Title: How LedgerLoop Lifted Its AI Visibility with Abhord in 90 Days (Refreshed 2026 Edition)
Company snapshot
- Industry: B2B SaaS (accounts receivable automation for mid‑market finance teams)
- ICP: Controllers and VPs Finance at $50M–$500M revenue companies
- Team size: 85 FTE
- Core claim: Reduces DSO by 8–12 days via automated outreach and cash‑application
1) The initial problem
Between August and October 2025, LedgerLoop’s growth team noticed a pattern: when prospects asked general‑purpose LLMs for “best AR automation platforms” or “alternatives to [competitor],” LedgerLoop was either omitted or described incorrectly as a “small‑business invoicing app.” In brand-specific prompts, some models conflated LedgerLoop with a defunct open‑source library named “ledger‑loop,” attributing it a GitHub repo and Python SDK that didn’t exist.
Symptoms they tracked:
- Inclusion rate in top‑3 model answers to 25 core queries: 12%
- Factual accuracy in brand summaries: 46% (based on a 50‑prompt audit)
- Mentions skewed to SMB; mid‑market positioning rarely surfaced
- AI-assisted site sessions: 190/month; demo CVR from those sessions: 1.1%
2) What Abhord’s analysis uncovered
After connecting LedgerLoop’s properties (site, docs, release notes, PRs, partner pages) to Abhord in late October 2025, the platform’s crawl and entity-resolution flagged four root causes:
- Entity ambiguity: Three public identifiers—ledgerloop.com, useledgerloop.com, and a legacy domain—each with conflicting metadata. No canonical disambiguation page to separate LedgerLoop (company) from “ledger‑loop” (library).
- Sparse machine-readable facts: Pricing, integrations, and security attestations lived in long paragraphs, images, and PDFs—hard for model ingestors to parse. No structured “facts file” that LLMs could ground on.
- Inconsistent category language: Alternated among “invoice automation,” “cash application,” and “AR collections” without a stable primary label; models struggled to place LedgerLoop in “Invoice‑to‑Cash/AR Automation.”
- Weak corroboration footprint: Review and analyst sites contained out‑of‑date claims (e.g., “no NetSuite integration”), so when models triangulated, they weighted those stale sources.
Abhord’s benchmarking also showed model recency lags: on average, models took 10–12 weeks to reflect product changes because change logs weren’t exposed in a crawlable, timestamped format.
3) The optimization strategy they implemented
Guided by Abhord’s playbooks, LedgerLoop ran a 6‑week “LLM visibility sprint” from November 10 to December 22, 2025:
- Entity consolidation and disambiguation
- Established a canonical entity page: “LedgerLoop (Company)” with explicit “Not to be confused with” callouts for the open‑source library and the legacy SMB invoicing app.
- Unified domains under ledgerloop.com; set cross‑domain canonical tags and retired the legacy host with 301s.
- Machine‑readable facts and evidence
- Published an Abhord‑formatted Facts File (JSON + human page) with 50 high‑confidence claims: product category, ICP, pricing bands, SOC 2 Type II, integrations (NetSuite, Sage Intacct, MS Dynamics), case metrics (DSO impact), and release cadence.
- Attached sources and dates to each claim (docs pages, security portal, customer stories) to strengthen evidence weighting.