AI Fightclub. You Get the Truth.
Multiple engines debate your question, challenge each other's logic, and prove their answers—with receipts.
One question. Multiple models propose, attack, and defend—backed by evidence—until only verified answers remain standing.
Ask Your First Question →Every AI Sounds Confident. Few Are Correct.
Large language models produce fluent, persuasive text—even when they're wrong. They skip nuance. Invent sources. Double down with conviction.
The Short Version
Make Them Argue
We run multiple best-in-class models in parallel. They propose answers, poke holes in each other's logic, and compete to out-prove their claims.
Check the Receipts
Every claim gets tested against sources. Weak citations, hand-waving, or invented facts are flagged, challenged, and suppressed.
Deliver What Holds Up
The system surfaces the most defensible answer—clearly cited—with a confidence assessment, known gaps, and alternative perspectives when they exist.
From Question to Verified Answer
Five phases. No shortcuts.
Question Parsed
What happens: We clarify scope, normalize terms, and surface implicit assumptions so every engine solves the same problem.
Why it matters: Misaligned questions produce misaligned answers.
Multi-Model Debate
What happens: Independent models draft answers from different angles—logical reasoning, factual recall, structural analysis. Then they cross-examine each other: "Where's your source?" "That contradicts X." "You're assuming Y."
Why it matters: Peer pressure for AIs. Bad reasoning gets called out immediately.
Evidence Checked
What happens: Claims face source validation. Retrieval systems pull documentation, benchmark data, academic papers. Unsupported assertions are downgraded or removed.
Why it matters: Confidence without evidence is just noise.
Consensus Forged
What happens: Models revise under scrutiny—strengthening citations, removing fluff, correcting errors. We synthesize convergent reasoning into a single defensible narrative. Disagreements? We show both sides with supporting evidence.
Why it matters: You get the strongest answer, not the loudest.
You Decide
What happens: You get a report showing the evidence, reasoning, dissent, and any integrity flags. The full audit trail is there if you need it.
Why it matters: You see what went into the answer. No mystery box.
Translation: We knock their heads together until the truth emerges—then we make it prove itself.
Real Question. Real Process.
Step 1 - Initial Proposals (abbreviated)
- Engine 1: "Simplify the form—remove optional fields."
- Engine 2: "Add trust badges and security indicators."
- Engine 3: "Enable guest checkout, skip forced account creation."
Step 2 - Cross-Examination (1 key critique shown)
- Engine 3 to Engine 1: "You say 'simplify' but which fields? Removing the wrong ones increases fraud or delivery failures."
- Engine 1 (revised): "Remove marketing opt-ins and secondary phone numbers. Keep address validation for delivery."
Step 3 - Evidence Retrieved
- Baymard Institute study: form field impact on completion rates
- E-commerce case studies: guest checkout conversion lifts
- UX research: trust signal effectiveness on mobile
Final Output: Enable guest checkout (18-23% conversion lift in studies). Reduce form fields to essentials: email, shipping address, payment. Add visible security badges near payment input. Keep address validation to prevent delivery issues.
Confidence Assessment: High support for guest checkout and field reduction; moderate evidence on specific badge placement.
Dissent: One model argued for account creation to boost LTV, but evidence shows it increases abandonment.
Sources: Baymard Institute E-commerce case studies UX research papers
One AI vs. Many
Single AI
- One perspective, no checks
- Confidently wrong
- "Trust me" citations
- Black box process
UsureRU
- Adversarial verification
- Confidence calibrated to evidence
- Every claim sourced and scored
- Full audit trail + reasoning chain
What You Get
Verified Answer
Clean synthesis with working citations. No guesswork.
Alternatives & Trade-offs
When there's no single "best," we show viable options and when to choose each.
Confidence Assessment
Plain-English explanation of where evidence is strong, where it's thin, and what we're certain about vs. uncertain.
Next Steps
Request deeper analysis, generate an action plan, export an executive summary, or adjust constraints and re-run.
Guardrails That Keep Us Honest
Source Awareness
The system clearly labels speculation vs. supported fact. If a claim lacks evidence, we say so.
Attribution Integrity
Citations stay attached to claims throughout the process. No orphaned sources.
Privacy First
Your prompts and outputs stay yours. Sensitive data can be masked or redacted. No training on your queries.
No Guru Mode
If evidence is thin or expertise is required, we tell you—and suggest how to get better info (e.g., "This requires domain expertise we can't verify").
When It Shines vs. When It Won't
Truth in advertising
Great For:
- ✅ Research synthesis & technical deep-dives
- ✅ Strategy options & policy comparisons
- ✅ Fact-checking, code reviews, market scans
- ✅ SOP drafts, documentation, competitive analysis
Not Magic For:
- ⚠️ Brand-new proprietary data no one's seen
- ⚠️ Legal advice, medical diagnosis
- ❌ Anything requiring licensed professional judgment or authority you haven't granted
FAQ
Used by researchers, strategists, and engineering teams who need answers they can defend
Stop Trusting. Start Verifying.
Ask a hard question. Watch the engines argue. Keep the full audit trail.
Ask Your First Question →