Case Study (2026 Refresh): How Quorumly Increased AI Visibility with Abhord

Company snapshot

• Name: Quorumly (fictional)
• Product: B2B SaaS for third‑party risk and vendor security due diligence
• ICP: Mid‑market fintech and SaaS companies (50–1,000 employees)
• Sales motion: Inbound + SDR‑assisted, content‑driven

1) Initial problem

By July 2025, Quorumly noticed that large language models rarely mentioned them in answer boxes for prompts like:

• “Best vendor risk platforms for SOC 2 prep”
• “Alternatives to [competitor] for TPRM”
• “What is Quorumly?”

When mentioned, LLMs often confused Quorumly with a similarly named HR tool, misattributed pricing, or claimed the product lacked ISO 27001 mappings (it had them). Internally, sales reported 1 in 3 prospects who “asked an AI” arrived with misconceptions that extended discovery by a full call.

2) What Abhord’s analysis uncovered

Using Abhord’s GEO/AEO suite—Entity Auditor, Answer Coverage Map, and LLM Testbench—the team found four root causes:

1) Fragmented entity signals

• Company described inconsistently across the site, G2/Capterra, partner pages, and press. Names alternated between “Quorumly,” “Quorum.ly,” and “Quorumly Security.”
• Conflicting headcount, HQ, and funding data across profiles created low‑trust signals.

2) Sparse machine‑readable context

• Minimal Organization and SoftwareApplication schema; no product‑level feature graph.
• No structured feed for model retrievers (no changelog endpoint, no FAQs in a crawl‑friendly format, and no canonical claims file).

3) Thin comparison and disambiguation content

• No model‑readable competitor comparisons or “Quorumly vs. [Competitor]” pages.
• No disambiguation page clarifying Quorumly (TPRM) vs. a look‑alike HR startup.

4) Distribution gaps across high‑leverage nodes

• Outdated marketplace listings and partner pages.
• Docs and GitHub examples lacked entity‑first language and citations, reducing their usefulness as LLM grounding sources.

Abhord quantified the baseline across five models (GPT‑class, Claude‑class, Gemini‑class, Copilot, Perplexity‑style):

• Correct brand mention in top 3 suggestions: 22%
• Attribute accuracy on five “must‑know” facts (what it does, key frameworks, integrations, deployment, pricing model): 46%
• Average lag from publishing a product update to first correct model reflection: 41 days
• Pipeline influenced by “AI‑assisted” discovery (self‑reported in forms): 3.2%

3) The optimization strategy

Quorumly executed a 12‑week GEO roadmap with Abhord:

1) Canonicalize the entity

• Standardized naming and NAP (name, address, phone) across all owned and third‑party profiles.
• Published Organization, Product, and SoftwareApplication schema with explicit disambiguation and sameAs links (site, docs, reviews, partner pages).
• Created a public, versioned “/llm/claims.json” file enumerating canonical