DealerIQ

Context-aware AI agents for collectible retailers: DealerIQ uses DataHub to turn launch demand, inventory, fraud signals, and lineage into auditable purchase-limit decisions.

Comment

Inspiration

Inspiration

Collectible retailers live in a strange pressure zone: a product launch can be exciting, emotional, and profitable, but also chaotic. Demand spikes, inventory feeds go stale, reseller clusters appear, and small shops have to make fast allocation decisions without the context that larger platforms take for granted.

DealerIQ is a launch-command cockpit for that moment.

What It Does

DealerIQ uses DataHub as the context layer for a team of AI agents that answer a practical retail question:

Should this product launch have a purchase limit, and why?

The system resolves DataHub metadata before acting, including:

  • canonical sales and inventory datasets
  • active forecast models
  • customer velocity and order-risk features
  • freshness and quality signals
  • lineage between inputs, model outputs, and recommendations

Then the agents produce a grounded launch recommendation, write evidence back as metadata, and expose an audit trail that a retail operator or data team can inspect.

How It Uses DataHub

DealerIQ treats DataHub as more than a catalog. It uses DataHub as the operational memory for AI agents:

  • agents retrieve trusted assets before making decisions
  • recommendations cite upstream datasets and ML models
  • prediction, lineage, and quality events are written back as Metadata Change Proposals
  • the final decision can be audited through context, evidence, and source lineage

Technical Execution

The project includes a deterministic evaluation harness with paired ablations and robustness tests:

  • 96 paired benchmark scenarios
  • 100% calibrated DealerIQ policy accuracy
  • +28.1 percentage-point lift over a no-context baseline
  • 240 seeded holdout scenarios
  • 192 holdout boundary cases with 0 DealerIQ errors
  • 1,198 invariant checks with 0 violations
  • 49/49 submission audit checks passing

What I Learned

The biggest lesson was that useful AI agents need more than a model response. They need governed context, provenance, freshness awareness, and a way to leave evidence behind for the next person or agent.

DataHub made that framing concrete: the agent is not just answering a question, it is participating in a metadata graph.

Challenges

The hardest part was balancing a believable retail workflow with rigorous evaluation. I wanted the demo to feel specific and human, not like a generic agent dashboard, while still giving judges enough scientific evidence to trust the behavior.

DealerIQ is my attempt to make AI agency feel accountable, situated, and useful in a real operational setting.

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for DealerIQ

Built With

Share this project:

Updates