How it works
Connect your document library and we read each document once into the model's memory. Every question after that is answered straight from memory, in the chat workspace or through your own agents and tools.
The problem
Per-token AI APIs charge you to push the same documents through the model again and again. The bigger your library and the more you ask, the more you pay for reading text the model has already seen.
Every question sends the same documents back through the model. You pay to re-read material you already paid to read, over and over, and the bill climbs with your traffic.
The more context each question carries, the longer answers take. As your library grows, the wait grows with it, and your users feel it.
The caches offered by the big AI APIs expire quickly, so the savings rarely materialize. You end up re-sending whole documents to rebuild state that existed a short while ago.
The approach
Engram CaaS uses Cache Augmented Generation. We read each document once into the model's memory and serve every answer from that memory. The reading is paid once at onboarding. After that, input and context tokens are never billed, so you get the same accuracy as retrieval based AI at a fraction of the cost.
You pay for the read one time, at onboarding. Every question after that is answered from memory, with input and context tokens never billed, so cost per answer stays low no matter how much you ask.
Answers draw on the full document, so nothing gets lost to a truncated chunk. Accuracy holds, measured head to head against retrieval based AI on the same questions and the same material.
Your library stays ready in the model's memory, so answers start in milliseconds. Speed stays steady whether the library holds a handful of documents or your whole knowledge base.
The pipeline
Bring your library through the SharePoint connector, the Google Drive connector, or direct upload.
Each document is read a single time into the model's memory and remembered from then on.
Ask in the chat workspace, or wire your own agents and tools to your library's MCP endpoint.
Every answer draws on your whole library from memory, and stays current with every document change.
Your library is isolated per tenant and encrypted at rest and in transit. It is never used to train any model, and when you delete a document it is gone from serving for good. Guarantees you can put in a contract.
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