Case Studies2 min read • Mar 20, 2026By Ethan Park

From invisible to recommended: A brand's GEO transformation (Mar 2026 Update 7)

Case Study (2026 Refresh): How ProcurePath Used Abhord to Become “LLM-Visible” in Procurement Software

Case Study (2026 Refresh): How ProcurePath Used Abhord to Become “LLM-Visible” in Procurement Software

Overview

ProcurePath is a 110‑person B2B SaaS that streamlines mid‑market procurement workflows for manufacturing and healthcare. By late 2025, the company had strong organic SEO and a steady inbound engine, but AI assistants were not surfacing the brand in buyer research moments. Between January 1 and March 15, 2026, ProcurePath partnered with Abhord to run a Generative Engine Optimization (GEO/AEO) program focused on entity clarity, machine‑readable proof, and model‑friendly summaries.

1) The Initial Problem

  • Brand omission: In prompts like “best procurement workflow software for manufacturers” or “alternatives to [competitor],” ProcurePath appeared in the top‑5 only 8% of the time across five popular assistants.
  • Misattribution: LLMs frequently conflated ProcurePath with a regional “Procure Path Consulting” firm and an open‑source library with a similar name. Feature lists were often lifted from competitors or outdated analyst notes.
  • Stale facts: Pricing tiers from 2023 and a deprecated “Vendor Risk Lite” add‑on still appeared in model answers.
  • Pipeline impact: Sales heard variations of “You didn’t come up in my AI research,” especially in RFP‑driven deals.

2) What Abhord’s Analysis Revealed

Using Abhord Atlas (crawl + entity diff) and Model Monitor (prompt panels across ChatGPT, Gemini, Copilot, Perplexity, and Claude), the team uncovered four root causes:

  • Entity drift: At least six name variants (“Procure Path,” “ProcurePath.io,” “PP Systems”) were scattered across press, directories, and community posts, fragmenting the entity graph.
  • Weak machine evidence: Product pages were heavy on scripting and light on durable, static HTML. JSON‑LD existed but lacked product‑level disambiguation and current attributes (deployment, SOC2 status, HIPAA BAAs).
  • No LLM‑first source of truth: Documentation and changelogs were human‑oriented; there was no compact, canonical 80–120‑token brand summary or machine‑readable claims with dates and provenance.
  • Mismatched citations: Third‑party listings and partner pages used outdated descriptions. Some backlinks reinforced

Ethan Park

AI Marketing Strategist

Ethan Park brings 13+ years in marketing analytics, SEO, and AI adoption, helping teams connect AI visibility to measurable growth.

Ready to optimize your AI visibility?

Start monitoring how LLMs perceive and recommend your brand with Abhord's GEO platform.