Your LLM hallucinates. Your users don't know. VEROQ fixes this with one line of code. result = shield(llm_response) Extracts claims from any LLM output, verifies each against live evidence, and returns a trust score with corrections. Works with OpenAI, Anthropic, Llama, — any model. Every claim gets an evidence chain and a permanent verification receipt. Self-hosted option for enterprise (Docker, your own LLM, air-gapped). Free tier: 1,000 credits/m pip install veroq / npm install @veroq/sdk

Wispr Flow: Dictation That Works Everywhere — Stop typing. Start speaking. 4x faster.

Stop typing. Start speaking. 4x faster.

Hey Product Hunt! I'm Johnny, builder of VEROQ. I kept shipping AI features where I had no idea if the output was actually correct. Every LLM confidently generates wrong numbers, fake quotes, and outdated facts. The only option was "hope for the best" or build a massive fact-checking pipeline. So I built shield() — one function call that extracts claims, checks each against live evidence, and tells you exactly what's wrong: from veroq import shield result = shield("NVIDIA reported $22B in Q4 revenue") # trust_score: 0.73 # corrections: [{"claim": "...", "correction": "actual was $22.1B"}] What makes it different from asking GPT "is this true?": every verification hits real sources (web search, financial data, public records). No LLM-judging-LLM. For enterprise teams that can't send data externally, we just shipped a self-hosted Docker version that runs inside your VPC with your own LLM. Free tier gives you 1,000 credits/month. Would love feedback on the API design — what verification use cases would be most useful for your stack?

Wispr Flow: Dictation That Works Everywhere — Stop typing. Start speaking. 4x faster.

Stop typing. Start speaking. 4x faster.

VEROQ — Stop Shipping Hallucinations

One line of code. Every LLM output fact-checked

One line of code. Every LLM output fact-checked