VEDNIQ logo VEDNIQ
Enterprise Catalog

Enterprise AI Upskilling

Flagship programs mapped to business outcomes, designed for corporate cohorts with integrated proctoring and secure validation.

AI Clarity AI Awareness

AI Clarity

A BFSI-focused awareness program designed for technical and non-technical teams together, with practical banking, insurance, lending, and operations examples throughout.

  • 4-hour live session
  • Virtual or on-site
  • Proctored assessment
  • Cross-functional
USD $50 per participant
AI Ready AI Readiness

AI Ready

A readiness-focused program covering model workflows, evaluation, deployment and how teams can prepare for production AI work without losing business alignment.

  • 8-hour live session
  • Implementation-focused
  • Proctored assessment
  • Hands-on
USD $50 per participant
Need help choosing?

Not sure which program fits your teams?

We can map your audience, learning objectives, and delivery model to the precise course path before you move into formal scheduling.

Request Consultation
  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