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Challenge

Build a Hines Development Feasibility RAG Agent with LlamaIndex and Mindset AI

Commercial real estate developers struggle to rapidly evaluate capital deployment signals and local zoning constraints across expanding development pipelines. Build a LlamaIndex RAG agent integrated with Mindset AI workflows to ingest real estate filings and market metrics, achieving an F1-score of at least 0.88 on feasibility query answers.

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Challenge brief

What you are building

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

Construct an enterprise RAG pipeline using LlamaIndex and Mindset AI to query complex commercial development filings and municipal zoning constraints.

Delivery guide

How work is evaluated

Evaluation

Evaluates the precision and response accuracy of the LlamaIndex real estate development feasibility agent.

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

Schema Compliance Test

Verifies that the agent returns expected JSON keys.

Binary check

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

Dimension 2

Extraction Precision

Percentage of correctly extracted underwriting features. • 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

  • Implement advanced vector indexing over commercial real estate SEC filings and development proposals using LlamaIndex.

  • Integrate Mindset AI workflow triggers for dynamic data retrieval and alert orchestration.

  • Evaluate RAG retrieval accuracy using precision and recall metrics on complex underwriting queries.

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic and publicly sourced real estate acquisition filings, zoning code summaries, and Hines capital allocation updates.

How this agent runs

Evaluates the precision and response accuracy of the LlamaIndex real estate development feasibility agent.

Challenge input

JSON containing document paths and underwriting questions

LlamaIndex

Required data framework for LLM indexing and RAG operations.

Mindset AI

Automates real estate deal pipeline workflows and triggers.

Evaluated output

JSON containing extracted parameters, zoning compliance flags, and confidence score

Checks for
  • Verifies that the agent returns expected JSON keys.
  • Percentage of correctly extracted underwriting features. • target: 0.9 • range: 0-1
Proof of success
  • Benchmark: RE_Underwriting_Eval
  • Extraction 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
  • Mindset AI · Required
  • Llama Index · Optional
  • Exa · Optional

Evaluation contract

  • Schema Compliance Test · Weight 1
  • Extraction Precision · Weight 1

Recipe state

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

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