{ "story_id": "7d23ed107c4e4bf545ad2c6b46c076b3", "desk": "drm3", "revision": 1, "published_at": "2026-09-01T23:06:00.000Z", "content_hash": "d1e64baa50abd4b9ac021839e0b11d273b558ed4a05bd07cee44b54e45543817", "hash_basis": "sha256 over `headline\\ndek\\nprose`, plus `\\n` + the canonical citations JSON when any source is placed, plus `\\n#blog` for blogs", "basis": { "headline": "Fundraise Up Launches Free AI Visibility Assessment for Nonprofits", "dek": "Fundraise Up launches a free tool to help nonprofits understand how AI assistants currently perceive their organizations.", "prose": "Fundraise Up launched the AI Visibility Assessment, a free tool that shows nonprofits how AI assistants currently understand and represent their organization. [^1]\n\nA five-person committee selected by the American Diabetes Association (ADA) conducted an internal review regarding the expulsion of five members from a scientific session in New Orleans. [^2]\n\nThe authors present DIASENTINEL, a fully on-premise multi-agent system designed for one-year type 2 diabetes mellitus risk screening and guideline-grounded report generation from electronic health records. [^3]\n\nFundraise Up CEO and Co-Founder Peter Byrnes stated that the launch is the first practical application of the company's Agentic Giving product suite introduced in July. [^4]\n\nOrganic search traffic to nonprofit websites has fallen approximately 35% year-over-year, while AI-referred traffic has grown roughly 1,000%. [^5]\n\nThe AI Visibility Assessment allows a nonprofit to enter its URL and see how AI currently interprets its organization, identify gaps, and receive immediate results with specific recommendations. [^6]\n\nThe committee rejected claims that the organization acted to silence protest or involved local police. [^7]\n\nThe review concluded that the members were ejected for violating a code of conduct that prohibits the distribution of printed materials in certain areas. [^8]\n\nDIASENTINEL employs a hybrid verification layer that combines rule-based checks with LLM entailment to ensure accuracy. [^9]\n\nThe system integrates calibrated risk prediction, deterministic clinical signal extraction, and Reciprocal Rank Fusion over American Diabetes Association guidelines. [^10]\n\nThe committee dismissed suggestions that the expulsion was a reaction to the substance of an April 29 commentary published in the ADA's Diabetes Care journal. [^11]\n\nThe authors state that DIASENTINEL demonstrates a practical framework for reliable, auditable, and privacy-preserving LLM-based clinical decision support. 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