RANK TEAM — APR 18, 2026
Rank
Put the most relevant result first, every time
Rank reorders retrieved documents so only the passages that truly answer the query reach your RAG pipeline and agents — cutting tokens, latency, and noise.
Trusted by enterprises and developers worldwide
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Better retrieval starts with better ranking
From feeding agents the right context to grounding RAG answers, Rank makes sure the most relevant result comes first.
Support intelligent agents
Hand agents leaner, more relevant context — less trace bloat, sharper task execution, and fewer wrong turns.
Top diabetic treatment plans by success rateSearch
Best matches — Clinical insights agent
RANK 10.93
Diabetes_Treatment_Outcomes
RANK 20.90
Patient_Treatment_Adherence
RANK 30.86
Diabetes_Management_Protocols
Learn more about Rank
RANK TEAM — MAY 09, 2026
Cutting RAG token cost by ranking before you generate
RANK TEAM — JUN 01, 2026
Ranking relevance across 100+ languages
Designed for the realities of enterprise retrieval
Real corpora are long, messy, and multilingual. Rank scores relevance across all of it — without truncation or a translation step.
Cross-attention for fine-grained ranking
Rank compares the query and each document directly, lifting result quality for complex, under-specified queries that embedding-only search gets wrong.
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Handles long, messy enterprise documents
Score lengthy filings, manuals, and support tickets without truncation — so the one relevant passage deep in a document isn't lost.
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Multilingual ranking out of the box
Rank orders results by relevance across 100+ languages, even when the query and the document don't share a language.
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Secure. Scalable. Seamless.
Privately deployable: Deploy Rank in your virtual private cloud or on-premises for full control over data privacy and security.
Built for speed and scale: Reorder retrieved results in real time with minimal latency, cutting compute cost across your RAG system.
Easy to integrate: Add precision to an existing search pipeline with a few lines of code — no heavy setup or system changes.
Sharper retrieval inside your workplace tools
Atlas
The enterprise agent platform. With Rank in the loop, Atlas agents act on the most relevant context — not the noisiest.
Learn moreBeacon
Intelligent search and discovery. Rank reorders every result set so the document people need surfaces first.
Learn moreDeployed and delivering — what teams say about Rank
◇ ORBIX
“Adding Rank to our retrieval stack was the single biggest jump in answer quality we have measured. The right document is simply at the top now, and our RAG token bill dropped because we stopped sending the model noise.”
— Priya Nandal, Founder
Ready to refine your retrieval pipeline?
Talk to our team to learn how Rank improves result quality, reduces system load, and scales semantic retrieval into production.
- Evaluate how Rank improves accuracy and efficiency across your retrieval stack
- Explore how Rank, Index, and the agent models work together in RAG pipelines
- Find the right deployment model for your infrastructure and privacy needs
- Get help adding Rank to your stack — no major setup or system changes