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Challenge

SEBI DRHP Compliance RAG Pipeline with LlamaIndex

Fintech startups preparing for IPOs face exhaustive SEBI disclosure audits across thousands of financial report pages. Build an automated DRHP compliance audit pipeline using LlamaIndex to extract financial metrics and verify statutory compliance with 95% metric precision.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build an enterprise SEBI Draft Red Herring Prospectus (DRHP) compliance and auditing pipeline using LlamaIndex structured RAG agents.

Delivery guide

How work is evaluated

Evaluation

Evaluates financial metric extraction accuracy and statutory compliance disclosure verification from sample DRHP filings.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

citation_presence

Verifies that every compliance finding includes specific document page citations

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

metric_extraction_precision

Precision of extracted financial metrics against ground-truth DRHP tables • target: 0.95 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Build advanced RAG pipelines with LlamaIndex SubQuestionQueryEngine

  • Extract complex financial table structures from multi-hundred page PDF prospectuses

  • Validate disclosures against SEBI ICDR (Issue of Capital and Disclosure Requirements) rules

  • Implement automated audit trail generation with citations

Resources and assets

Reference links and supporting material

Dataset notes

Extracted text and tabular contents from public Indian startup DRHP filings with SEBI audit annotations.

How this agent runs

Evaluates financial metric extraction accuracy and statutory compliance disclosure verification from sample DRHP filings.

Challenge input

Document text chunk and required disclosure item ID

LlamaIndex

Data framework for LLM applications featuring RAG, indexing, and query tools.

Evaluated output

JSON with status (COMPLIANT|NON_COMPLIANT|INCOMPLETE), page_citation, and findings

Checks for
  • Verifies that every compliance finding includes specific document page citations
  • Precision of extracted financial metrics against ground-truth DRHP tables • target: 0.95 • range: 0-1
Proof of success
  • Benchmark: SEBI IPO Regulatory Compliance Benchmark
  • Metric Extraction Precision target: 0.95
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Llama Index · Required
  • TensorBoard · Optional
  • Hamming · Optional

Evaluation contract

  • citation_presence · Weight 1
  • metric_extraction_precision · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about SEBI DRHP Compliance RAG Pipeline with LlamaIndex