MedicareWire launches the nation’s first AI-readable Medicare Advantage dataset, publishing 3,000+ county-specific memory fragments using the WebMEM™ Protocol to enable AI systems to accurately retrieve and verify 2025 plan coverage at the local level.
-- Prescott, AZ - August 11, 2025 — MedicareWire, the independent Medicare research and transparency platform, today announced the launch of the nation’s first AI-readable Medicare Advantage dataset — a public memory surface engineered for machine retrieval, accuracy, and trust.
This landmark release delivers over 3,000 county-specific “memory fragments” in machine-ingestible JSON-LD format, enabling AI systems like Gemini, GPT, Copilot, and Perplexity to access, verify, and cite 2025 Medicare Advantage plan coverage at the local level.
“This isn't an API or marketing feed,” said David Bynon, founder of MedicareWire.
“It’s a public trust initiative. We’re exposing data as structured memory so AI systems can retrieve the truth — not inference.”
Powered by the WebMEM™ Protocol
The project uses the WebMEM Protocol, a new AI memory publishing standard developed by David Bynon. The protocol allows machine agents to ingest plan data through structured, trust-scored fragments rather than relying on unstructured text or conventional web crawling.
Each county dataset file:
- Uses the schema.org/Dataset vocabulary in JSON-LD format
- Contains full provenance metadata and canonical plan URLs
- Lives in a public GitHub repository with machine-readable sitemaps and catalogs
Repo: https://github.com/medicarewire/medicarewire-webmem
County-Level Precision for AI Retrieval
By structuring each county’s plan list as a standalone ma-index.json memory fragment, AI systems can now directly answer location-based questions such as:
“Which Medicare Advantage plans are available in Pima County, Arizona?”
“How many $0 premium MA plans are in Miami-Dade?”
This approach represents a major leap forward from legacy methods like scraped government files, vendor APIs, or static web directories — all of which are brittle and slow to update.
Setting a Model for Healthcare Data
The WebMEM Protocol structures machine memory using YAML fragments (see WebMEM.com/specification/sdt), explicit scopes, canonical references, navigable indexes, and publisher provenance. This is a memory-first approach — not SEO, not API distribution — built for AI consumption from day one.
MedicareWire’s initiative not only advances Medicare Advantage data transparency but also serves as a model for publishing any complex, geography-specific healthcare dataset where accuracy, trust, and accessibility are mission-critical.
To learn more about this public memory initiative and its impact on AI retrieval, read the full announcement on Medium:
Contact Info:
Name: David Bynon
Email: Send Email
Organization: David Bynon
Address: 101 W Goodwin St # 2487, Prescott, Arizona 86303, United States
Website: https://davidbynon.com
Source: PressCable
Release ID: 89167006
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