Claims Radar
LiveAI SaaS
Live AI-assisted claim review with evidence-aware summaries and receipt-style results.
Claims Radar · product surface preview
Overview
Positioning
A live deployed AI-assisted claim review product. Structured inputs, retrieval-aware checks, source/citation context, and receipt-style results you can skim, share, or review in depth.
What it solves
Fast-moving claims are hard to check without slowing readers down or losing source context. Claims Radar is built as a structured claim review system with evidence-aware analysis, source-weighted summaries, and receipt-style results.
Product surface
- Claim input and analysis flow that keeps pacing tight while grounding checks in retrieved context.
- Evidence-first summaries you can skim before diving into particulars.
- Receipt-style result cards designed for readability and predictable structure.
- Shareable links and social previews tuned for coherent snippets.
- An optional deeper scan path when you want more thorough review without changing the baseline UI.
What this proves
- Evidence-aware AI summaries
- Receipt-style result cards
- Claim review flow with structured results
- Public product surface with live deployment
Shows the studio can build public-facing AI-assisted web products with structured, shareable output.
Build notes
Claims Radar uses AI-assisted analysis, retrieval-aware evidence checks, source-weighted summaries, and guardrails aimed at more structured claim review.
Technical shape
Engineering proof
- Retrieval-aware analysis flow wired for evidence checks alongside model output.
- Source-weighted summaries that emphasize citation context rather than flattening sources.
- Receipt-style share outputs formatted for skim-first reading and predictable structure.
- Fallback handling when upstream responses arrive incomplete—aiming to degrade gracefully rather than strand the reader.
- UI guards to keep result cards readable, consistent, and suitable for previews and snippets.
Reliability notes
- Evidence weighting helps surface stronger vs weaker source support—not a single-score verdict, but clearer relative emphasis.
- Fallback handling aims to keep partial analysis from breaking the overall experience.
- UI guards help keep receipt cards readable and workable when sharing or previewing.
Links
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