How it works
Connect a source, train it once into cartridges, then answer every question cheaply — grounded in your own documents.
The problem
It stuffs thousands of context tokens into a frontier model for every question — so you pay full price, again and again, for knowledge you already own.
Every query re-sends ~6k tokens to a premium model. Costs track traffic and never amortize.
Big corpora overflow the window. You chunk, lose context, and answers get shallow.
Sensitive documents stream to third-party APIs on every call. Hard to govern and audit.
The approach
Engram Smart CAG compresses your documents into a trainable KV-cache cartridge — the knowledge, baked into an open model's attention. Read once, infer many.
A one-time training cost, then a flat per-query price far below frontier RAG — and a flat time-to-first-token, both independent of corpus size.
Answers come from your documents with source attribution — measured at RAG parity on single-document QA.
We shard large corpora into many cartridges and retrieve the right ones — 50,000 docs answer like 50.
The pipeline
Point Engram Smart CAG at SharePoint, Confluence, Drive, S3, or an upload. Incremental sync keeps it fresh.
We generate self-study Q&A and distil your documents into cartridges. A one-time cost.
Semantic search picks the right cartridges per question — by meaning, not keywords.
The open model answers from the cartridge — grounded, sourced, cheap to serve at scale.
Capabilities
Runs in your cloud with zero public ingress. Documents never leave your account.
Dense embeddings find the right cartridges by meaning — robust to paraphrase.
When documents change, only affected shards retrain — minutes, not a full rebuild.
Measured training cost, per-query cost, and break-even vs RAG for every corpus.
Every query is scoped to your own cartridges. Nothing crosses tenants.
Open-weight LLMs you run, served on the standard open-model serving stack (vLLM) — no per-token lock-in to a frontier vendor.
Engram Smart CAG deploys inside your own cloud account, with no public endpoint. Your documents never train a shared model and never leave your perimeter.
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