Hallucination prevention
Catch hallucinations before they reach a user
Even frontier models are not 100% safe on hallucinations – and the cheaper the model, the higher the rate. A company shipping LLM features is one confidently-wrong answer away from a wrong number in a report, a fabricated fact to a customer, or a decision made on invented data. Usually they find out after it shipped.
The proxy attacks hallucination with a layered, cheapest-first strategy. The first pass is deterministic – it recomputes the model’s arithmetic, detects figures that drifted from the source, and catches directional contradictions like “up 12%” when the numbers went down – with no extra model call. Only when a claim needs judgment does it escalate to a fact-check judge, and only when it needs outside evidence does it reach for retrieval.
In FIX mode, safe repairs happen automatically – wrong arithmetic is corrected in place. In PREVENT mode the response is blocked with a 422 before your app ever shows it. Every finding is recorded in a per-request grounding report: the answer marked up with exactly what was caught and why.
Deterministic numeric grounding
Recomputes every arithmetic claim, matches every figure in the answer to the source, and flags directional / percentage contradictions. Zero extra tokens; it never hallucinates about hallucinations.
Fact & consistency judge
A guard model flags internal contradictions, claims false by common knowledge, and overconfident extrapolation. An honest “I don’t know” or a hedged estimate is never punished – only unhedged certainty without support.
RAG against your documents
Retrieves your ingested document chunks (pgvector) and verifies the answer is supported by them.
Web search & consilium
Checks claims against live web results, or puts the answer in front of one or more independent critic models – disagreement is a grounding signal.
Private data via MCP Coming soon
Verify a claim against a private source – e.g. a market-share figure only your own data can confirm.
How do you detect hallucinations without another LLM?
The first layer is deterministic: it recomputes arithmetic, compares every number in the answer against the source material, and checks stated directions against the underlying values. That class of hallucination – altered figures, botched math, contradicted trends – is caught with no model call at all.
Can hallucinations be fixed automatically?
Where a safe fix exists, yes. In FIX mode wrong arithmetic is repaired in place; findings with no single safe rewrite (contradictions, unsupported claims) are flagged and recorded. In PREVENT mode the response is blocked before your app receives it.
What does it cost per request?
The deterministic pass adds no model tokens. Judge and retrieval layers are opt-in, and every guard-model sub-call is priced into the request, so the dashboard shows the true cost of checking – measured, not estimated.
Does this let us run a cheaper model?
That is the core economics: the proxy watches the output, so you can route traffic to a cost-effective model and keep the quality bar. The guarantees come from the checks, not from the model tier.
Catch hallucinations on your data
Upload a document and watch the proxy catch altered figures, unsupported claims, and contradictions in real time. We are running a limited demo - sign up and we will get you in as soon as we can.