The Engine
From question to accountable verdict.
Most AI gives you one answer. Syntheric gives you five models arguing, a structured cross-examination, wrong question detection, landmines, failure modes, and one confidence-scored verdict. The disagreement is the signal.
Up to 5
Concurrent Models
10+
AI Providers
R.U.D.E.
Core Engine
Will AI create more jobs than it eliminates over the next 20 years?
Wrong Question Detected
Which specific job categories, skill sets, and income levels are most exposed to AI displacement — and what transition timeline should individuals and institutions actually plan for?
The council answered your question — but this is the question you should have asked.
Probably yes — but the distribution is the crisis, not the net number. Historical technology transitions created more jobs than they eliminated in aggregate, but the new jobs appeared in different geographies, industries, and skill bands than the eliminated ones. AI displacement will likely follow the same pattern: net job creation over 20 years, with severe concentrated harm in the 5–10 year transition window for workers in routine cognitive tasks.
Assumptions: No AGI-level capability shift occurs within the window. Policy response (retraining, social support) follows historical norms. Productivity gains are partially reinvested in labor-intensive sectors.
🔴 Landmines Detected
• All three models treated "jobs" as a homogeneous unit. The real variable is wage level — AI disproportionately replaces mid-wage cognitive work while creating high-wage technical roles and low-wage service roles, accelerating inequality regardless of net job count.
• The 20-year horizon obscures the critical 3–7 year window where displacement peaks before new job categories mature. Most individuals and institutions are not planning for that gap.
If This Verdict Is Wrong
• This verdict fails if AI capability reaches a threshold where it can perform novel creative and strategic work — at which point historical technology analogies break down entirely and net job creation assumptions no longer hold.
3 models argued · 1 verdict · Wrong question caught · Landmines surfaced
The pipeline
Five layers. No shortcuts.
Any input. Any format.
A question, document, image, or code file enters Syntheric. Drop a PDF contract, a screenshot, a Python file, or type a question. One prompt. Every selected model sees the same thing.
Independent reasoning — no herd behavior.
Up to 5 frontier models answer before seeing each other's conclusions. Claude, GPT, Grok, Gemini, DeepSeek — reasoning in parallel, independently. No cross-contamination. No consensus before the work starts.
Cross-examination under structured pressure.
Reasoning Under Disagreement Evaluation. Every answer is challenged by competing models. Assumptions tested. Contradictions surfaced. Evidence gaps flagged. The model that survives scrutiny wins — not the one that sounded most confident.
Confidence scored from reasoning quality.
JUDGE calibrates confidence from RUDE's scores — not from token probability or self-reported certainty. Hallucination risk, dissent level, evidence strength, and reasoning quality all factor in.
One defended answer. Full traceability.
A confidence-scored conclusion with landmines detected, failure modes listed, and wrong question flagged if you asked the wrong one. Every verdict is auditable — which models agreed, what dissent was preserved, what risk was measured.
RUDE Engine
Models answer.
RUDE contests.
The model that sounds most confident is not always right. RUDE stress-tests every answer, measures disagreement, preserves dissent, and scores confidence from reasoning quality — not self-reported certainty.
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 tested before it becomes a verdict.
What makes Syntheric different
Nobody else does this.
Wrong Question Detection
After the council delivers a verdict, Syntheric tells you if you asked the wrong question — and rewrites it. No other AI product does this. Advisors charge thousands for this insight.
Landmine Detection
Critical gaps the council missed that you absolutely need to know. Not caveats — specific, named risks the models collectively overlooked.
Decision Insurance
After every verdict: 'If this verdict is wrong, here's exactly why.' Specific failure modes tied to the conclusion. How decisions actually get burned.
Challenge My Verdict
One click. The council reruns as devil's advocates, attacking the winning answer. Confidence drops from 88% to 41%? Now you know where the real risk is.
Syntheric Lens
Drop any image. Four vision-capable models analyze it simultaneously, disagree about what they see, and deliver a verdict. Charts, contracts, screenshots, photos — the council sees what you might miss.
Code Review & Fix
Drop a code file. The council finds bugs, security issues, and performance problems independently. Then hit 'Fix This' — each model produces a corrected version. Approve the winner and download.
Built for
High-stakes decisions deserve structured deliberation.
Research & Analysis
Five models read the same source and disagree. Wrong Question Detection rewrites your research question. Landmines surface what the literature missed. You get a verdict, not a summary.
Software Engineering
Drop your codebase. The council finds bugs independently, produces fixed versions, and lets you approve the best fix before downloading. Senior engineering review in seconds.
Strategy & Investment
Drop your pitch deck or deal memo. The council argues about it. Decision Insurance tells you how the verdict fails. Wrong Question Detection tells you what you should have been asking.
Legal & Contract Review
Upload a contract. Multiple models find the landmines independently. Dissent shows where interpretation diverges. Full traceability for compliance and audit.
Document Intelligence
Drop any PDF. Auto-prompt selects the right analysis mode. Find What Matters, Red Flags, Key Risks, Opportunities — one click, no prompt engineering required.
The opportunity
The next layer of value sits above the models.
Foundation models are commoditizing. The durable infrastructure layer is not generating answers — it is measuring reasoning quality, exposing uncertainty, and determining when AI should be trusted. Syntheric owns that layer.
Model Layer
Vendor agnostic
Syntheric orchestrates multiple providers. No single lab controls the verdict. When a better model ships, it joins the council.
Trust Layer
Disagreement first
The platform is designed around reasoning quality, disagreement, confidence, and risk — not speed or fluency.
Decision Layer
Accountable verdicts
Every output is a defended answer that can be trusted, challenged, or audited. Wrong questions flagged. Landmines surfaced.