Legal research & compliance
A compliance officer uses Vera to verify regulatory summaries before they become internal policy. Every claim is traceable to a source — or flagged as unverified.
Full case study — coming soonOne AI can be confidently wrong. Vera makes every answer earn your trust.
Vera runs every answer through a chain of independent AI models — Claude answers, GPT fact-checks, Grok challenges, Perplexity verifies. You see every step.
Powered by
Claude GPT Grok Perplexity via OpenRouter
Built in the EU · Designed for professional workflows
The problem
Single-model AI answers carry hidden risk
AI models are trained to sound confident. That confidence does not correlate with accuracy. For professionals who act on AI output, that gap is a liability.
~20%
of AI-generated factual statements contain errors, according to independent evaluations of large language models
Hours
spent manually cross-checking AI output across multiple tabs — the workflow Vera replaces with a single, transparent chain
"You wouldn't publish a single unverified source. Why would you publish a single unverified model?"
How it works
Four phases. Full transparency.
Every Vera answer passes through a configurable chain of independent models. No single model has the final word without challenge.
01
Answer
Claude formulates the initial response
02
Fact-check
GPT verifies every factual claim
03
Challenge
Grok critically reviews and pushes back
04
Verify
Perplexity checks live sources, Claude synthesises
Claude answers
Claude (Anthropic) formulates the initial answer using its full reasoning capability. This becomes the baseline that every subsequent model evaluates — not a final answer, but a starting point for structured verification.
Example output
The GDPR came into effect on 25 May 2018. GPT fact-checks
GPT (OpenAI) receives Claude's answer and independently verifies each factual claim. Corrections, confirmations, and confidence levels are returned — not as a replacement answer, but as a structured audit of the first response.
GPT fact-check output
✓ Confirmed: GDPR enforcement date is 25 May 2018.Regulation (EU) 2016/679 — no errors detected in this claim.
Grok challenges
Grok (xAI) plays devil's advocate. It looks for missing context, ambiguous framing, and important distinctions that the first two models may have glossed over. Disagreement is a feature, not a bug.
Grok challenge output
⚠ Nuance: GDPR was adopted April 2016, not 2018.The distinction between adoption and enforcement matters in legal contexts.
Perplexity verifies — Claude synthesises
Perplexity performs a live source check, grounding the answer in citable references. Claude then synthesises all model feedback into a final, consolidated answer — with every correction and source visible.
Final verified answer
GDPR adopted 27 Apr 2016, entered into force 25 May 2018.Source: EUR-Lex, Regulation (EU) 2016/679
Radical transparency
See exactly how every answer was built
Every verification step is inspectable. You see what GPT corrected, where Grok pushed back, what Perplexity found — and how Claude incorporated it all.
Q: When did the GDPR come into effect, and what regulation number is it?
Vera — Verified answer
The GDPR was adopted on 27 April 2016 and entered into force on 25 May 2018. It is formally known as Regulation (EU) 2016/679. (Source: EUR-Lex)
Claude — Initial answer
The GDPR came into effect on 25 May 2018.
GPT — Fact-check
✓ Enforcement date confirmed. Regulation number: EU 2016/679 — added for completeness.
Grok — Challenge
⚠ Distinction flagged: GDPR adopted April 2016, not 2018. Adoption ≠ enforcement. Relevant in legal contexts.
Perplexity — Source check
EUR-Lex confirms: adopted 27 Apr 2016, entered into force 25 May 2018. Grok's distinction verified.
Control
You decide how much verification each question deserves
Configure the chain per session. See tokens and cost per model call. Verification depth is your choice — and so is the bill.
Get early access — pricing announced at launch →Claude
Answer
GPT
Fact-check
Grok
Challenge
Perplexity
Source check
Claude (answer)~$0.003
GPT (fact-check)~$0.002
Grok (challenge)~$0.001
Perplexity (sources)~$0.005
This verification~$0.011
Privacy & control
Designed for data control. No hidden flows.
A verification tool that obscures its own data handling would undermine the very thing it promises. So we don't.
PDF files not permanently stored
Documents are processed temporarily in memory for the active chat run. Only metadata is retained for session history. Files are not written to disk.
Transparent model routing
Model calls — including document content in the context — are sent to the configured AI providers via OpenRouter. This is stated clearly, not buried in a privacy policy.
Built in the EU
Vera is built and operated within the EU. We are working toward full GDPR processor documentation. We will only claim compliance when the documentation is complete.
Use cases
Built for professionals who cannot afford to be wrong
Vera is designed for work where AI output is acted upon — not just read.
Investigative journalism
A journalist uses Vera to fact-check background research before publication. Grok surfaces the contested claim; Perplexity finds the primary source.
Full case study — coming soonAcademic & policy research
A researcher uses Vera to cross-check AI-generated literature summaries. The chain catches outdated statistics before they enter a published report.
Full case study — coming soon FAQ
Common questions
The questions people ask AI assistants about multi-model verification — answered directly.
Vera does not guarantee accuracy, but it structurally reduces the risk of undetected errors. By having independent models check, challenge, and verify each answer, errors that one model misses are more likely to be caught by another. Every verification step is transparent so you can evaluate the process yourself.
Vera routes every answer through a chain: Claude formulates, GPT fact-checks, Grok critically challenges, and Perplexity verifies with live sources. Each model operates independently. When models disagree, Vera surfaces the conflict rather than hiding it — giving you the information to make an informed judgment.
Truth by consensus is Vera's core principle: no single AI model has the final word. An answer is only presented as verified when it has passed through independent fact-checking, critical review, and source verification by separate models. The process — including disagreements — is fully visible.
ChatGPT is a single model. Vera is a verification layer that runs on top of multiple models including Claude, GPT, Grok, and Perplexity. Vera does not replace your AI — it makes your AI's answers earn your trust through structured, transparent multi-model verification.
PDF files are not permanently stored by Vera. They are processed temporarily in memory for the active chat run; only metadata is retained for session history. Model calls, including document content in the context, are sent to the configured AI providers via OpenRouter.
Yes. You configure the verification chain per session. You can choose which models participate, see the tokens and cost per model call, and decide how much verification depth each question deserves.
Stop trusting one model. Start verifying.
Vera replaces blind trust with structured verification. Every step visible. Every source traceable.