LLM Hallucination Detection Services
Packages & Pricing.

LLM hallucination detection services that check answers against sources

LLM hallucination detection services verify that each claim in an AI answer is supported by the source documents before the user sees it. Omeiro scores answers against the retrieved context, blocks or flags unsupported statements and checks numbers that do not add up.

Higher tiers verify claim by claim and add a custom judge model.

LLM hallucination detection: deciding what to block and what to flag

Not every unsupported statement is equally risky. LLM hallucination detection lets you choose different responses: block a wrong dosage or price, flag a loosely worded summary, and let harmless pleasantries through. Before ordering, sort the answers your app gives by consequence, and define what counts as supported by the sources. Provide examples of past mistakes, because the detector is tuned against them and then tested on them again afterwards.

Detection depends on retrieval. If the right passage never reaches the model, an answer can be wrong and still look grounded in whatever was retrieved, so check retrieval quality alongside verification. LLM hallucination detection also carries a latency cost, since each answer is checked before delivery, so decide whether you prefer slower, safer replies or a flag after the fact. A custom judge model on Premium can be tuned to your domain vocabulary and checked against human labels.