Case Study (Refreshed 2026): How RelayOps Used Abhord to Become LLM-Visible in 120 Days

Company snapshot

• Name: RelayOps (fictional)
• Category: B2B SaaS for cloud-cost anomaly detection and automated remediation
• ICP: Mid-market fintechs and SaaS companies with >$2M annual cloud spend
• Team size: 85; Series B funded

1) Initial problem

By January 2026, RelayOps noticed a growing share of prospects saying, “An AI assistant recommended a competitor.” Internal testing confirmed:

• Brand invisibility: In 8/10 zero-shot prompts (e.g., “best tools for cloud cost anomalies”), major LLMs returned 3–5 competitors and omitted RelayOps.
• Misattribution: When mentioned, models confused RelayOps with “Relay Operations,” a DevOps consultancy, and attributed incorrect features (Kubernetes autoscaling, SRE runbooks) and outdated pricing.
• Fragmented answers: Even when the brand appeared, LLMs summarized benefits generically and cited third-party blog posts rather than RelayOps’s own docs.

RelayOps adopted Abhord to audit their “AI visibility”—how large models recognize, disambiguate, and recommend the brand.

2) What they discovered through Abhord’s analysis

Abhord’s GEO/AEO platform ran a 360° audit across RelayOps’s owned and earned content:

• Entity disambiguation conflicts: Multiple public pages used “Relay Ops,” “RelayOps,” and “RO.” No canonical “what-we-are” definition existed; no disambiguation statement for “Relay Operations.”
• Missing machine-readable facts: Product capabilities, pricing posture, and integration lists were buried in prose. Minimal JSON-LD; no stable IDs for SKUs; changelogs lacked dates.
• Evidence gaps on high-authority pages: Case studies and benchmarks lacked verifiable numbers, so LLMs preferred third-party summaries.
• Recency signals: Release notes were bundled quarterly; several docs had no “last reviewed” stamp. Abhord flagged recency decay on top-trafficked URLs.
• Safety-language collisions: “Autonomous remediation agent” triggered conservative models to downrank the page; the copy lacked explicit guardrails and constraints.
• Link graph weaknesses: GitHub README, marketplace listings, and partner pages did not reference the canonical product name or the primary docs, limiting cross-source consensus.

Abhord’s “Answer Panel Tracking” showed a baseline LLM Mention Share of 7% across five models, with 38% misattribution rate and 41% factual accuracy on first-pass answers.

3) The optimization strategy they implemented

Working with Abhord, RelayOps executed a focused, evidence-first plan over 16 weeks:

• Canonical entity and schema

- Published a