Products

Better RAG: a truthful recall layer

Most “RAG” stacks stop at chunks in a prompt. Answers drift, provenance is fuzzy, and the model still fills gaps from weights. Better RAG means a governed memory plane: your facts as versioned nodes and embeddings, injected at inference—with scope, lineage, and checkpoints when your corpus changes.

Try Better RAG — hands-on demo →

What “better” changes

• Stable recall, not vibes

  Retrieval is tied to explicit workspace memory—not opaque re-ranking that changes run to run without a record.

• Provenance by default

  Claims trace to what was ingested, not just “similar text.” That is the bar for review, compliance, and customer trust.

• Same LLM vendor

  Amnesis does not replace your model provider. It sits beside any LLM you run and feeds it governed context.

How Amnesis delivers Better RAG

You ingest documents and structured sources into a workspace-scoped plane. The system builds nodes with canonical bodies and projections suitable for search and injection. At chat or query time, recalled material is scoped (workspace, checkpoint policy as you enable it) so “what the system knew” is answerable afterward.

Optional flows such as reflection and checkpoints (per product roadmap) make semantic state and versioning explicit—so Better RAG matures into a full memory operating model, not a one-off pipeline.

Who it is for

Teams that have outgrown demo RAG: regulated industries, internal copilots, customer support, and any workflow where “the model said so” is not an acceptable audit trail. See also industry solutions and other Amnesis use cases.

Try it

Use the guided demo page for step-by-step flow, or open Test a Better RAG (live UI).

Try demo Test a Better RAG How it works Security