Sankofa Rank
A semantic boost to search quality.
Sankofa Rank re-scores your search results with deep semantic understanding, lifting the documents your users actually meant to find to the top — inside your own environment.
Capabilities
Relevance that learns from every query
Relevance optimization
A dedicated ranking model evaluates query-document meaning, not term overlap, so the best answer wins even when the wording doesn't match.
Personalized search
Tailor ranking to user roles, departments, and historical context — each team gets results ordered for its own needs, under your access controls.
Dynamic refinement
Rankings adapt based on real interactions. Click-through and feedback signals continuously sharpen relevance without manual tuning cycles.
The difference
From keyword matching to semantic ranking
Sankofa Rank sits behind your existing index as a re-ranking stage. Same query, same corpus — a fundamentally better ordering.
Before — keyword search
Query: "indemnification cap for suppliers"
-
1
Supplier Onboarding FAQ
Repeats the keywords often — but never answers the question.
-
2
2019 CapEx Committee Minutes
Lexical collision on "cap" — irrelevant to contract liability.
-
3
Master Supply Agreement §14.2
The actual indemnification clause — buried on page two.
After — Sankofa semantic boost
Query: "indemnification cap for suppliers"
-
1
Master Supply Agreement §14.2
Semantically identical intent — ranked first.
-
2
Liability & Indemnity Policy v3
Conceptually adjacent governance document, correctly second.
-
3
Supplier Onboarding FAQ
Keyword-dense but low-value — demoted by meaning, not by hand.
Illustrative example. In production, Sankofa Rank re-scores the candidates your index already returns — no re-indexing, no pipeline rewrite.
Architecture
A re-ranking stage, not a migration
Keep your existing retrieval stack. Sankofa Rank consumes the top candidates from Zuberi Vector, Qdrant, pgvector, or your legacy engine, and returns a semantically re-ordered list in a single API call.
Deployed in your VPC, on-premises, or in a Kervalt-managed Kanda Vault, ranking signals and interaction data never leave your perimeter. Independent by design.
Test Ranking APIPairs with Zuberi Vector
Embed with Zuberi, retrieve candidates, then let Sankofa Rank maximize precision at the top of the list where users actually look.
Measurable uplift
Evaluate ranking quality offline against your own labeled queries before you ship — relevance gains you can defend to stakeholders.
Ready to deploy sovereign AI?
Run Kervalt models in your own cloud, on your own terms.