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Lenz
Independent, multi-model fact-checking API for AI workflows
211 followers
Independent, multi-model fact-checking API for AI workflows
211 followers
Lenz is an AI fact-checking API for products that cannot afford to hallucinate. It extracts verifiable claims from any text, then checks each one: searching independent sources, running multi-model debate, and routing through a review panel — returning a scored verdict with every source, argument, and step visible. Most AI tools give you one model's best guess from memory. Lenz ensures no single model's blind spots drive the conclusion. Available as API and MCP. Try it free at lenz.io/ph
This is the 2nd launch from Lenz. View more
Lenz
Launching today
Lenz is an AI fact-checking API for products that cannot afford to hallucinate. It extracts verifiable claims from any text, then checks each one: searching independent sources, running multi-model debate, and routing through a review panel — returning a scored verdict with every source, argument, and step visible. Most AI tools give you one model's best guess from memory. Lenz ensures no single model's blind spots drive the conclusion. Available as API and MCP. Try it free at lenz.io/ph
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Launch tags:Artificial Intelligence
Launch Team / Built With
Lenz
Kosta here, co-founder of Lenz. Many businesses ship AI-generated content to their customers. Some of those use cases could benefit from factual verification of that AI output. That's why we built Lenz, packaged it as an API/SDK, and made it available across multiple platforms (n8n, Zapier, MCP, CLI), so people can easily integrate it into their workflows. Lenz verdicts come with a full audit trail - sources, citations, reasoning, confidence.
How Lenz is different than just asking a model:
(1) separate evidence gathering step (with source ratings) that doesn't rely on the model's memory or retrieval capabilities
(2) multi-vendor, multi-model approach to address single-model biases
(3) multi-round adversarial debate to crystallize the strongest for/against arguments
(4) multi-model jury reviewing the evidence and the debates across multiple axes
Key API primitives: /extract - extracts the factual claims from a text; /assess - quick assessment of a claim; /verify - the full deep claim verification; /ask - follow-up post-verification questions.
We measured the level of disagreement between the individual frontier models: on 23% of real-world claims, they disagree significantly, which sets the floor of the error Lenz is built to address.
To try a Lenz verification via the UI: lenz.io/verify
More about the LLM disagreement research: lenz.io/research/llm-disagreement
To integrate Lenz: lenz.io/integrations
GitHub: github.com/lenzhq
Hope you find this useful. Let me know either way :)
@kostaj love the focus on visible reasoning. curious how it handles situations where online sources totally contradict each other?
Lenz
Hey @vikramp7470, we use the models to analyze the overall body of evidence collected during the research step of the pipeline. Before that, we rate each piece of evidence (source) for authority, recency, and relevance. During the final jury vote, one AI jury member is specifically instructed to act as a Source Auditor and reexamine various aspects of each source, including source quality and potential conflicts of interest related to the claim being analyzed. The findings are flagged in the final report under the panelist Source Auditor, and are taken into account during the final decision.
PicWish
@kostaj Integrating /assess right into a GitHub Action for pre release checks would be a game changer for sure. are you guys planning to make native Git hooks so we don't manually run the pipx command every time
+congrats lessgo
Lenz
Thanks,@mohsinproduct! Very interesting idea and much in line with our strategy to make Lenz easily accessible in all dev tools and environments.
FuseBase
Congrats on a launch, team @kostaj @vicky_dodeva @david19782 @pavel_j - are you a family, wow?
I have a question regarding the product as well. Could Lenz verify outputs from Deep Research in ChatGPT?
Lenz
@kate_ramakaieva we've got that question a lot 😄 Some are family members indeed and the rest is just a suspicious concentration of the same surname 😄
And yes, Lenz can verify outputs from ChatGPT Deep Research. The typical workflow would be:
1. Copy the Deep Research output text
2. Run it through /extract to pull out the verifiable claims
3. Send claims through /verify for the full 8-model pipeline verdict with sources
You can find more at lenz.io/docs/quickstart
Softorino 💻📲
Half the pages that rank for file format questions I work on are confidently wrong, and they're the most linked. How do your source ratings handle a topic where the crowd is wrong?
Lenz
@yelyzaveta_kibets We assess the authority of the sources too in an effort to find the "scientific consensus" - e.g., give priority to non-retracted published research papers, official gov sources, etc. over YouTube and Reddit posts, etc. That said, there's of course no guarantee that Lenz is always right, but in our experience, a well-orchestrated, dedicated multi-model fact-checking pipeline systematically outperforms any single model alone.
Jinna.ai
Congrats on the launch! Does your product provide the full chain of sources for manual verifiability? (not just direct links, but maybe logical facts if this -> then this -> hence this <source>)
Lenz
@nikitaeverywhere We provide the full weight-ordered list of sources and the exact citations, as well as the information for their analysis, including the analysis of logical correctness and fallacies. You can check out the full output of the verification process in some of the public examples in the library: lenz.io/library - the logical fallacies analysis is available for each claim analysis in section Reviewer 1: The Logic Examiner. You can also submit any claim at lenz.io/verify and see the result.
Lenz
@megha_t1 Thanks for trying it, Megha—and glad the text input worked smoothly! I’m part of the development team behind Lenz. For some YouTube videos, we don’t have permission to extract the transcript, which may have caused the issue. We’d like to investigate this further, so please feel free to send the video link to info@lenz.io, and we’ll look into exactly what happened.
Lenz
Hi, I'm Pavel, part of the Lenz team. I worked on the MCP server and the CLI amongst other things.
Quick note on why we believe this needs to exist: we gave the same 1,000 real claims from Lenz to 5 frontier models (Fable, GPT 5.6, Gemini 3.1 Pro, Sonar Deep Research, Grok 4.5), identical prompt, all with web search and thinking. All five agreed on only 37% of them, and on 23% the verdicts were 2+ steps apart on a 5-point scale. Surprisingly confidence was almost meaningless: 76% of all answers were self-rated at 9 or 10 out of 10. The figure below is that result. Each ring is a model, each spoke is a claim, grouped by agreement.
The two services I built:
- MCP server at lenz.io/mcp. To connect see lenz.io/integrations/mcp-server or just hand it to your agent.
- CLI: pipx install "lenz-io[cli]", then lenz verify "the Great Wall is visible from space"
Paper, data and prompts are open if you want to pick it apart: lenz.io/research/llm-disagreement/v1.1
Happy to answer anything. Let me know if you try the MCP.
Lenz
Hi, Vicky here 👋 the non-tech co-founder of Lenz
AI has genuinely changed what I can do on my own (from ops to marketing to building alongside an engineering team without being one). I’m one of those people it has really opened doors for.
But there’s another side to this. More and more companies rely on AI to produce the actual content their customers see: reports, articles, recommendations, research, support replies, ... And "someone will check it before it goes out" stops working at scale.
That’s the part of Lenz that really resonates with me. For editorial and production teams, that means letting verified claims through, sending uncertain ones for human review, and keeping the sources behind every decision.
AI will let us produce vastly more. Verification has to scale with it.
Very excited to have Lenz out here today and would love to hear your use cases.
@vicky_dodeva nice launch congrats🙌i think this will solve a massive bottleneck in automated research in agents
Lenz
@priya_kushwaha1 thanks Priya! This is exactly where we think verification will be critical. Appreciate the support!
Lenz
Thanks a lot @atul15