Human.Readable
Human.Readable provides a software layer that makes opaque AI outputs reviewable, cited, and defensible for regulated industries like insurance claims, clinical data verification, and permit review. The system measures its own uncertainty, flags those moments for human review, and creates evidence trails for every judgment. It sits underneath existing workflows, so teams can deploy AI without rip-and-replace projects or loss of human accountability.
- Artificial Intelligence
- AI Agents
- Enterprise Software
- Government Technology
- Healthcare Technology
- Insurance Technology
- Regulatory & Compliance Technology
- Software Only
Funding
Founders
Product
Problem
Regulated teams in industries like insurance, healthcare, and government face a compliance dilemma: AI can accelerate routine document processing, but its outputs are often opaque and cannot withstand auditor, regulator, or court scrutiny. Without a complete evidence trail and human oversight, organizations cannot safely deploy AI on consequential judgments without risking legal and reputational exposure.
Solution
Human.Readable provides an engine that sits underneath an organization's existing systems picture, turning opaque AI outputs into reviewable, cited, and defensible decisions without requiring a rip-and-replace integration. Every document—whether a claim, clinical record, or permit application—runs through a standardized six-step loop: upload, automatic document-type matching via cosine-similarity, extraction, analysis, human review, and audit logging. The system explicitly measures its own uncertainty and flags those precise moments for human review, ensuring that a person is never removed from a consequential judgment. The resulting record is an evidence-backed audit trail that can be presented directly to auditors, regulators, or courts, proving both what the AI concluded and why, and that a human was accountable at each step.
Target Audience
Primary customers are compliance-heavy organizations in insurance, healthcare, and government sectors that process high volumes of documents and need to maintain human accountability, auditability, and regulatory defensibility when deploying AI.
Features
- Automatic document-type recognition using cosine-similarity comparison against previously seen document types, eliminating manual pre-selection
- Built-in uncertainty quantification that identifies and flags low-confidence outputs for mandatory human review
- Complete audit trail production for every step, enabling defensible records for auditors, regulators, and courts
- A layer architecture that integrates with existing workflow systems without requiring replacement projects or multi-year procurement cycles
- One engine adaptable across regulated verticals, including claims adjudication, clinical data verification, and permit review