The use case: how Jenova deploys Pinecone
Jenova uses Pinecone as the retrieval layer for its AI agent platform, storing and retrieving conversation history, uploaded documents and web content, and shared cross-agent user memory. Each user's data is kept in a separate namespace so agents can pull relevant context at query time.
More reported results
Figures published in Pinecone’s case study. They are not independently measured by usedby.
Jenova has grown to over $1M ARR and 200,000 user signups across more than 70 countries, with nearly 100% of that being growth organic.
Combined with Pinecone's retrieval accuracy, this improved judged retrieval relevance by roughly 20–25 percentage points in internal evaluations.
Zero Pinecone-related production incidents since launch.
In the customer’s words
For an agent platform, the quality of the knowledge layer determines whether users stay or leave. Pinecone is what lets us store everything a user has ever worked on and retrieve exactly the right piece of it in milliseconds. That's the foundation our entire product is built on.Pinecone users in Artificial Intelligence
Companies in the same sector, with public evidence.
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Based on Pinecone’s published case study. We check the public evidence; the results are reported by the vendor.
pinecone.io- Last checked
- Nov 11, 2026
Open source capture What the story claims, and what we checked
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- The figure: 70-95%Checked
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- The passage quoted aboveChecked
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- Jenova uses PineconeChecked
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- The result itselfNot checked
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For typical engaged sessions, this reduces token consumption by 70–80%. For deep sessions, the reduction reaches 85%. For the longest-running power sessions, it exceeds 95%. Those savings flow directly into gross margin.