AI product · Evidence integrity
ReportVerity: Evidence-Backed Reporting
An evidence-aware reporting workspace that keeps supported facts, user assertions, AI synthesis, missing evidence, and human judgment visibly distinct.
The problem
Professional reports often combine source material, author knowledge, interpretation, and missing information into prose that looks equally certain. Generative AI can accelerate drafting, but it can also make those boundaries harder to see.
ReportVerity explores a more accountable model: every consequential claim should show what supports it, where uncertainty remains, and when a person must decide what is appropriate to publish.
The product direction
The workspace organizes report content into five visible states:
- Supported fact — anchored directly to a source passage or data cell
- User assertion — supplied by the author but not independently evidenced
- AI synthesis — a labeled interpretation across one or more sources
- Gap — information the current evidence does not establish
- Human review — a consequential judgment reserved for an accountable person
This creates a reviewable path from source material to an approved report without presenting every generated sentence as equally authoritative.
The interaction model
The live foundation demonstrates four connected layers:
- Sources show uploaded files and their validation or extraction state.
- Profiles make the requirements for a reporting context explicit.
- Drafts keep claim type and evidence references visible beside the writing.
- Review requires unresolved gaps and consequential judgments to be addressed before approval or export.
The initial examples cover education, business, and compliance-support contexts while explicitly avoiding universal compliance claims.
Guardrails by design
The product direction treats source files as untrusted, keeps private material within an owner-scoped account boundary, and reserves final approval for a person. Evidence links and gap states are part of the interface rather than hidden implementation details.
The current public workspace is intentionally labeled as an interactive product foundation. Upload, generation, approval, and export controls remain staged in the product roadmap and are not represented as complete.
What this project demonstrates
- Turning an abstract trust problem into a concrete information and interaction model
- Designing AI assistance around provenance, uncertainty, and human accountability
- Translating reporting requirements into reusable profiles instead of vague promises
- Connecting product language, interface states, security boundaries, and launch constraints
- Communicating product maturity honestly while making the direction tangible
Evidence note: This case study is based on the public ReportVerity overview, workspace demonstration, sign-in surface, and observable deployment behavior reviewed on September 12, 2026. Private implementation records, completed workflow verification, screenshots, and usage outcomes should be added only after publication review.