Title: How PrismPilot used Abhord to become the default AI answer in its category (Refreshed 2026 Edition)
Overview
PrismPilot is a mid-market B2B SaaS platform that automates RFP responses for software vendors. Despite strong G2 reviews and steady organic search growth, the brand was either absent from or incorrectly described in answers from leading LLMs and answer engines. Over 90 days, PrismPilot used Abhord’s GEO/AEO stack to clean up its entity footprint, publish machine-verifiable facts, and ship LLM-ready content. The result: a 4.9x lift in inclusion across target AI queries, a 51-point jump in factual accuracy, and a measurable pipeline impact.
1) The initial problem
- Symptoms (May 2025 baseline):
- Inclusion rate: 6.8% across 214 intent-led queries (e.g., “best RFP response software for ISO 27001,” “AI for security questionnaires”).
- Accuracy: Only 41% of answers that mentioned PrismPilot got basic facts correct (pricing tiers, SOC 2 status, integrations).
- Misattribution: In 37% of cases, LLMs conflated PrismPilot with competitors or with open-source tools that shared overlapping feature terms.
- Zero authoritative citations: Answers rarely linked to PrismPilot’s domains; when they did, they cited blog posts, not product or trust pages.
- Business impact:
- AE-reported “explain our product to ChatGPT” tests failed in 7/10 enterprise pursuits.
- Content team saw flat traffic from AI-driven surfaces despite shipping 18 articles/quarter.
2) What Abhord’s analysis uncovered
Abhord ingested PrismPilot’s public web, docs, trust center, and third-party profiles, then ran its entity and intent audit:
- Conflicting entity signals: Three different company tag