AI makes mistakes.
Superficial fixes them.
Superficial is the accuracy layer for AI, built to eliminate mistakes at machine speed and make large language models reliable for real-world applications.
Benchmarks
Superficial achieves 100% factual accuracy on models from OpenAI, Google, xAI, and Anthropic — as measured on Google DeepMind’s FACTS benchmark.
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.
Our models
Superficial's neurosymbolic models are purpose-built to extract, ground, and deterministically verify AI generated content — enabling traceable, high-accuracy outputs at machine speed.
Flash 1.0
Our fastest, most affordable model for lower latency, lower stakes use cases.
Pro 1.0
Our balanced model with individual claim processing suitable for most applications
Ultra 1.0
Our most expansive grounding effort, for high-precision, high-stakes uses.
Your AI has already made mistakes
Superficial finds and fixes them — before they cause problems