POLYBRAIN.
Building a control plane.
Validation Run
We check the work before you see it.
Real Output
This is what a validation run looks like.
Actual output from validating a published research brief. Three models score quality. A fourth attacks it.
From a real run on March 29, 2026. The adversarial reviewer flagged vendor-sourced data as a bias risk. The brief passed at 80.4 after the finding was addressed.
The Pipeline
Three stages. Every artifact.
Awareness, validation, gate. What enters the pipeline either passes or does not ship.
Classify the artifact.
Polybrain identifies what is being validated: research paper, production plan, client site, agent, reveal, dashboard, or media. Each type has its own validator with targeted checks.
Run independent checks.
Research gets multi-model scoring from GPT-4o, Llama 3.3, and Grok 3, then an adversarial review that finds every flaw. Sites get HTTP and availability checks. Agents get identity verification and cross-contamination detection. Each validator runs checks specific to what it is reviewing.
Pass, warn, or block.
Every artifact must clear its validator. Research needs an 80+ composite score. Product deployments exit with a pass or fail signal. Critical failures block the deploy. Warnings get flagged for review.
By The Numbers
Real numbers from real runs.
Not projections. Not roadmap. What the system does today.
3
Independent models
GPT-4o, Llama 3.3, Grok 3. Different companies, different training data, different blind spots. That is the point.
6
Typed validators
Site, agent, reveal, dashboard, media, and shared component. Each with checks specific to what it validates.
3
Pipeline stages
Awareness, validation, gate. Every artifact is classified, checked, and either passes or does not ship.
$0.04
Average cost per run
Cheap enough to run on everything. Research briefs, production plans, client sites, agent behavior. No reason to skip it.
Mistakes We Caught
AI makes mistakes. We catch them first.
Every one of these errors was caught by the validation pipeline before anything reached a client.
A research brief cited vendor-sourced data as objective market research. The adversarial reviewer flagged the bias risk. The final version disclosed the source.
White-Label AI brief, March 2026
A report stated a tool had 3.6 million weekly users. Cross-validation with a second model found the real number was 33.8 million. Off by 10x. Caught before publication.
Agent Orchestration brief, March 2026
A market analysis left out a major competitor entirely. A second AI model flagged the gap. The final version included it with proper positioning.
Competitive Landscape brief, March 2026
Two sections of the same brief disagreed on a key market projection. The pipeline spotted the conflict and resolved it with verified sources.
Identified during Research Quality Audit
The Stack
Different models. Different companies. Different blind spots.
No single AI provider validates its own output. Independence is the architecture, not the afterthought.
GPT-4o
OpenAI
QUALITY + ADVERSARIAL + FEASIBILITY
Scores quality, then tears the work apart in a separate adversarial pass. For plans, evaluates real-world feasibility.
Llama 3.3
Groq
QUALITY + REVISION
Free inference via Groq. Scores the same quality dimensions, then strengthens weak areas flagged by the adversarial review.
Grok 3
xAI
QUALITY + FACT-CHECK
Most recent training data. Scores quality and independently fact-checks every claim against its knowledge.
Vision Models
Multi-provider
VISUAL AUDIT
GPT-4o Vision and Llama Vision score screenshots across 8 dimensions: visual polish, brand consistency, mobile readiness, and more.
Research
Published work behind the pipeline.
The methodology is documented, the claims are sourced, and we audit our own papers with the same system.
Cross-Model Consensus
Why different models catch different things. Cites Zheng et al. (NeurIPS 2023) on single-model bias and Verga et al. (2024) on panel-of-judges evaluation. The methodology behind the pipeline.
Read the paperStructural Role Separation
Four functional roles appear in biological, organizational, and AI systems. When role boundaries collapse, the same failure modes emerge. The theoretical framework behind Polybrain.
Read the paperResearch Quality Audit
We audited all 20 of our own papers against the evidence standards of Anthropic, Stripe, McKinsey, and a16z. 45% had zero clickable source links. Here is every gap and the fix.
Read the paperSee what validated work looks like.
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