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Superficial

AI makes mistakes.
Superficial fixes them.

Superficial is the accuracy layer for AI, built to eliminate mistakes and make language models reliable for critical real-world applications.

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A path to reliable AI

Superficial — A path to reliable AI

Company · 5 min read
Introducing the Superficial API

Introducing the Superficial API

Product · 5 min read

Benchmarks

Superficial achieves 100% factual accuracy on models from OpenAI, Google, xAI, and Anthropic — as measured on Google DeepMind’s FACTS benchmark.

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Models are evaluated using Google DeepMind’s FACTS methodology. When FACTS marks a response as inaccurate, we one-shot enhance it using Superficial’s audit results, then re-score it with FACTS to measure the independent accuracy gain. Real-world results may vary with source availability and domain complexity.

Accuracy at every stage

Superficial ensures your models are accurate and auditable from development and pre-release right through to production.

Development

Superficial integrates directly into your development workflow, empowering you to build more accurate models, faster.

Run claim-level accuracy audits on your models as you build.
Understand why your model is wrong and surgically fine-tune it on the fly.
Audit, fine-tune, and re-audit in a seamless loop, and embed verification directly into your CI/CD pipeline.

Pre-Release

Superficial provides the independent, auditable proof you need to deploy with certainty.

Run your final evaluation dataset through Superficial to generate a definitive benchmark of your model's performance against your specific accuracy and safety standards.
Get the deterministic, auditable evidence that your compliance, legal, and risk teams require for approval.
Prove to internal stakeholders and external regulators that your model has met rigorous pre-deployment standards.

Production

A model's accuracy is not static. Superficial provides the ongoing monitoring you need to maintain trust and performance in the real world.

Sample live production outputs and run them through Superficial to catch regressions, identify new failure modes, and detect performance drift before they impact users.
Automatically build an unbroken audit trail of your model's real-world accuracy, ensuring you are always prepared for regulatory review.
Capture and label production failures and turn them into a high-quality dataset to improve your model's accuracy over time.

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Your model has already made mistakes

Superficial finds and fixes them — before they cause problems