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An API that catches what your LLM confidently got wrong

Details

External ID
47700143
Source
HN
Company
—
Product
An API that catches what your LLM confidently got wrong
Website domain
factagora.com
Launched
April 9, 2026
Cohort
—
Upvotes
5
Upvotes percentile
0.11182519280205655
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Enrichment

Theme
Vertical
Horizontal
Function
Observability & eval
Audience
Developer
AI stance
AI feature
Project type
Commercial product
Normalized one-liner
detect when llms make confident mistakes
Manually corrected
False

Could you build this?

Partial Wrapping an LLM call to cross-check another LLM output is trivial, but building a reliable, high-precision hallucination and error detection API requires specialized evaluation models, fact-checking datasets, and citation verification engines.

What it would actually take: A production hallucination detection service uses fine-tuned NLI (Natural Language Inference) models, external knowledge-graph retrieval, and calibrated token log-probability / entropy analysis. The hard part is providing deterministic, low-latency verification that doesn't simply suffer from the same hallucination pitfalls as the generation model it audits.

Discussion

2 comments analyzed.

Concerns raised: Performance in real production use cases unclear, Whether it actually catches hallucinations effectively

Competitors

Other products that read as similar to this one — 237 launches clear the similarity bar, closest 8 shown.

Attention rank: #208 of 238 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 162 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a observability & eval tool for Media & entertainment yet.