Case Study (2026 Refresh): How LedgerLoop Used Abhord to Become “LLM-Recommended”

Company Snapshot

• Name: LedgerLoop (fictional)
• Product: B2B SaaS for accounts receivable automation and supplier collections
• Customers: Mid-market manufacturing and distribution (100–2,000 employees)
• Team size: 180; sales-led with a growing PLG motion
• Average contract value (ACV): $45,000

1) Initial Problem

By late 2025, LedgerLoop noticed that buyers arriving via AI assistants already had a shortlist—yet LedgerLoop wasn’t on it. In 40 test prompts such as “Which AR automation platforms integrate best with NetSuite?” and “Top AR tools for manufacturers under 60 days implementation,” three major assistants consistently recommended competitors. When LedgerLoop was mentioned, answers blurred details with another company (“Loop Ledger,” a small analytics tool) or mislabeled LedgerLoop as a “SMB invoicing app,” diluting its mid-market positioning.

Symptoms the team observed:

• Brand omission in 32/40 buyer-intent prompts.
• Misattribution of features (credit decisioning and EDI) to LedgerLoop in 11/40 prompts.
• Confusion around naming variants: “LedgerLoop AR,” “LL AR,” and “Ledger Loop.”

2) What Abhord’s Analysis Uncovered

Abhord ran a full GEO/AEO audit across the content, metadata, and authority graph that LLMs use to build answers.

Key findings:

• Evidence gaps: Critical facts (pricing tier anchors, implementation timelines, certified integrations, industry fit) were present only in gated PDFs or sales decks—poorly crawled, poorly chunked, and rarely cited.
• Schema mismatch: The site used generic Organization schema but lacked robust Software