AI makes mistakes.
Superficial fixes them.
Superficial delivers deterministic accuracy evals that provide claim-level visibility into model performance and training data to turn mistakes into compounding capability gains.
Deterministic accuracy for every leading model
Stop guessing. Start seeing.
Today's accuracy evals are a black box, producing subjective, response-level labels that mask errors and give false confidence in model accuracy.
Superficial moves beyond subjective labelling by decomposing model outputs into atomic claims and applying symbolic rules to deterministically verify every individual statement a model makes.
As a result, Superficial identifies up to 20x more mistakes across major models than LLM-as-judge techniques.
(LLM-as-judge)
Comparison shows % responses identified as inaccurate by Google DeepMind FACTS and Superficial using FACTS dataset examples.
Go from seeing to fixing
Finding errors isn’t enough. Superficial closes the loop — from errors to fixes — automatically.
For every inaccuracy, Superficial generates a verified correction, pinpoints the root cause, and classifies the reasoning flaw. The result: models that self-correct, fine-tune faster, and converge on proof, not probability.
In benchmarking, Superficial increased average claim-level accuracy from 78.56% to 95.16% across leading models.
From accuracy to capability
Accuracy isn’t the end point — it’s the foundation for capability.
Superficial turns deterministic accuracy checks into capability gains through a policy-instructed upgrade loop, eliminating the need for slow, expensive manual data labelling.
Expert-defined policies set your standard. Every failed check exposes a capability gap and every verified correction becomes a precise, teachable lesson tied to that policy.
The outcome: fixes become upgrades. Your model doesn’t just avoid mistakes — it gains the expert capability you define that compounds with every run.
Optimise accuracy at every stage
Superficial ensures models are accurate and traceable from development to production with its automated find <> fix loop.
Development
Superficial integrates directly into your development workflow, empowering you to build more accurate models, faster.
Pre-Release
Superficial provides the independent, auditable proof you need to deploy with certainty.
Production
A model's accuracy is not static. Superficial provides the ongoing monitoring you need to maintain trust and performance in the real world.
Who we help
From regulated industries to high-stakes applications, Superficial provides the logical proof and actionable data needed to de-risk, fine-tune, and safeguard mission-critical AI.
AI Engineering & Labs
Stop relying on slow, expensive manual labeling.
Have experts write custom policies and let Superficial deterministically audit against them to generate precise corrections and remediation heuristics to fix errors and align your models at machine speed.
Risk & Compliance Teams
Move from a black box to an open book.
Superficial satisfies the accuracy and traceability standards required for deploying AI in regulated environments. Our platform provides audit-ready transparency to show why your model produces specific outputs — and whether those outputs are correct.
Enterprises & AI Startups
Deploy, Monitor, and Continuously Improve.
Superficial provides the verifiable assurance to de-risk your launch by catching the errors other evals miss. In production, our platform enables a continuous find-fix loop—monitoring live outputs, catching errors, and generating new training data to ensure your model's accuracy is always improving.
See Superficial in action
Our audit of 100 GPT-5 responses uncovered 146 mistakes that LLM-judges missed — complete with root-cause analysis and actionable training data to turn them into new capability.