Triplets is the reliability layer between your retrieval stack and your model. It answers only from verified data — and when the data can't support an answer, it tells you so instead of making one up.
The demo never hallucinates. Production always does. RAG works in the room, then it ships, retrieval comes back thin, and the model fills the gap with a confident, fluent, wrong answer.
One confident, wrong answer can undo months of user trust. The product that felt magical in the demo becomes the product nobody believes.
The support tickets, escalations, and churn that follow a wrong answer cost real money — long after the trust is already gone.
When the knowledge base can support an answer, you get it. When it can't, Triplets refuses, flags the gap, and repairs it — so the same failure never happens twice.
Triplets doesn't make your model smarter.
It makes it stop guessing.
Triplets is infrastructure. It lives inside your product, under your brand, next to the vector store or graph you already run.
We'll tell you that on day one — rather than sell you a bad fit.
* Design-partner stage results from internal holdout evaluations. Every number gets re-run on your own data during the proof of concept.
Pick a slice of your corpus and the questions your system gets wrong today. We run them against your current stack and against Triplets, side by side, and hand you the report. No integration work. No commitment — the report is yours either way.