Catalog and conversational data for commerce that converts.

Commerce AI spans messy catalogs, multimodal product understanding, and conversational shopping. Teams need clean attribute data, image-text grounding, and evaluation of assistants against real shopper intents — across a constantly changing catalog.

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AI use cases

Where it applies.

Product attribute extraction
Catalog and image understanding
Conversational shopping agents
Search and recommendation eval
Multimodal grounding
Customer support agents

Data requirements

What it takes.

Attribute and taxonomy labeling
Image-text grounding
Conversational trajectories
Locale and catalog coverage
Quality sampling
Continuous refresh pipelines

Relevant data products

Products that map to this work.

Data Product

Multimodal AI Data

Image-text data, video understanding, document AI, and cross-modal alignment at production quality.

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Data Product

Agentic AI Data

Golden trajectories, tool-use logs, RL environments, and workflow simulations for agents that ship.

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Data Product

Model Integrity & Evaluation

Private benchmarks, hallucination evaluation, safety red teaming, bias and compliance audits.

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Workflow

How the program runs.

01Scope
02Taxonomy Design
03Production
04Cross-modal QA
05Evaluation
06Continuous Iteration

Continuous loop — outputs feed back into the data engine.

Quality & compliance

Built for regulated, high-stakes work.

Every engagement runs on our quality system and enterprise-grade security workflows — the controls an auditor would expect.

Secure data handling
PII controls
Data rights management
Quality governance

Quality & Security model

Case study

Proof in production.

Enterprise AI

Policy-Driven Task Environments for Enterprise Agent Evaluation

Policy-driven task environments and failure taxonomies for enterprise agents — executable evaluation suites covering tool use, recovery behavior, and escalation accuracy.

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