01
Review layer included
Search relevance
Query-result judgments, graded relevance, intent labels, and difficult-query sets.
Relevance and discovery
Human judgment for better discovery, ranking, and recommendations.
Relevance data and evaluation systems for search, recommendation, catalogs, marketplaces, and conversational commerce.
Signal map
Program outcomes
Better intent coverage
Consistent relevance labels
Actionable failure analysis
Inside the work
Human expertise, connected to a controlled data workflow.
Every engagement links the people doing the work, the evidence used to review it, and the model behavior the program is meant to improve.
Capabilities
Built to fit the model, domain, and decisions behind your program.
02
Review layer included
Catalog intelligence
Attribute extraction, taxonomy mapping, normalization, and entity matching.
03
Review layer included
Recommendations
Preference data, diversity assessment, and contextual ranking evaluations.
04
Review layer included
Conversational commerce
Task data and evaluation for product discovery, comparison, and support agents.
What gets delivered
More than a dataset: a usable package of data, controls, and learning.
The exact artifacts change by program, but every delivery is designed to be inspectable, actionable, and ready for the next model decision.
01 / Design
Collection specification
Population, environment, consent, and signal requirements in one operating brief.02 / Data
Aligned modality set
Timestamped speech, speaker metadata, and linked visual or textual context.03 / Insight
Representation audit
Coverage, signal quality, and exception findings prepared for program decisions.Delivery model
Designed for fast learning and controlled scale.
Programs move in visible stages, with a review point before scope, volume, or complexity increases.
- 01Frame
Map user intent and business rules
- 02Calibrate
Build calibrated judgment tasks
- 03Produce
Measure agreement and edge cases
- 04Learn
Target the largest relevance gaps