Recommendation engines run on embeddings. Every product, user, and preference is a vector. If one is forged or leaked across accounts, the recommendations downstream are silently wrong.
If similar items, semantic product search, or personalized feeds are part of your product, Mnemo applies. The same setup spans marketplaces, retail media, and platform SaaS. Works with your existing system.
02Why it matters
Bad data looks real without verification.
Catalog injection. A bad seller, an internal bug, or a partner pipeline pushes embeddings that do not match any authorized source. Those products get recommended anyway. Cross-account leakage. On multi-customer retail media and white-label platforms, the proof of isolation today is configuration, not identity. AI-generated content. Generated product images and copy produce embeddings. Without provenance, regulated categories cannot serve them safely.
03What this does
Each customer's data stays verifiable and separate.
Every catalog and preference embedding carries a UID identifying its source and account. Retrieval calls verify() before surfacing results. Cross-account retrieval becomes verifiable, not just configured.
04Scope
What Mnemo does not do.
Does not replace your system
Does not score data quality
Does not filter bad data before embedding
Does not personalize on your behalf
Mnemo focuses on one layer: attaching identity to embeddings and verifying them when they are used.
05Who this is for
Head of Personalization. VP Data Science. VP Retail Media. CTO at marketplace and e-commerce platforms.
06Why teams adopt it
Per-recommendation provenance for incident review.
Cross-border retail media networks need evidence regulators can read.
Recommendation quality debugging at corpus scale.
Multi-customer isolation proof for white-label and marketplace APIs.
Start verifying in minutes.
Free tier covers small pipelines. The first verify is on us.