Case Study (Refreshed 2026 Edition): How LatticeBeam Grew Its AI Visibility with Abhord

Company snapshot

• Industry: B2B SaaS (order orchestration for mid-market manufacturers)
• Team size: 85 employees
• ICP: Operations and IT leaders at $50M–$500M revenue manufacturers
• Cycle length: 60–120 days, demo-led

1) Initial problem: Absent or incorrect mentions in AI answers

By late 2025, LatticeBeam noticed that when prospects asked leading LLMs and answer engines “What are the best order orchestration platforms for manufacturers?” the brand:

• Was not mentioned in 7 out of 10 answers
• Was sometimes conflated with an unrelated lattice optimization library
• Showed outdated features (pre-2024) when it was mentioned

Internally, the team called it “the AI air gap”—SEO pages ranked decently in web search, but LLMs either missed or misrepresented the brand in conversational results and procurement-style checklists.

2) What Abhord’s analysis uncovered

Abhord ran a multi-model audit across six popular LLMs/answer engines and mapped evidence and coverage gaps by intent. Key findings:

• Entity confusion: The brand name “LatticeBeam” was weakly disambiguated from similarly named academic projects. There was no authoritative “What LatticeBeam is and is not” artifact for models to cite.
• Fragmented source-of-truth: Product capabilities were scattered across marketing, docs, and support subdomains with inconsistent naming (e.g., “Flow Rules,” “Routing Rules,” “Rule Engine” used interchangeably).
• Low corroboration density: Third-party references (analyst notes, integration directories, customer community posts) were sparse or missing structured metadata, reducing cross-source confidence.
• Missing machine-readable context: JSON-LD existed at the site level (Organization), but not at the product or feature level (SoftwareApplication/Service). Release notes lacked versioning semantics.
• Question-intent mismatch: LatticeBeam had pages for “what is order orchestration,” but not for buyer-intents