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Challenge

Vercel AI SDK Real-Time Commercial Office Investment Dashboard

Institutional capital is returning to top-tier commercial office markets through selective target investments. Build a real-time streaming investment screening agent using Vercel AI SDK (`ai`) to process market sentiment, vacancy rates, and capital return signals live.

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Challenge brief

What you are building

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

Develop a real-time streaming office investment dashboard using Vercel AI SDK to filter high-conviction commercial acquisitions.

Delivery guide

How work is evaluated

Evaluation

Evaluates schema accuracy and streaming response correctness for office screening.

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

zod_schema_match

Ensures returned object validates against defined Zod target schema.

Binary check

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

Dimension 2

conviction_accuracy

Accuracy of conviction tier assignments against benchmark rules. • target: 0.95 • 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

  • Use Vercel AI SDK `streamText` and `generateObject` with TypeScript schemas

  • Implement structured dynamic UI streaming using React / Next.js hooks

  • Filter market signals based on physical vacancy vs effective post-tech occupancy

  • Construct dynamic investment conviction tags (BUY, HOLD, AVOID) based on capital return data

Resources and assets

Reference links and supporting material

Dataset notes

Quarterly market telemetry for 25 primary and secondary US office submarkets.

Challenge setup

Evaluates schema accuracy and streaming response correctness for office screening.

Challenge input

JSON market metrics (market_name, prime_vacancy, badge_swipe_rate, capital_inflow_m)

AI SDK

Provides TypeScript-first streaming and structured object generation.

Evaluated output

JSON with market_name, conviction_rating, rationale

Evaluation checks
  • Ensures returned object validates against defined Zod target schema.
  • Accuracy of conviction tier assignments against benchmark rules. • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: CREMarketScreeningBench
  • Conviction Accuracy target: 0.95
  • 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
  • Hume AI · Optional
  • Arize AI · Optional
Orchestration
  • Vercel AI SDK · Required

Evaluation contract

  • zod_schema_match · Weight 1
  • conviction_accuracy · Weight 1

Metrics

  • conviction_accuracy · Target 0.95

Benchmarks

  • CREMarketScreeningBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON market metrics (market_name, prime_vacancy, badge_swipe_rate, capital_inflow_m)

    Output: JSON with market_name, conviction_rating, rationale

    Execution harness: JavaScript

Evaluation tools

  • AI SDK · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Vercel AI SDK Real-Time Commercial Office Investment Dashboard