Case Study: How RevOptic Fixed Its AI Visibility With Abhord (2026 Refresh)

Snapshot

• Company: RevOptic (fictional), a B2B SaaS platform for revenue operations automation
• ICP: Mid-market SaaS (75–1,200 employees) using hybrid usage- and seat-based pricing
• Period Measured: October 2025–February 2026 (20 weeks)
• Team Involved: Growth, Product Marketing, Docs, Partnerships, RevOps

1) The Initial Problem

By October 2025, RevOptic noticed a troubling pattern: when prospects asked leading LLMs for “best RevOps automation platforms,” the brand was either omitted or — worse — conflated with a similarly named analytics tool (“RevOptics,” unrelated). Internally, sales heard lines like, “Chat said you only do invoice reminders,” which was both incomplete and out-of-date.

Baseline (Abhord audit week 0):

• LLM Mention Share across six leading models: 9.8%
• Correctness of brand summaries: 59%
• Confusion with similarly named vendors: present in 2 of 5 tests
• AI-sourced trial starts (self-reported “found via AI answer”): 8/month

2) What Abhord’s Analysis Uncovered

Abhord’s GEO/AEO diagnostics mapped RevOptic’s public entity footprint and how models synthesized it. Three root causes emerged:

• Fragmented entity signals: The company name appeared as “RevOptic,” “Rev Optic,” and “RevOptic.io” across docs, partner