Evals
Most AI accuracy evaluations are a black box. They produce a single, opaque score that indicates whether your model is right or wrong — but not why, where, or how to fix it. Superficial replaces that opacity with deterministic, claim-level verification that makes every accuracy decision traceable, explainable, and directly usable for model improvement.
Across leading models from OpenAI, Anthropic, Grok, and Google, Superficial increased average claim-level accuracy from 78.56% to 95.16%.
Methodology
Superficial transforms model outputs into deterministic accuracy signals — decomposing responses into atomic claims, verifying each with symbolic logic, and generating structured correction, preference, and abstention data that integrate directly into your training and evaluation pipelines for measurable, re-audited accuracy gains.
Atomic claim extraction
Model outputs are decomposed into atomic factual claims—the smallest verifiable units of truth. This separates every individual assertion from model reasoning and responses.
Evidence grounding
Each claim is grounded against relevant evidence from trusted sources such as private knowledge bases, trusted rule sets, documentation, or even the public web.
Symbolic verification
Symbolic verification replaces statistics with logic. Instead of asking an LLM for a probabilistic score, Superficial classifies each claim by its logical pattern and applies a corresponding verification policy from our library of human-audited rules.
Fine-tuning
The signals generated by Superficial integrate into fine-tuning or post-training pipelines, enabling teams to systematically reduce hallucinations, improve calibration, and teach models when to abstain.
Re-test
After tuning is applied, new outputs can be re-evaluated through the workflow, delivering claim-level before-and-after metrics that quantify accuracy gains, reduced confident errors, and stronger abstention behaviour.
Superficial vs traditional approaches
Most evals stop at measurement. Human labelers are slow and inconsistent. Superficial delivers deterministic claim-level accuracy scoring and training inputs at machine speed.
| Traditional evals | Traditional labelers | Superficial | |
|---|---|---|---|
| Granularity | Whole outputs | Sentence/segment-level, subjective | Claim-level, deterministic |
| Speed | Automated, fast | Human-in-the-loop, slow | Machine-speed, instant |
| Consistency | Probabilistic | Subjective, variable | Deterministic, repeatable |
| Abstention | Ignored | Rarely captured | Explicit abstention signals |
| Output | Accuracy scores | Labels for training | Correction pairs, preferences, abstention inputs |
| Traceability | Limited | Human rationale notes | Source spans, rules fired |
| Cost | Low per run | High per dataset | Scales with usage, lower unit cost |
Who we help
From regulated industries to high-stakes applications, Superficial keeps your models accurate, your brand protected, and your model outputs fully traceable.
Labs
Turn evaluations into training leverage: claim-level breakdowns become correction pairs, preference signals, and abstention data you can feed directly into fine-tuning, making your models more accurate and reliable.
Enterprises
Win customer trust at scale: deliver audit-ready outputs with span-linked evidence and omission detection, backed by deterministic checks that give your clients and regulators confidence in every response.
Startups
Compete on accuracy: show measurable gains against baselines and ship models that outperform rivals, with deterministic labels and proof you can use to build credibility fast.
Regulated industries
Deploy with defensible compliance: deterministic audits and calibration signals that enforce abstention, delivering traceable proof and safer rollouts under regulatory scrutiny.
Get started with a free atomic accuracy audit
Superficial offers qualified first-time clients a complimentary atomic accuracy audit — a fast, zero-risk way to expose hidden accuracy gaps and see how deterministic verification transforms your model’s reliability. Request an audit below.