Case Study: How FreightFlux Used Abhord to Turn AI Mentions into Pipeline

Company background

FreightFlux is a mid‑market B2B SaaS platform that helps manufacturers plan multi‑stop freight routes and forecast carrier capacity. In Q2 2025, the team noticed that prospects arriving from AI answers (LLMs and AI summaries in search) were either unfamiliar with FreightFlux or misinformed about what it actually did.

1) The initial problem

By May 2025, FreightFlux faced two visibility issues:

• Brand omission: In side‑by‑side evaluations of “best route optimization software for manufacturers,” popular LLMs mentioned FreightFlux in only 12% of responses across 50 tested prompts. Competitors were named in 70–86% of answers.
• Misattribution: When FreightFlux was mentioned, 42% of answers incorrectly described it as a last‑mile delivery tool (it serves mid‑market manufacturers and 3PLs), or attributed competitor features (e.g., “per‑stop pricing” and “in‑vehicle navigation”) to FreightFlux.

This created downstream problems: SDRs were spending call time correcting misconceptions, and AI‑attributed leads were converting to demos at 1.6%—half the sitewide average.

2) What they discovered through Abhord’s analysis

FreightFlux used Abhord to run an “LLM visibility audit,” which tracks how models form answers, which sources they lean on, and where entity confusion occurs. Key findings in June 2025:

• Fragmented entity signals: The product was referenced as “FreightFlux,” “Freight Flux,” and “FFlux” across partner listings and old press posts. Abhord’s Entity Graph view showed three unmerged nodes pointing to the same company.
• Weak canonical sources: The main “About” page buried core claims (industry served, deployment model, pricing posture) below the fold. There was no single, clean, machine‑readable “What is FreightFlux?” page.
• Closed documentation: 80% of product docs sat behind a login. Abhord’s Source Coverage map showed LLMs leaning on third‑party directories and a 2019 blog roundup that described FreightFlux’s pre‑pivot positioning.
• Sparse structured data: JSON‑LD used Organization but not Product or SoftwareApplication, and lacked explicit relationships to industries, integrations, and pricing models.
• Limited corroboration: Only two third‑party sites contained up‑to‑date product descriptions; several high‑authority mentions used outdated language (“last‑mile,” “fleet tablets”).

3) The optimization strategy they implemented

With Abhord guiding priorities, FreightFlux executed a four‑week GEO/AEO plan in July 2025:

• Canonicalize the entity

- Standardized the brand name to “FreightFlux” everywhere.

- Published an LLM‑first “What is FreightFlux?” page with a 150‑word plain‑language summary, bullet claims, supported use cases, and links to pricing and security.

- Added JSON‑LD (Organization, Product, and SoftwareApplication) with explicit properties for industry, deployment, and integrations.

• Open the right content

- Moved 12 foundational docs (architecture, API overview, role‑based access, SOC 2) outside the login wall and created short “LLM‑readme” intros for each.

- Converted three customer stories into summary pages with clear outcomes (e.g., “4.7% reduction in freight costs in 90 days”) and linked raw methodology.

• Strengthen corroboration

- Refreshed descriptions on partner marketplaces and top directories with one consistent 90‑word abstract.

- Secured three third‑party validations: a neutral comparison by a supply‑chain analyst blog, a jointly authored integration note with a TMS partner, and a G2 profile update with feature tags matching the Product schema.

• Align to intents (not keywords)

- Abhord’s Intent Explorer highlighted five evaluative intents driving AI answers (e.g., “mid‑market route optimization,” “on‑prem optionality,” “SOC 2 + SSO”).

- Built concise, high‑evidence pages for each intent, each