MACROBOND AI DATA FEED  ·  EVALUATION FRAMEWORK

AI Data Infrastructure Checklist

A practical evaluation framework for data engineers, quants, and AI infrastructure leads assessing macro data feeds for model and agent consumption.

Most macro data vendors claim their feeds are ‘AI-ready.’ In practice, that usually means they’ve added an API endpoint. The data itself may still be inconsistently sourced, sparsely documented, and missing the structure that AI agents and quantitative models actually need.

When to use this checklist
01
Evaluating a new vendor

Assess a macro data feed before you commit.

02
Auditing an existing feed

Check a feed you already use for AI readiness.

03
Scoping build vs. buy

Weigh in-house build against a vendor feed.

04
Model risk review

Respond to a validation or governance request.

How it works

Mark each question and we'll score it:

Pass3 pts Partial2 pts Fail1 pt
01
Data Quality & Attributes

Can agents reliably find, interpret, and use the data?

02
Legal & Compliance

Will legal, risk, and compliance approve this data in AI workflows?

03
AI Workflow & Interaction

Does it integrate cleanly and support real-world agent usage?

Start evaluation
Evaluation Result

Your evaluation summary

Based on 9 questions across 3 sections.

0/27

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Score by section
Where the gaps are

No gaps flagged

Every question scored a full Pass. This feed meets the framework end to end. Document any caveats and proceed.

See how Macrobond scores against your checklist

The Macrobond AI Data Feed was built specifically for the criteria in this document — point-in-time data with curated metadata, delivered via Web API, Skill endpoint, and MCP Server.

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