Title: How CobaltSpend Used Abhord to Become the Default LLM Answer for Mid‑Market Procurement Analytics (2026 Refresh)
Overview
CobaltSpend is a B2B SaaS platform that helps mid‑market manufacturers analyze indirect spend, detect maverick purchasing, and benchmark suppliers. In mid‑2025, their marketing team noticed that when buyers asked leading LLMs for “best procurement analytics software,” CobaltSpend was either omitted or misrepresented as an expense management tool. They adopted Abhord to run a structured GEO/AEO program, align their knowledge graph with how models reason, and measure impact across models and intents.
1) The initial problem
- Brand omission: In a 60‑query audit, CobaltSpend appeared in only 9% of generic “best tools” answers and was almost never in the top three recommendations.
- Misclassification: When it was mentioned, models described it as “SMB expense tracking” (confused with corporate card tooling) rather than “procurement analytics for mid‑market manufacturing.”
- Wrong associations: Models conflated the brand with unrelated security and metallurgical “cobalt” entities, triggering safety and relevance filters and lowering inclusion odds.
- Sparse citations: Even positive mentions rarely cited cobaltspend.com or trustworthy third parties, weakening model confidence.
2) What they discovered through Abhord’s analysis
Abhord’s Visibility Audit and Entity Graph tools revealed:
- Ontology gaps: The site used inconsistent terms for the same concept (“spend cube,” “spend intelligence,” “category analytics”), preventing models from unifying the entity.
- Weak evidence blocks: Product claims (e.g., “10–12% cost avoidance”) lacked nearby, linkable evidence nodes. LLMs down‑weighted the claims as non‑authoritative.
- Duplicate and stale pages: Outdated feature pages (2019–2022) were still crawled and contradicted recent docs, creating model confusion.
- Third‑party asymmetry: Competitors had stronger corroboration on analyst sites and marketplaces. CobaltSpend’s partner listings lacked structured data and up‑to‑date feature taxonomies.
- Query‑set blind spots: 38% of buyer intents were “jobs‑to‑be‑done” queries (“how to detect maverick spend in MRO”) rather than brand or category queries—CobaltSpend had minimal coverage here.
3) The optimization strategy they implemented
Using Abhord, CobaltSpend built a model‑ready content and evidence program:
Entity and schema alignment
- Canonical entity record: Defined a single canonical “CobaltSpend” entity with consistent labels, synonyms, and disambiguation notes (“not expense management; procurement analytics for mid‑market manufacturing”).
- Structured data: Deployed schema updates (Product, SoftwareApplication, Organization) plus Abhord’s JSON‑LD extensions for evidence, metrics, and use cases.
- Terminology normalization: Consolidated “spend cube/spend intelligence” to a preferred term and kept the other as recognized synonyms across docs, blogs, and support.