Relata stores arbitrary information, discovers evidence-backed relationships between items, and retrieves related knowledge with an explanation of why each result was returned.
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Most systems collapse "these documents read alike", "these documents are connected" and "this document answers your question" into a single relevance figure. They are three different claims with three different meanings, and Relata keeps them apart in storage, in the API and on screen.
How alike are these two pieces of text?
Cosine distance between embeddings. Nothing more. Two incident reports written by the same team in the same house style will score highly and describe entirely different events.
How well evidenced is a link between two items?
A weighted sum of named signals, each stored with its own value and weight, so the arithmetic is always inspectable. Not a probability.
How well does this item answer this question?
Several retrieval paths fused by rank. Comparable within one result set, not across queries — and it says so.
This is the distinction the whole design exists to preserve. Two documents that share wording are not thereby connected; two documents that mention the same company are not thereby about the same thing. Relata records what it actually found:
| Signal | Value | Strength | Weight |
|---|---|---|---|
| claim_contradiction | cost: 42,000 and 51,500 | 1.00 | 0.22 |
| shared_entity | Example Ltd | 1.00 | 0.25 |
| semantic_similarity | 0.89 cosine | 0.76 | 0.30 |
Both figures are named. Relata does not decide which is correct.
A model may classify a relationship's type or describe a connection. Its opinion is one signal, weighted 0.15 — below the threshold at which a relationship is stored at all. A model cannot create a connection on its own, however confident it is. That is a property of the arithmetic, not a promise, and there is a test asserting it.
| Signal | Weight | What it means |
|---|---|---|
{{.Signal}} |
{{printf "%.2f" .Weight}} | {{.Means}} |
Relationship scores are heuristic strengths, not calibrated probabilities — the weights were chosen by judgement, not fitted to labelled data. A 0.7 does not mean "70% likely to be real". Deterministic extraction is deliberately narrow. There is no OCR. Prompt-injection defences make injection much harder and bound its effect; they are not a proof.
The full account is on the how-it-works page, including what is verified and what is merely intended.