Use cases

Built for developers shipping AI in production.

Six real applications where single-call hallucination detection changes what's possible.

RAG hallucination firewall

Score every LLM response before showing it to users. Block or flag outputs automatically. Route uncertain responses through additional verification.

"Our customer support bot answered 12,000 questions last month. We used AgnosLogic to flag 430 as high-risk — 89% of those flagged were genuinely hallucinated."
How: POST every LLM output to /v1/score before returning. Show "verified" directly, route "uncertain" through retrieval, block or rewrite "flagged".

Research assistant with confidence

Generate an answer and tell the user how confident the model is. Route uncertain answers to web search. Deliver verified answers directly.

"Question: What was the name of the first satellite? Answer: Sputnik 1, launched October 4, 1957. [Verified]"
How: Use /v1/ask to get answer + verdict in one call. Display the verdict next to the answer. Fall back to search for "flagged" or "uncertain" results.

Legal and compliance review

Flag uncertain claims in contracts, regulatory filings, and compliance documents. AI-generated summaries often contain fabricated citations or altered figures.

"Our paralegal team runs all AI-drafted summaries through AgnosLogic. Caught three fabricated case citations last quarter."
How: Split documents into sentences, score each via /v1/score, highlight flagged spans in the UI for human review.

Code review agents

When an AI explains a bug or suggests a fix, score the explanation. High-risk explanations often contain made-up API methods or invented syntax.

"The AI suggested using pandas.to_blockchain() to export data. [Flagged] — that method doesn't exist."
How: Score AI-generated code explanations. High-risk outputs get auto-verified against documentation or flagged for human review.

Medical and scientific writing

Fabricated drug interactions, invented research citations, and made-up clinical trial data can cause real harm. Score AI-generated medical content before publication.

"We score every AI-drafted patient education document. High-risk sections trigger expert review."
How: Combine /v1/score with domain expert review for all "flagged" content. Never ship unverified content for high-stakes domains.

Evaluation and benchmarking

Comparing two LLMs? Run their outputs through /v1/compare to see which produces more trustworthy responses on your specific prompts.

"We ran 500 questions through Qwen-72B and our fine-tuned model. /v1/compare showed our model had 23% fewer flagged responses."
How: Use /v1/compare for pairwise evaluations. Aggregate verdicts across your test set to compare models objectively.

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