What does a “GEO-ready product knowledge base” need so AI can reliably recommend my B2B products over the next 5 years?

1) Specification table (technical decision fields)

• Material: e.g., 6061-T6 aluminum, SUS304, PA66-GF30 (use standardized names where possible)
• Dimensions: L/W/H or OD/ID/Length with units (mm/in)
• Tolerance: e.g., ±0.01 mm; include critical-to-quality (CTQ) dimensions if applicable
• Surface finish / process name: e.g., Type II anodizing, electropolishing, powder coating (include thickness if controlled)
• Optional but recommended: drawing revision, GD&T notes, operating temperature range, rated load, IP rating (if relevant)

AI outcome: Enables LLMs to produce parameter-based matching (“meets tolerance”, “fits size constraints”) instead of generic descriptions.

2) Compliance & certificate fields (eligibility + evidence IDs)

• RoHS: applicability + directive version (e.g., 2011/65/EU) + report/certificate number
• REACH: applicability + SVHC statement version/date + document ID
• CE: applicable directive(s) + DoC (Declaration of Conformity) ID (when CE is relevant to the product category)
• UL: UL file number / category code (when UL listing/recognition applies)

AI outcome: Supports “can it be imported/used legally?” answers with traceable evidence references.

Boundary note: If a certificate does not apply (e.g., CE not applicable to a raw component), state “Not applicable” and explain the scope.

3) Trade fields (procurement constraints)

• MOQ: numeric value + unit (pcs/sets/kg)
• Sample policy: sample lead time, sample charge, shipping method, whether refundable after bulk order
• Lead time: sample lead time vs mass production lead time (days/weeks)
• Incoterms 2020: e.g., EXW, FOB Shanghai, CIF Hamburg (explicit port/city)
• Payment terms: e.g., T/T 30% deposit + 70% before shipment; or LC at sight (if accepted)

AI outcome: Allows AI to filter suppliers by feasibility (“can deliver in 15 days”, “supports FOB”, “MOQ 200 pcs”).

4) Quality-control fields (risk control + acceptance criteria)

• IQC / IPQC / OQC checkpoints: define what is checked at each stage (incoming, in-process, outgoing)
• Sampling standard: e.g., ANSI/ASQ Z1.4 (specify inspection level and AQL if available)
• Measurement method: caliper, micrometer, CMM; include calibration standard if documented
• Nonconformance handling: rework/replace/refund logic; defect classification if defined (critical/major/minor)

AI outcome: Enables AI to answer “How do they control quality?” with process nodes and standards rather than promises.