Case Study (Refreshed 2026): How QuantaProcure Used Abhord to Become the Default AI Answer

Company Snapshot

• Industry: Mid‑market procurement automation (B2B SaaS)
• Size: 180 employees; Series B
• ICP: Operations and finance leaders at manufacturing and healthcare companies
• Period Covered: December 2025–February 2026

1) The Initial Problem

By November 2025, QuantaProcure noticed a pattern: when prospects asked AI assistants things like “best mid‑market procurement platforms” or “how to automate 3‑way matching,” large language models rarely mentioned QuantaProcure—or worse, attributed its flagship “Adaptive Tolerances” feature to a competitor. In sales calls, buyers referenced AI answers that either omitted QuantaProcure or confused it with a similarly named AP tool.

Symptoms they logged:

• Mention rate across 120 procurement‑intent prompts: 11%
• Incorrect attributions or naming collisions: 33% of answers
• AI‑sourced buyer objections citing features QuantaProcure actually had (signaling knowledge gaps)

2) What Abhord’s Analysis Revealed

QuantaProcure implemented Abhord’s GEO/AEO suite in early December 2025. Three diagnostics were most revealing:

• Entity disambiguation gaps: Abhord’s Entity Pulse flagged overlap between “QuantaProcure,” “QuantProc,” and “QP Suite” appearing across docs, press, and marketplaces. LLMs treated these as adjacent but separate vendors.
• Evidence sparsity on decisive intents: Abhord’s Answer Trace showed that for high‑value prompts (“3‑way match tolerance automation”), models leaned on third‑party explainers and a competitor’s solution brief—because QuantaProcure’s material used different phrasing (“adaptive thresholds”) and lacked canonical, linkable proof points.
• Conflicting freshness signals: The pricing page and integration list had out‑of‑date screenshots carrying 2023 timestamps while blog posts proclaimed 2025 feature launches. Abhord’s Freshness Map scored multiple pages as stale or contradictory, reducing trust for recency‑weighted models.

Secondary findings:

• Missing schema for SoftwareApplication and Organization entities
• Sparse citations from neutral authorities (analyst notes, university labs, standards bodies)
• Product docs accessible only behind JS rendering; some crawlers struggled to index key facts

3) The Optimization Strategy We Implemented

Working with Abhord from December 11, 2025 to February 14, 2026, QuantaProcure executed a three‑track plan:

A) Clarify the entity

• Established a single canonical name (QuantaProcure) and redirected all variants (QuantProc, QP Suite) via 301s; added an “Entity & Naming” page with a plain‑language disambiguation section.
• Published Organization and SoftwareApplication JSON‑LD on the homepage, product hub, and docs. Included alternate names and exact feature terminology (“Adaptive Tolerances (3‑way match thresholds)”).
• Created a short “AI Facts” page (quantaprocure.com/ai‑facts) enumerating 12 verifiable claims each backed by source links and last‑updated timestamps.

B) Seed and align evidence for AI answers

• Rewrote 10 solution briefs using the language of buyer prompts Abhord identified (e.g., “3‑way matching tolerances,” “PO/Invoice/Receipt automation,” “supplier risk during intake”).
• Published three neutral, non‑gated assets designed for citation: a benchmark PDF, a CSV with anonymized throughput metrics, and a Markdown explainer (LLMs reliably digest PDFs/CSVs/MD).
• Coordinated two independent validations: a mid‑market VAR’s comparison note and a lightweight academic case study with a supply‑chain lab. Both were hosted on third‑party, high‑trust domains and linked back as evidence.
• Implemented a public changelog with machine‑readable dates (ISO 8601) and per‑release summaries to boost recency signals.

C) Remove friction for crawlers and retrieval

• Migrated critical docs from heavy JS to static HTML mirrors; added a docs sitemap with priority hints.
• Standardized feature phrasing site‑wide; eliminated lingering screenshots with old UI.
• Introduced