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Challenge

New October Industrial Complex Financing Agent

ADCB Egypt and IDG's $12m financing deal for New October industrial park requires tracking tenant leasing milestone disbursements. Build a stateful automated workflow in Python using FastAPI and PostgreSQL to evaluate tenant lease terms and trigger bank loan drawdowns. Ensure zero unauthorized disbursement executions across test lease submittals.

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Challenge brief

What you are building

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

Build an automated loan drawdown workflow for industrial complexes using FastAPI and PostgreSQL.

Delivery guide

How work is evaluated

Evaluation

Evaluates loan drawdown authorization logic against industrial tenant lease parameters.

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

Condition Precedent Check

Fails if approved is true when lease_signed is false or completion_pct < 50.

Binary check

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

Dimension 2

Disbursement Accuracy Rate

Percentage of valid drawdown requests correctly authorized without errors. • target: 1 • 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

  • Design transactional database schemas in PostgreSQL for industrial facility financing

  • Build FastAPI endpoints for processing tenant lease agreements and site progress milestones

  • Enforce compliance rules before issuing tranche disbursement instructions

  • Audit all loan drawdowns with immutable transaction logging

Resources and assets

Reference links and supporting material

Dataset notes

New October industrial city lease contracts and construction phase progress data.

Challenge setup

Evaluates loan drawdown authorization logic against industrial tenant lease parameters.

Challenge input

JSON object with complex_id, tenant_lease_signed, completion_pct, requested_drawdown_egp

FastAPI

Standard Python microservice development framework.

Evaluated output

JSON object with approved, tranche_amount_egp, rejection_reason

Evaluation checks
  • Fails if approved is true when lease_signed is false or completion_pct < 50.
  • Percentage of valid drawdown requests correctly authorized without errors. • target: 1 • range: 0-1
Configured evaluation evidence
  • Benchmark: FinAPI-Bench
  • Disbursement Accuracy Rate target: 100%
  • 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
  • Zed · Required
  • ElevenLabs · Optional
  • Traceloop · Optional

Evaluation contract

  • Condition Precedent Check · Weight 1
  • Disbursement Accuracy Rate · Weight 1

Metrics

  • Disbursement Accuracy Rate · Target 1

Benchmarks

  • FinAPI-Bench
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with complex_id, tenant_lease_signed, completion_pct, requested_drawdown_egp

    Output: JSON object with approved, tranche_amount_egp, rejection_reason

    Execution harness: Python

Evaluation tools

  • FastAPI · Web Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about New October Industrial Complex Financing Agent