How It Works
From question to
accountable verdict.
Syntheric turns multi-model disagreement into a structured reasoning process: independent answers, cross-examination, evidence review, and one traceable verdict.
Up to 5
Concurrent Models
7
AI Providers
JURY
Core Engine
JUDGE
Final Layer
The pipeline
Five layers. No shortcuts.
User prompt
A problem, research task, analysis request, or decision enters Syntheric. One question. Every model sees the same thing.
Independent reasoning
Up to 5 frontier models — OpenAI, Claude, Grok, DeepSeek, Mistral — answer before seeing each other. No cross-contamination. No herd behavior.
Cross-examination
Reasoning Under Disagreement Evaluation. Every answer is anonymously challenged by competing models. Assumptions tested. Contradictions surfaced. Evidence gaps flagged.
Confidence calibration
R.U.D.E. has already scored reasoning quality, evidence strength, hallucination risk, and dissent. JUDGE calibrates confidence from those scores and produces the final weighted verdict.
One defended answer
A confidence-scored conclusion with full traceability — which models agreed, what dissent was preserved, what risk was measured. Auditable by design.
RUDE Engine
Models answer.
RUDE contests.
Capability alone does not create trust. RUDE stress-tests reasoning, measures disagreement, preserves dissent, and scores confidence before the verdict is delivered.
Claim Extraction
INPUTRUDE breaks raw model responses into testable claims instead of treating answers as finished truth.
Disagreement Testing
CHALLENGECompeting conclusions are forced through contradiction checks, weak-logic detection, and missing-evidence review.
Confidence Calibration
SCOREAgreement, evidence strength, dissent quality, and hallucination risk are measured before the verdict is trusted.
Verdict Generation
OUTPUTSyntheric delivers an auditable conclusion with confidence, risk, and transparent dissent attached.
What RUDE checks
Every claim is tested before it becomes a verdict.
Built for
High-stakes decisions deserve structured deliberation.
Research & Analysis
Compare reasoning across models, preserve dissent, and reduce blind trust in a single generated answer before it becomes a conclusion.
Software Engineering
Pressure-test implementation plans, surface contradictions, catch hallucinated APIs, and improve confidence before code ships.
Strategy & Investment
Turn competing perspectives into a clearer decision signal. Know what the models disagree on before you commit.
Investor Thesis
The next layer of value
sits above the models.
Foundation models will continue to improve and commoditize. The durable infrastructure layer is not simply generating answers. It is measuring reasoning quality, exposing uncertainty, and determining when AI should be trusted.
Model Layer
Vendor agnostic
Syntheric orchestrates multiple model providers instead of depending on one frontier lab.
Trust Layer
Disagreement first
The platform is designed around reasoning quality, disagreement, confidence, and risk signals — not speed.
Decision Layer
Accountable verdicts
The output is a defended answer that can be trusted, challenged, or audited.