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Challenge

LlamaIndex Advanced RAG Cyber Acquisition Due Diligence Engine

Munich Re's $575M acquisition of At-Bay highlights M&A expansion in cyber insurance. Build a LlamaIndex-powered RAG and agent workflow to ingest target cyber MGA underwriting portfolios, evaluate cyber catastrophe aggregation risks, and output automated due diligence audit scoring.

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Challenge brief

What you are building

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

Construct an enterprise LlamaIndex RAG application to perform cyber catastrophe due diligence on MGA target portfolios.

Delivery guide

How work is evaluated

Evaluation

Evaluates retrieval accuracy and due diligence risk score computation across M&A target portfolios.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

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

bound_check

Ensures exposure percentage is between 0.0 and 1.0

Binary check

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

Dimension 2

retrieval_hit_rate

Recall of relevant risk factors from corpus • target: 0.88 • 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

  • Implement LlamaIndex VectorStoreIndex and Document summary indices for target MGA portfolios

  • Build custom QueryEngineTools for cyber attack scenario stress testing

  • Deploy sub-question query engines to synthesize systemic cyber loss exposures

  • Calculate target company risk-adjusted valuation impact factors

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic cyber insurance policy documentation and portfolio risk profiling reports.

Challenge setup

Evaluates retrieval accuracy and due diligence risk score computation across M&A target portfolios.

Challenge input

JSON object with target_mga_name and portfolio_summary document link.

LlamaIndex

Enterprise vector search and hierarchical document indices

Evaluated output

JSON containing systemic_cloud_exposure_pct, max_single_event_loss, and due_diligence_recommendation.

Evaluation checks
  • Ensures exposure percentage is between 0.0 and 1.0
  • Recall of relevant risk factors from corpus • target: 0.88 • range: 0-1
Configured evaluation evidence
  • Benchmark: CyberDueDateBench
  • Retrieval Hit Rate target: 88%
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Llama Index · Required
  • Exa · Optional
  • Command R+ · Optional

Evaluation contract

  • bound_check · Weight 1
  • retrieval_hit_rate · Weight 1

Metrics

  • retrieval_hit_rate · Target 0.88

Benchmarks

  • CyberDueDateBench
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with target_mga_name and portfolio_summary document link.

    Output: JSON containing systemic_cloud_exposure_pct, max_single_event_loss, and due_diligence_recommendation.

    Execution harness: Python

Evaluation tools

  • LlamaIndex · RAG Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about LlamaIndex Advanced RAG Cyber Acquisition Due Diligence Engine