RAG & Knowledge Retrieval

Give retrieval a structure you can govern

Dataplane moves your rules out of the text and into an explicit model, where relationships, access, and meaning are enforced rather than left to chance.

The Problem

When retrieval misses, you can't fix it by writing more documentation.

When an answer comes back wrong, the only lever most teams have is prose, so they pile on more context, spell out exceptions, and hand-tune content for specific questions. None of it holds, because nothing enforces it, and retrieval still surfaces whatever reads as closest.

Prose is your only knob. When retrieval misses, the one thing you can edit is the text, so every rule ends up buried in a document.

Documents can't enforce. A 'MUST NOT' written into a knowledge file is just more content to retrieve, not a constraint the system honors.

Every fix is a one-off. Hand-tuning content for one question breaks the moment the docs or the questions change.

Dataplane Approach

From rules buried in prose to a model retrieval has to follow.

Model the knowledge

Capture your entities, relationships, and the access and ambiguity rules retrieval must respect in a governed ontology, not buried in documents.

model · relationships · bindings

Enforce through the harness

The graph and harness decide what each query can reach and how, so your rules are constraints the system honors, not text it might surface.

graph · access · enforce

Capture & refine

Log what retrieval returns, then extend the model through abstraction and inheritance, so one fix compounds across every team and agent.

observe · feedback · refine

Built on:Data Ontology Data Quality Data Enrichment Data Lineage

Outcomes

Where You Feel It

An explicit, enforced model changes what retrieval will and won't do.

Accuracy

Rules the system enforces catch what more documentation never could.

Cost

Scoped retrieval pulls only what a query needs, so you stop paying to process the rest.

Manual effort

Rules live in one model instead of docs you endlessly hand-tune for each new miss.

Reuse

Model the knowledge once and it compounds across every team, agent, and pipeline.

Benchmark retrieval on an explicit model.

We'll model a slice of your knowledge, move the rules out of your documents and into the graph, then run retrieval both ways, so you can see the lift in accuracy and control for yourself.