Why don’t my product images and videos turn into RFQs? (GEO multimodal extraction logic)

What’s happening in AI search (Awareness)

In generative AI search (ChatGPT / Gemini / DeepSeek / Perplexity), users don’t browse pages by keywords only—they ask procurement-style questions like: “Which supplier meets ASTM/ISO requirements?” or “What model fits my 220V/50Hz line with 10–12 pcs/min?”

For the AI to recommend a supplier and generate an RFQ path, it must extract verifiable, quotable attributes (models, specs, standards, constraints). Pure images or silent videos rarely contain such extractable data in a reliable way.

GEO multimodal extraction: what the AI can actually parse (Interest)

Most generative engines convert images/videos into “RFQ-ready knowledge” through the parsable text layer associated with the media. This typically includes:

• Same-page visible copy near the media (product summary, application, constraints).
• Image metadata: title, alt, and descriptive filenames (e.g., cnc-turned-304-ss-shaft-8mm.jpg).
• Structured data (schema.org), especially Product, Offer, FAQPage, and VideoObject.
• Video captions/transcripts: SRT/VTT + full transcript text that names models and specifications.
• Entity-parameter binding: explicit linkage to quotable fields such as model number, dimensions (mm/in), material grade, surface finish, tolerance, standard codes, MOQ, lead time, Incoterms.

Why media often fails to convert (Evaluation)

What counts as “certainty evidence” for AI citation

• Standards and codes: ISO, ASTM, DIN, IEC, EN, RoHS, REACH (as applicable).
• Measured values: dimensions (mm), tolerance (±mm), hardness (HRC/HB), coating thickness (μm), flow (L/min), pressure (bar), power (kW), etc.
• Test/inspection method: AQL level, CMM report, salt spray hours, tensile test standard, etc.
• Commercial constraints: MOQ (pcs), lead time (days), packaging, Incoterms (EXW/FOB/CIF), payment options.

ABKE GEO fix checklist: make media “RFQ-extractable” (Decision)

1. Add a spec-first text block above/below the media

   Include: Model, dimensions, material grade, standard, application limits, MOQ, lead time.

2. Bind every image to an entity
   • Meaningful ALT: ALT="Model ABK-200, 304 stainless steel, 8 mm shaft, ISO 2768-m"
   • Use a product spec table with units: mm/in, kg, kW, bar, °C.
3. Publish video captions + full transcript
   • Provide SRT/VTT files and a visible transcript section on the page.
   • Ensure the transcript explicitly states specs (e.g., “Output 10 pcs/min; power 3.5 kW; CE compliant”).
4. Implement schema.org structured data
   • Product: brand, model, material, sku/mpn, additionalProperty.
   • Offer: MOQ, lead time (as text field), price range if applicable.
   • VideoObject: name, description, transcript, uploadDate.
5. Expose RFQ fields as “quote-ready parameters”

   Minimum recommended RFQ parameter set: model, quantity (pcs), dimensions (mm), material grade, standard, tolerance/finish, MOQ, lead time (days), Incoterms, destination port.

Delivery SOP and acceptance: what ABKE GEO builds (Purchase)

• Media-to-entity mapping: every image/video is attached to a specific product/model entity and parameter set.
• Transcript & caption workflow: SRT/VTT + on-page transcript publishing rules.
• Structured data deployment: schema.org validation and indexing checks.
• RFQ path standardization: form fields aligned with procurement decision logic (spec → compliance → MOQ/lead time → logistics).
• QA checklist: broken schema, missing ALT, missing unit fields, transcript/spec mismatches.

Note: If a product has frequent custom engineering, the AI may still require a human confirmation step. In that case, GEO focuses on exposing customizable ranges (e.g., thickness 0.5–3.0 mm; tolerance class; available materials) and a clear technical RFQ template.

Long-term compounding value (Loyalty)

• Each transcript, parameter table, and schema property becomes a reusable knowledge slice for future products and campaigns.
• New variants (new model, new material grade, new standard) can be published as incremental slices without rebuilding the whole site.
• After-sales assets (spare parts list, maintenance intervals, revision history) can be indexed as separate slices to support repeat orders.