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Challenge

Private Equity AI Risk Audit System with LlamaIndex RAG

Private equity portfolios face significant unmonitored regulatory and operational risks in their deployed AI payment architectures. Build a compliance auditing agent using LlamaIndex to query complex governance documents, evaluate system logs against regulatory frameworks, and achieve 90% precision on compliance gap detection.

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Challenge brief

What you are building

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

Develop an advanced RAG and auditing agent using LlamaIndex to identify governance and compliance blind spots across private equity tech portfolios.

Delivery guide

How work is evaluated

Evaluation

Evaluates precision and grounding accuracy of compliance audit summaries generated across PE portfolio docs.

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_grounding_check

Verifies that all reported regulatory gaps contain valid document citations.

Binary check

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

Dimension 2

gap_detection_precision

Precision score of true compliance vulnerabilities detected. • target: 0.9 • 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

  • Construct multi-document index structures using LlamaIndex for complex regulatory frameworks

  • Build auto-retrieval query engines with metadata filtering for portfolio governance checks

  • Detect compliance gaps between deployed AI payment systems and EU AI Act / SEC guidelines

  • Generate prioritized risk mitigation roadmaps with verifiable source citations

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic enterprise document repository containing 100 architecture specs, AI policy documents, and system audit logs from 10 fintech portfolio companies.

How this agent runs

Evaluates precision and grounding accuracy of compliance audit summaries generated across PE portfolio docs.

Challenge input

JSON input specifying portfolio company ID, document index key, and target regulatory framework

LlamaIndex

Framework for building data-driven LLM applications and agentic RAG.

Evaluated output

JSON audit result listing detected risks, citations, severity, and compliance score

Checks for
  • Verifies that all reported regulatory gaps contain valid document citations.
  • Precision score of true compliance vulnerabilities detected. • target: 0.9 • range: 0-1
Proof of success
  • Benchmark: LegalRAG-Audit-v1
  • Gap Detection Precision target: 0.9
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Llama Index · Required
  • LlamaIndex · Optional
  • Llama 3.2 · Optional

Evaluation contract

  • citation_grounding_check · Weight 1
  • gap_detection_precision · Weight 1

Recipe state

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

Frequently Asked Questions about Private Equity AI Risk Audit System with LlamaIndex RAG