Every team tries the same four fixes before they call us. Here's why each one fails — and what it actually takes to solve this.
A prompt is a request, not a constraint. The model has no ground truth to check itself against, and instruction-following is probabilistic — it slips on edge cases, long contexts, and every model update. Hallucination is the generation mechanism working as designed. It is not the model disobeying you.
Models are badly calibrated. Stated confidence doesn't track correctness, so the model can't reliably know what it doesn't know. Tune it toward caution and it refuses questions your data can answer. Tune it toward coverage and the fabrication comes back. You're turning a dial you can't see.
Training lowers the error rate. It can't set a floor of zero. And behavior baked into weights can't cite where an answer came from, doesn't update when your data changes tomorrow, and regresses every time you swap or upgrade the model.
Deterministic decoding makes the output repeatable, not correct. A zero-temperature model gives you the same wrong answer every time, with the same fluent confidence. Consistency and accuracy are different things.
Hallucination is an architecture problem, not an obedience problem. Generation cannot police itself. Correctness has to be enforced by a system that sits outside the model and knows what is verified, what is not, and where the line is.
Most RAG retrieves some chunks and hopes the model behaves. Triplets treats answering as a hierarchy of guarantees, and only generates when generation is the right tool.
Exact facts answered from structured, verified values. No generation, no room to improvise — used whenever the data allows it.
Questions that span entities and documents are answered by walking verified relationships: which policy covers which clause, which drug interacts with which order.
Open questions go to constrained generation. The model reasons over retrieved evidence but stays bound to it. Verified facts override the model — never the reverse.
When none of the three layers can answer from verified data, Triplets declines, tells the user why, and logs the gap. A refusal is recoverable. A fabrication is not.
Most systems throw failures away. Triplets treats every failure as fuel.
Every refusal, partial answer, and failed eval is captured as a signal — not noise.
The failure is diagnosed by type: missing data, broken relationship, conflicting sources, or retrieval miss.
The knowledge base is corrected at the source, so the same class of question answers correctly next time.
A memory ledger records what failed, what was fixed, and why. Accuracy compounds instead of resetting.
Triplets re-ingests your corpus and models it into its own structured stores of entities, relationships, and verified values. Then it governs generation at query time using that model.
That makes it substrate-agnostic: it runs alongside whatever vector database, graph, or pipeline you already have. Your stack stays where it is — Triplets governs how its output becomes an answer.
What it is not is content-agnostic. It's built for entity-rich, fact-verifiable data — and it's honest about where that ends.
re-ingest once · govern at query time
Bring the questions your AI gets wrong today. We run them against your stack and against Triplets, side by side, and hand you the before-and-after report. No integration work. No commitment.