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m0vom0vo

Discovery & AI

Discover candidates, enrich with AI, keep humans in control

Manual research does not scale when a vendor’s portfolio spans dozens of products. m0vo combines Wikidata discovery with optional AI enrichment so editors can find candidates quickly, fill structured metadata, and still review or override every result.

What you get

  • Vendor and product discovery against Wikidata
  • Guided discovery sessions with multi-select into the catalog
  • AI enrichment for description, capabilities, metrics, and lifecycle
  • Daily enrichment budget and explicit re-enrich actions
  • CSV import with optional post-import enrichment queue

Open-data discovery for vendor portfolios

Start from a vendor name, explore product candidates from Wikidata, and pull selected items into the catalog with dual-key identity (Wikidata QID and normalized name). That reduces duplicate sprawl while accelerating first-pass coverage of a vendor line.

AI enrichment that respects your edits

m0vo can call AI to propose structured product intelligence. Apply paths preserve rows marked manual, store raw enrichment for audit (admin-visible), and count finished jobs against a daily budget so cost stays predictable.

Explicit enrichment, not silent mutation

Creating or editing a product form never auto-calls the model. Enrichment runs on discovery ingest, explicit re-enrich, bulk incomplete re-enrich, or opt-in CSV upload enrichment—so operators know when AI runs and why.

More platform capabilities