Raw rows in,
structured assets out.
Multi-label classification, entity extraction, taxonomy mapping, sentiment, intent and relevance grading — the metadata layer that makes search, recommendation and RAG actually work.
Built for production, not just demos.
- Multi-label catalog taxonomy mapping
- Named-entity recognition (NER) — person, place, brand, product
- Intent + sentiment classification across languages
- Search relevance grading (4-point and 7-point scales)
- Recommender ground-truth (similar-item, query-item)
- Embedding-based similarity audits
- Open + closed ontology support
- Multi-language enrichment with native reviewers
How a typical engagement runs.
Taxonomy
Lock the label space — either map to a public ontology or co-design a custom one.
Calibrate
100-item gold set; refine guidelines and resolve ambiguities before launch.
Pilot
5k-item pilot with IAA report + per-label confusion matrix.
Production
Scale to weekly batches with live IAA dashboards and adjudication queue.
Evolve
Quarterly taxonomy refresh based on emerging categories and drift signals.
What you get in your bucket.
Questions, answered.
Can you work with my existing taxonomy?
Yes — we'll map directly to your private taxonomy or to public ontologies (IAB, Schema.org, Google Product Taxonomy). For new categories we co-design the schema with your ML/PM team.
How accurate is the enrichment?
Average inter-annotator agreement is 0.93. We publish per-batch and per-label IAA plus confusion matrices so you can target the weak spots.
Do you support search-relevance grading?
Yes — 4-point and 7-point relevance scales with calibration to your judges' guidelines. Common for retail, news and document-search teams.
Can you produce ground-truth for recommenders?
Absolutely. Similar-item pairs, complementary-item pairs, and query-item relevance — calibrated with your business metrics.
Ready to build
AI you can trust?
Talk to a solutions architect — get a pilot scoped in 48 hours.