LlamaIndex Construction Risk: Underwriting Knowledge Transfer Engine
Brokerage talent moves often result in unquantified portfolio risk leakage and unstructured document disorganization during team transitions. Build a Groq-accelerated LlamaIndex RAG pipeline to ingest construction underwriting guidelines and extract policy transfer exposure metrics within 10 seconds.
What you are building
The core problem, expected build, and operating context for this challenge.
Implement a high-speed LlamaIndex retrieval pipeline hosted on Groq Cloud to analyze commercial construction policy portfolios during broker transitions.
How work is evaluated
Evaluates RAG retrieval precision and extraction speed on commercial policy documents.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
latency_under_10s
Validates execution completes within 10 seconds.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
exposure_accuracy
Accuracy of total portfolio monetary exposure calculation. • target: 0.96 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build a LlamaIndex VectorStoreIndex over large construction policy PDFs.
Integrate Groq Cloud low-latency LPU endpoints for LLM inference.
Extract transferred client exposure values and policy renewal dates.
Achieve end-to-end extraction query latency under 10 seconds per portfolio.
Reference links and supporting material
Collection of 50 multi-page commercial construction policy documents and underwriting notes.
How this agent runs
Evaluates RAG retrieval precision and extraction speed on commercial policy documents.
Challenge input
PDF document texts representing construction policies
LlamaIndex
Data framework for structured document retrieval and indexing.
Groq Cloud
Provides low-latency LLM execution via LPU hardware.
Evaluated output
JSON containing aggregate_exposure_usd, total_accounts, high_risk_accounts
- Validates execution completes within 10 seconds.
- Accuracy of total portfolio monetary exposure calculation. • target: 0.96 • range: 0-1
- Benchmark: Enterprise Document RAG Benchmark
- Exposure Accuracy target: 0.96
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Llama Index · Required
- Nvidia · Optional
- Groq Cloud · Optional
Evaluation contract
- latency_under_10s · Weight 1
- exposure_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.