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Challenge

Implement Real-Time Venture Capital Due Diligence Engine with Vercel AI SDK

Tight capital markets demand rapid financial risk assessment for early-stage fintech investments. Construct a streaming due diligence agent using Vercel AI SDK and Mastra AI to synthesize startup pitch decks and regulatory filings, calculating risk scores with 90% accuracy.

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Challenge brief

What you are building

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

Develop a streaming TypeScript backend using Vercel AI SDK and Mastra AI to evaluate fintech valuation and risk signals concurrently.

Delivery guide

How work is evaluated

Evaluation

Evaluates risk scoring accuracy against financial health datasets of historical fintech startups.

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

Critical Insolvency Warning

Triggers alert when runway is under 6 months.

Binary check

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

Dimension 2

Scoring Accuracy

Correlation with benchmark risk labels. • target: 0.92 • range: 0.85-1

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

  • Leverage Vercel AI SDK `streamText` and tool calling for structured risk generation.

  • Integrate Mastra AI memory tools to store dynamic startup evaluation context.

  • Synthesize balance sheets, pitch deck summaries, and market risk metrics into standardized scores.

  • Provide real-time streaming UI feedback for investment analysts.

Resources and assets

Reference links and supporting material

Dataset notes

Anonymized financial metrics dataset of early and growth-stage UK fintech startups.

Challenge setup

Evaluates risk scoring accuracy against financial health datasets of historical fintech startups.

Challenge input

JSON containing annual revenue, burn rate, runway months, and regulatory flags

AI SDK

Provides typed JSON streaming responses.

Mastra AI

Stores historical interaction memory for portfolio evaluation.

Evaluated output

JSON with solvency_score (0-100) and vulnerability_summary

Evaluation checks
  • Triggers alert when runway is under 6 months.
  • Correlation with benchmark risk labels. • target: 0.92 • range: 0.85-1
Configured evaluation evidence
  • Benchmark: FintechVCEval
  • Scoring Accuracy target: 0.92
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript 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
  • Vercel AI SDK · Required
  • Mastra AI · Optional
Orchestration
  • Vercel AI SDK · Required
  • Mastra AI · Optional

Evaluation contract

  • Critical Insolvency Warning · Weight 1
  • Scoring Accuracy · Weight 1

Metrics

  • Scoring Accuracy · Target 0.92

Benchmarks

  • FintechVCEval
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing annual revenue, burn rate, runway months, and regulatory flags

    Output: JSON with solvency_score (0-100) and vulnerability_summary

    Execution harness: JavaScript

Evaluation tools

  • AI SDK · Streaming Engine
  • Mastra AI · Agent System

Recipe freeze marker

No recipe freeze time is recorded.

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