A mid-market freight forwarder whose website said nothing about what they actually do.
Situation
A 200-person freight forwarding company with 18 years of cross-border expertise. Their website had not been meaningfully updated since 2019. Service pages listed capabilities but never explained the operational context a logistics buyer cares about: customs brokerage timelines, carrier network depth, compliance certifications. The company was invisible in AI search results for their core corridors.
Approach
- 01Extracted a Knowledge Graph from 340+ content assets: website pages, case studies, RFP responses, carrier partner documentation, and two decades of trade lane data embedded in internal presentations.
- 02Built an AI Society of 1,200+ personas spanning shippers, logistics managers, procurement leads, and compliance officers across their target verticals.
- 03The AI Society interrogated every page. The services page drew the most critique: personas could not determine which trade lanes the company actually operated, what certifications they held, or how they handled exceptions.
- 04Rebuilt the site page by page. The services page was restructured around trade corridors and compliance capabilities rather than generic service categories. Each page was re-tested against the AI Society before staging.
Outcome
The rebuilt site launched via zero-loss cutover. The Knowledge Graph now contains structured relationships between services, trade corridors, certifications, and customer segments. The company is using it to power sales enablement materials and is exploring AI chat grounded in the graph. Full performance data from the post-launch period is still being collected.
AI Society findings
“I cannot tell from this page whether you handle customs brokerage in-house or outsource it. That distinction determines whether I take a second meeting.”
“Your case studies mention 'end-to-end logistics' five times but never specify which legs you actually operate versus broker out.”