scope, audience fit, delivery format, and assessment-backed details laid out clearly.

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AI Clarity
AI awareness

AI Clarity

A BFSI-focused awareness program that helps teams understand what AI, GenAI, RAG, Agentic AI, and prompt engineering actually mean in day-to-day work. It is designed for technical and non-technical teams together, with practical banking, insurance, lending, and operations examples throughout.

Includes pre/post assessment with virtual proctoring.

Audience Technical and non-technical teams
Format 4-hour or 8-hour live session
Industry BFSI scenarios throughout
  • Plain-language explanation of GenAI, RAG, Agentic AI, and prompt engineering
  • Hands-on BFSI examples covering underwriting, claims, compliance, KYC, and operations
  • Responsible AI guidance, privacy boundaries, and safe workplace usage
Discuss program fit
AI Ready
AI readiness

AI Ready

A readiness-focused technical program for teams that need to move from AI awareness into practical implementation thinking. It covers model workflows, evaluation, deployment context, and how enterprise teams can prepare for production-oriented AI work without losing business alignment.

Includes pre/post assessment with virtual proctoring.

Audience Engineers, analysts, and AI teams
Format Instructor-led cohort
Industry Enterprise AI implementation
  • AI workflow foundations, data preparation, and evaluation practices
  • Training, inference, monitoring, and model delivery considerations
  • Implementation context for enterprise AI and ML workflows
Discuss program fit
Next step

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  1. Part 1: Why AI, why now
  2. Module 01: The AI evolution and why BFSI teams feel the shift first
  3. Module 02: What GenAI actually changes in underwriting, claims, compliance, and operations
  4. Part 2: Understanding the technology
  5. Module 03: Foundation models and LLMs in plain language
  6. Module 04: Fine-tuning, RAG, and Agentic AI, and when to use each
  7. Module 04B: Live Agentic AI workflow demonstration
  8. Part 3: Prompt engineering
  9. Module 05: Writing prompts that produce useful business outputs
  10. Module 05B: Guided hands-on exercises with live AI tools
  11. Part 4: BFSI case studies
  12. Module 06: Underwriting, claims, BPO, and credit workflow examples with measurable outcomes
  13. Part 5: Responsible AI and readiness
  14. Module 07: Hallucinations, bias, privacy, and regulatory direction
  15. Module 08: A practical checklist before using any AI tool for work
  1. Part 1: AI workflow foundations and problem framing
  2. Data preparation and feature thinking for enterprise AI teams
  3. Evaluation basics and model-quality checkpoints
  4. Part 2: Model building and delivery context
  5. Training patterns, inference flows, and deployment tradeoffs
  6. Monitoring, iteration, and production readiness
  7. Part 3: Enterprise implementation readiness
  8. Governance, guardrails, and delivery responsibilities
  9. Applied use cases for analytics, engineering, and AI teams
  10. Readiness review with assessment-backed checkpoints