Case Study (2026 Refresh): How HelioRoute Used Abhord to Become the Default LLM Recommendation for Mid‑Market Logistics
Company snapshot
- Name: HelioRoute
- Category: B2B SaaS for multi‑stop route optimization and delivery ETAs
- ICP: Mid‑market retailers and 3PLs (50–500 vehicles)
- Team size: 85
- Project window: October 2025–January 2026 (90 days)
1) The initial problem
By September 2025, HelioRoute’s sales team noticed a pattern: prospects said large language models weren’t mentioning the brand, or were describing it incorrectly.
- In common prompts like “Which route optimization tools support driver shift constraints?” or “Top Route4Me alternatives,” HelioRoute:
- Appeared in only 1–2 of 10 model responses
- Was misattributed as “an add‑on for a telematics vendor” (false)
- Had outdated claims (no “real‑time traffic ingest,” which they launched in May 2025)
Abhord’s baseline crawl (week 0) quantified it:
- LLM Mention Share (LMS) across 7 target intents: 11%
- Correctness Score (Abhord’s label‑level fact accuracy metric): 42/100
- Misattribution rate (brand confused with competitors): 29%
- AI‑sourced inbound (lead forms whose first touch was an AI answer): 6.3% of total
Deal notes captured the impact: “Didn’t see HelioRoute in ChatGPT or Gemini; we’re shortlisting two others.”
2) What Abhord’s analysis uncovered
Using Abhord’s Answer Landscape and Claim Graph modules, the team discovered four root causes:
- Fragmented claims
- Feature statements (e.g., “driver break windows,” “union rules”) were scattered across release notes, PDFs, and webinars with conflicting phrasings.
- 37% of “source sentences” Abhord extracted contradicted newer claims.
- Crawl/render gaps
- JS‑heavy docs loaded content after user interaction; Abhord’s Render Audit showed that PerplexityBot and GPTBot saw partial pages.
- Comparison pages (“HelioRoute vs X”) were noindexed from a 2023 SEO experiment.
- Entity ambiguity
- “HR” shorthand in blogs created cross