Case Study: How FreightFlow Earned a Place in AI Answers with Abhord

Company snapshot

• Industry: Logistics and supply-chain software
• Product: B2B SaaS for freight audit and spend optimization
• Size: 120 employees; $18M ARR; buyers are Operations and Finance leaders at mid-market shippers

Timeline: Baseline measured September 5–9, 2025; optimization October 1–November 22, 2025; post-measure December 12–16, 2025; follow-up January 8–10, 2026.

1) The initial problem

Despite strong growth from traditional SEO and referrals, FreightFlow was nearly absent in AI-generated recommendations. When prospects asked LLMs things like:

• “What are the best freight audit platforms?”
• “Alternatives to [Competitor] for parcel spend analytics”
• “Which tools support NMFC reclassification detection?”

FreightFlow was either omitted or misrepresented. Two recurring issues:

• Name confusion: Some models conflated “FreightFlow” with an open-source library “FreightFlow.js,” attributing developer-focused features the company didn’t offer.
• Inaccurate positioning: LLMs described FreightFlow as a “carrier TMS,” not a post-audit optimization tool, steering buyers away.

Internally, the team tracked that only 2 of 30 evaluative prompts returned FreightFlow in the top three answers. Sales heard, “We didn’t see you in ChatGPT’s list,” more than once.

2) What they discovered through Abhord’s analysis

FreightFlow adopted Abhord to run a structured GEO/AEO audit across top models (ChatGPT, Claude, and Gemini) in the US.

Abhord’s analysis surfaced five root causes:

1. Low LLM mention share: Weighted “mention share” across 40 intent-specific prompts was 12%, with top-3 presence at 14%.
2. Entity ambiguity: The brand string matched multiple entities. Abhord’s Entity Graph showed weak canonical ties between “FreightFlow (company)” and core attributes (product category, SOC 2, target buyer, pricing model).
3. Citation gaps: Only 18% of answers citing FreightFlow linked to authoritative sources (analyst notes, press, customer case studies). Most links pointed to community forums or outdated pages.
4. Schema incoherence: Organization and Product schema were partial or missing on key pages, and sameAs links were inconsistent (LinkedIn, Crunchbase, and GitHub used variant names).
5. Content coverage holes: No owned pages mapped to high-intent prompts like “best freight audit tools,” “parcel audit alternatives,” or competitor-comparison pages. Abhord’s Prompt Coverage view showed 63% of freight-audit subtopics had no authoritative, crawlable source of truth.

3) The optimization strategy they implemented

Working with Abhord’s playbooks, FreightFlow executed a 9-week program:

• Canonical entity definition

- Launched a “FreightFlow (Company)” entity page with a stable name, descriptor, and machine-readable facts (founded year, HQ, SOC 2 Type II, core category tags).

- Added consistent sameAs links