A public-record alignment engine for healthcare hearings.

What it does

It turns testimony into committee prep.

Users enter a hearing title, committee, healthcare topic, company type, CEO name, organization, and draft testimony. The application extracts claims, maps them to healthcare issue tags, retrieves evidence fixtures or configured public sources, scores alignment, and generates red-team questions and safer rewrites.

Committee coverage

Built beyond Appropriations.

Evidence discipline

Every senator-specific claim needs a source.

• Direct evidence is separated from reasonable inference.
• Thin evidence is labeled instead of filled in with speculation.
• Recommendations are marked as strategic communications guidance, not facts.
• Unsupported LLM-style senator claims are blocked by citation auditing.

Healthcare topics

Configured for issues senators actually press.

The taxonomy includes Medicare Advantage, prior authorization, AI in healthcare, drug pricing, PBMs, hospital consolidation, cybersecurity, NIH funding, rural health, long-term care, patient access, safety, quality, reimbursement, privacy, fraud, billing transparency, and price transparency.

Guardrails

Designed to avoid misleading testimony.

Author

Built by Wayan Vota.

Wayan Vota is a digital development strategist focused on practical technology adoption and responsible AI uses by mission-driven organizations.

Wayan is not a software engineer; he cannot code his way out of a paper bag. All aspects of this tool were vibe coded using OpenAI Codex and its direction to work with other web services.

He built this healthcare-focused tool as a proof of concept to show how public-record retrieval, source discipline, and AI-assisted synthesis can improve Senate testimony preparation without replacing human judgment.

This is a personal passion project. That does not reduce his enthusiasm. It makes him fearless.