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Challenge

Build a Pydantic AI VC Portfolio Liquidity Predictor

Venture capital firms tracking startup investment rounds require structured runway and liquidity analysis. Construct a Pydantic AI investment analyst agent that processes funding updates, calculates runway projections, and enforces explicit type constraints with 100% schema validation accuracy.

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Challenge brief

What you are building

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

Develop a Pydantic AI investment analyst agent that evaluates funding round telemetry and projects venture portfolio runway.

Delivery guide

How work is evaluated

Evaluation

Evaluates portfolio model accuracy and Pydantic output validation success.

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

runway_math_check

Verifies accuracy of runway division math

Binary check

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

Dimension 2

schema_conformance

Validation rate against target Pydantic models • 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

  • Utilize Pydantic AI agent models to process investment round announcements

  • Calculate quarterly burn rates and post-money valuation metrics

  • Enforce strict numeric schemas for financial metric models

  • Output standardized portfolio vulnerability reports for investors

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic venture capital funding round summaries and financial metrics.

Challenge setup

Evaluates portfolio model accuracy and Pydantic output validation success.

Challenge input

JSON funding event payload

Pydantic AI

Type-safe financial analysis framework

Evaluated output

Pydantic model JSON object with runway_months and status

Evaluation checks
  • Verifies accuracy of runway division math
  • Validation rate against target Pydantic models • target: 1 • range: 0-1
Configured evaluation evidence
  • Benchmark: Financial Modeling Agent Benchmark
  • Schema Conformance target: 1
  • 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
  • Pydantic AI · Required
  • RAI · Optional
  • ZenML · Optional
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • runway_math_check · Weight 1
  • schema_conformance · Weight 1

Metrics

  • schema_conformance · Target 1

Benchmarks

  • Financial Modeling Agent Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON funding event payload

    Output: Pydantic model JSON object with runway_months and status

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

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