The B2B signal problem
Canon sells industrial imaging — machine vision sensors, broadcast cameras, surgical optics, ROS-integrated camera SDKs — across global markets. The buyers are technical evaluators, procurement officers, and standards-body specialists.
Three structural problems break standard market intelligence tools when applied to this vertical:
01 — Search volume is low. Decision-makers don't Google "best camera for government tender". The categories barely register in keyword tools.
02 — Signal sits in deep web. AVIXA SiG threads, ROS Discourse, EMVA working group output, GitHub OSS issue trackers, ResearchGate imaging forums, Capture One Forum, PSN Europe — much of it behind login walls and never crawled by general SEO tools.
03 — Procurement is multi-stakeholder. Each role generates different signals across different surfaces.
The implication: standard SEO, social listening, and Amazon-style market research tools consistently undercount B2B intent.
What we built
A dedicated 9-stage pipeline that maps the deep-web conversation, every market, every language.
S1 SEED ── multilingual prompts per market (US, DE, JP, KR)
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S2 LLM_SCRAPE — ChatGPT cited-source mining
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S3 SERP+YT — DataForSEO Google + YouTube
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S4 SCRAPE — B2BAuthorityScraper + Oxylabs
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S5 FILTER — embedding + LLM relevance gate
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S6 ENRICH — Haiku + GPT-4o-mini extraction
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S7 ONTOLOGY — vendor / product / standard / application graph
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S8 BACKFILL — queryable repository (MCP-accessible)
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S9 SYNTHESIS — per-pack deep dives + per-market action briefs
Five focus packs, refreshed quarterly: CMOS sensors · industrial lenses · SDKs · AI anomaly detection · gravity wells (high-attention sub-markets).
The source map (8,000+ classified sources)
We mapped the source ecosystem you'd take 18 months to build internally:
- Tier A — Authority: GenICam.org, EMVA, JIIA, VDMA, A3 (AIA), vendor knowledge bases
- Tier B — Trade press: Vision Systems Design, EE Times, ITmedia Monoist (JP), Hellot (KR), Imaging & Machine Vision Europe
- Tier C — Engineer forums: ROS Discourse, Stack Overflow vision tags, Reddit r/computervision + r/robotics, vision-doctor.com, Naver Cafe (KR), Qiita & Zenn (JP)
- Tier D — OSS repos: GitHub Aravis, GenICam-harvesters, ROS drivers, pylon SDK contributions
- Tier E — YouTube channels: per vendor + Tier-A analyst channels
- Tier F — Standards bodies: EMVA Business Conference proceedings, GenICam SFNC working group, IEEE imaging publications
- Tier G — Market analysts: IndexBox, ReportPrime, Yole, TechInsights, ABI Research industrial vision
Across all tiers: continuously refreshed, classified, scored for authority and relevance.
The citation-forcing discovery
We measured per-market source-citation rates from ChatGPT across the pipeline:
| Market | Avg sources / item | 0-source rate |
|---|---|---|
| KR | 5.84 | 20% |
| JP | 3.57 | 54% |
| US | 2.95 | 63% |
| DE (raw) | 0.69 | 89% |
| DE (with citation forcing) | 4.06 | ~10% |
German ChatGPT skips browser tool 89% of the time on long matchup prompts — unless prompted with explicit citation forcing. We solved this in production. Without that fix, your DE B2B picture is functionally invisible.
This is the kind of trade craft you discover by running production systems at scale. It saves clients 8+ months of "DE is blind" reporting.
What the output looks like
After 9 stages on a single category (e.g. industrial CMOS sensors), the pipeline produces a multi-entity ontology:
- 38+ vendor nodes with mention counts, country presence, share of voice in technical forums
- 130+ products with vendor links + mention counts + sentiment per technical dimension
- GenICam / GigE Vision / CXP / SFNC standards with versions, modules, references
- 50+ vertical applications with use-case dimensions
- Decision drivers + pain points as enumerated entity classes
- Edges: vendor↔product, product↔standard, vendor↔application, vendor↔vendor
Queryable via your own ChatGPT or Claude assistant over MCP — ask "which 5 vendors are most cited for GigE Vision compliance in EU machine-vision forums" and get a sourced answer in seconds.
Example output: a matchup analysis
One pack output, sanitised: Edge-AI inference — Hailo vs Qualcomm Dragonwing vs Sony AITrios IMX900
| Vendor | Cited in EU forums | Sentiment range | Sample claim surfaced |
|---|---|---|---|
| Hailo | 47 cited threads | +0.42 | "Hailo-8 outperforms Coral edge-TPU on YOLOv8 latency" |
| Sony AITrios IMX900 | 31 cited threads | +0.51 | "Best-in-class on stacked-sensor architecture" |
| Qualcomm Dragonwing | 18 cited threads | +0.28 | "Robust low-power but limited model zoo" |
This output is monthly, refreshed automatically, available in 7 languages.
Why this matters
Every industrial brand running a marketing function is sitting on a paradox:
- Their buyers are sophisticated and well-informed.
- Their buyers leave detailed signals across deep-web communities.
- Their marketing tooling — built for consumer markets — can't see those signals.
The result is that industrial brands rely on quarterly trade-show insights, sampled custom panels, and gut feel from sales conversations. None of that is continuous. None of that is sourced. None of it is queryable.
Theia's Canon B2B engine fixes this. The same engine, with a different vendor list and a different prompt set, applies to any industrial / B2B brand.
If you sell to procurement officers, engineers, or standards-body specialists — and you can't currently answer "which 5 sources cite us most in our EMEA category" — book a 30-minute call.