Case Study: Banking Multimodal RAG
Walk a real bank build end-to-end — Azure AI Search + Vision + GPT-4o, document cracking, hybrid retrieval, and an MRM submission that actually passes.
Last updated: 2026-05-19
What you'll learn
By the end of this course you'll be able to:
- Why pure text-RAG fails on financial documents — concretely, with examples
- A Stack Fit Assessment for Azure AI Search, Azure AI Vision, and GPT-4o
- Document cracking across text, tables, and charts in real annual reports
- Indexing and hybrid retrieval strategies at production scale
- An evaluation harness that lets the model risk team sleep at night
- How an MRM submission and regulator pre-check actually read
Who this is for
Technical leaders, ML engineers, and product managers learning by walking through a real bank multimodal RAG build. Especially valuable for teams who have shipped a text-only RAG and run head-first into the realities of charts, tables, scanned annexures, and regulator pre-checks — RBI in India, SAMA in the Gulf — and need to see a production-grade build cohere end-to-end on the Microsoft stack.
Prerequisites
- · ai-in-financial-services
- · rag-architectures-foundations
Curriculum
9 chapters · 2 hands-on exercises · capstone challenge
Each chapter ends with the learning objectives ticked off. Quizzes are auto-graded with feedback; exercises are open-ended and produce artifacts you can take to your team.
1. The banking use case — why pure text-RAG fails on financial documents
- Walk through the failure modes pure text-RAG hits on annual reports and disclosures
- Define the success criteria the bank actually negotiated with the business
2. Stack Fit Assessment — Azure AI Search, Vision, GPT-4o
- Map each capability to a concrete part of the retrieval and generation pipeline
- Justify hybrid search (vector + keyword + semantic ranker) for this workload
3. Document cracking — text, tables, and charts
- Crack scanned PDFs, embedded tables, and chart imagery into retrieval-ready chunks
- Preserve table semantics so the model can reason over rows and columns
4. Indexing strategy
- Design index schemas that carry chunk type, page, and source-document provenance
- Pick embedding models and chunk sizes that hold up across financial document classes
5. Hybrid search at production scale
- Tune hybrid search and semantic ranker for financial vocabulary
- Build re-ranking and citation surfacing the front-end can render trustworthy
6. Evaluation harness
- Design a labelled eval set across retrieval, grounding, and answer accuracy
- Wire continuous evaluation into the CI pipeline so regressions surface early
7. Regulatory acceptance — RBI and SAMA pre-checks
- Map the build to RBI and SAMA expectations on explainability and auditability
- Anticipate the 5 questions regional regulators consistently ask about RAG
8. MRM submission
- Structure a Model Risk Management submission that mirrors Basel-aligned expectations
- Document model lineage, eval results, and human-in-loop controls in a defensible pack
Capstone: Capstone: Adapt the build to your bank
- Translate the reference architecture to your bank’s document set and regulator
- Draft the first 30 days of your own multimodal RAG build plan
Capstone deliverable: Every learner who completes this course produces «Your 30-Day Multimodal RAG Build Plan» — a tangible artifact you take back to your organization.
Curriculum live · full chapter content rolling out through 2026.
The outline, learning objectives, references, and capstone deliverable are published. Full chapter content (video, narration, exercises) ships progressively. Get notified when each chapter goes live.
References & sources
Built on cited sources — not vibes.
Every course is researched fresh against vendor documentation, regulatory sources, and peer-reviewed work. Sources used in this course:
Azure AI Search Documentation
Microsoft Learn · Source link
Azure AI Vision Documentation
Microsoft Learn · Source link
RBI — Working Group Report on Digital Lending
Reserve Bank of India · Source link
SAMA — Cybersecurity Framework
Saudi Central Bank · Source link
BCBS — Principles for the Sound Management of Operational Risk
Basel Committee on Banking Supervision · Source link
Course details
Track
Applied (Case-led)
Level
Advanced
Audience
Technical practitioner, Director, Manager
Function
IT & Engineering, Finance & Accounting
Industry
Financial Services
Stack
Microsoft
Paired Gennoor Way phase
innovate, build
Format
video, reading, hands-on
You finished the course. Now what?
From course to outcome.
Reading this course is step one. The next step is applying it where you work. Here's how Gennoor helps — without the deck, without the pitch.
Run this for your team
A 2-day workshop or virtual cohort for up to 25 of your people, with exercises run on your data and a 30-day adoption plan.
From $5k · 2 weeks · function-specific
Apply this to your data
A 4–6 week pilot that takes what you learned and ships a working system inside your environment. Fixed scope, fixed price, code transferred day one.
From $25k · 6 weeks · production-grade
Just want to talk?
Free 30-minute call. No deck, no pitch. We listen to your situation and tell you honestly what makes sense — even if it isn't us.
Free · no commitment · 30 minutes
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