ABKE (AB客) recommendation: treat GEO procurement as a modular system, purchased in the order that AI systems typically “trust” information: Evidence Assets → Distribution → Monitoring → Correction. This reduces waste because you only scale distribution after evidence is machine-readable and verifiable.

---

1) Awareness: What problem does “modular GEO” solve?

In generative AI search (e.g., ChatGPT, Gemini, DeepSeek, Perplexity), buyers often ask complete questions instead of typing keywords, such as:

• “Which supplier meets ISO 9001 and can provide traceability?”
• “Who can manufacture to ±0.01 mm tolerance and provide inspection reports?”
• “Which company complies with RoHS / REACH for this material?”

If your website and content do not expose structured evidence (standards, tolerances, test methods, certifications, delivery capability, warranty terms) in AI-readable formats, AI systems may not cite you—even if you are technically capable.

2) Interest: What is the ABKE modular structure (technical difference vs. “traditional SEO”)?

Traditional SEO procurement often focuses on rankings for a small set of keywords. GEO module selection focuses on verifiable evidence blocks that AI can extract and cite. ABKE splits delivery into four modules:

1. Evidence Assets (on-site): machine-readable facts and proof
2. Distribution (off-site + cross-domain): controlled replication of evidence to multiple trusted locations
3. Monitoring: fixed query-set tracking of AI visibility and citations
4. Correction: semantic fixes when AI misattributes, omits, or confuses entities

---

3) Evaluation: Budget-based module selection (what you buy, what you get)

A. Foundation Package (budget-limited, first 30–45 days)

Goal: make your company “understandable and citable” from your owned assets (website).

• On-site structured evidence blocks
  • Schema/JSON-LD: Organization, Product/Service, FAQPage, BreadcrumbList (as applicable)
  • Specification tables: e.g., material grades, tolerance (mm), surface treatment (µm), operating temperature (°C), test methods
  • FAQ evidence: answers including standards (ISO/ASTM/EN), measurable parameters, documentation list
• Core query mapping: 10–20 buyer-intent queries mapped to specific pages/sections (e.g., “ISO 9001 supplier + product category”, “tolerance + process capability”, “inspection report type”).

Boundary / risk: without off-site distribution, AI citation lift may be slower in highly competitive categories; your main improvement will be extractability and consistency rather than immediate “top recommendations”.

B. Growth Package (scaling, typically 45–75 days)

Goal: expand your evidence into multilingual and multi-location footprints that AI systems can discover and cross-validate.

• Multilingual evidence clusters: at least 3 languages (commonly EN + 2 target-market languages). Each language set contains consistent: specifications, compliance statements, documentation, and process capability facts.
• Cross-domain distribution: at least 5 domain landing points (e.g., brand site + product microsite + documentation hub + technical article domain + partner/PR domain), each referencing the same entity facts (company name, address, product scope, certifications, test reports).

Boundary / risk: multilingual work requires strict terminology control (units, standards naming, material designations). If translations introduce parameter drift (e.g., mm vs. inch errors), AI may reduce trust or cite conflicting numbers.

C. Reinforcement Package (control + reliability, typically 60–90 days and ongoing)

Goal: measure AI visibility with a fixed query set, and correct semantic gaps that block citations.

• AI search visibility monitoring
  • Weekly or monthly reporting cadence
  • Fixed query set: track the same buyer questions each cycle
  • Metrics: AI answer visibility (presence/position when applicable), citation rate (being referenced), and the source URLs where citations occur
• Semantic correction work orders
  • Entity disambiguation (company vs. similarly named brands)
  • Evidence strengthening (missing test methods, incomplete spec ranges)
  • Content and schema corrections (wrong attributes, missing language alternates)

Boundary / risk: AI platforms can change retrieval behavior; therefore, ABKE recommends measuring outcomes via citations + source URL evidence, not only “rank”.

---

4) Decision: What acceptance criteria should be written into the contract?

ABKE suggests a minimum measurable acceptance clause based on a controlled query set and citation evidence:

• Core query set: ≥ 20 buyer-intent queries (fixed list agreed before execution)
• Time window: 60–90 days after baseline measurement
• Acceptance KPI: “AI citation rate” improves by ≥ 30%
• Proof: provide a source URL list showing where the brand/company is cited or referenced in AI answers or AI-linked sources

Note: If your industry has long sales cycles or strict compliance requirements (e.g., medical, automotive), add a second KPI: number of published evidence documents (e.g., test reports, material certificates, SOP summaries) with stable versioning.

---

5) Purchase: Delivery SOP (what you should expect during implementation)

1. Baseline: confirm query set, current citation presence, and existing evidence inventory (certifications, standards, spec ranges, reports).
2. Evidence build: implement schema/JSON-LD, spec tables, FAQ evidence blocks; align units (mm/µm/°C), standard codes (ISO/ASTM/EN), and document names.
3. Distribution: publish multilingual evidence clusters and cross-domain landing points with consistent entity facts.
4. Monitoring: run scheduled checks on the fixed query set; record citations and URLs.
5. Correction: issue work orders for missing attributes, conflicting specs, entity confusion, or weak proof chains.

---

6) Loyalty: How do modular GEO investments compound over time?

The long-term value comes from re-usable evidence assets that continue to be cited:

• Knowledge slices (specs, standards, test methods, documentation) become a maintained library—updated as products or certifications change.
• Monitoring reports create a historical dataset of which buyer questions generate citations and leads.
• Correction cycles reduce misattribution and improve entity consistency across AI systems.

When to upgrade packages: upgrade from Foundation → Growth once your on-site evidence is consistent and complete; upgrade from Growth → Reinforcement once you need predictable reporting, controlled query sets, and ongoing semantic correction.