We checked whether our AI EMR appears in generic searches. It did not.

Description and use

Krasyn built a canonical product catalog and generates structured data to improve search visibility and ensure accurate product information.

Problem

Krasyn’s AI EMR was not appearing in generic search results, limiting visibility to potential new buyers.

Outcome

Krasyn’s products are now discoverable in generic searches, with consistent, machine‑readable records and corrected copy, enabling future ranking improvements based on real usage data.

Workflow

• 1. Conduct baseline search test with seven generic queries and four branded queries.
• 2. Identify missing or inconsistent product information on public site and third‑party directories.
• 3. Build a canonical product catalog containing name, price, URL, category, operating surface, and capability boundaries for each product.
• 4. Generate crawlable SoftwareApplication structured data from the catalog.
• 5. Update product pages and static HTML to reflect catalog data; enforce build failure on drift.
• 6. Correct misleading copy (e.g., AI Coder status) and prepare factual correction requests for directories.
• 7. Set up weekly automated re‑runs of baseline queries to monitor visibility and source support.
• 8. Define success metrics for each product (e.g., completed checks, activated accounts, qualified evaluations).

Evidence

On August 29, 2026, we checked seven generic queries across normal web search and AI-answer surfaces. They covered phrases such as best AI EMR, AI-native EHR for an independent practice, and EMR with an AI scribe. Krasyn appeared in zero of the seven.

Source

We created one canonical product catalog and now use it to generate crawlable SoftwareApplication records for: Note Check, Krasyn Scribe, the Patient Portal pilot, Krasyn EMR.

Source