Four industries, four starting points
Pick the one closest to your situation
Each one states the challenge, what we did, and what was measured.
CASE 01 · B2B precision manufacturer
Turning a spec sheet into answers an AI will cite
The challenge
Nearly all site traffic came from brand terms — only people who already knew the company could find it. Buyers searching "who can supply this specification" or "how do I choose this material" before purchasing were being intercepted almost entirely by a handful of competitors. Every unfamiliar buyer was being handed to the other side.
What we did
- Diagnosed the brand versus non-brand traffic split and confirmed the ceiling was a non-brand vacuum
- Worked back from procurement decisions to the educational keywords behind them
- Turned the facts on the spec sheet — certifications, specifications, lead times — into content that can be cited directly
- Strengthened the technical foundations and structured data in parallel
Non-brand organic traffic
〔TBC〕
Target terms appearing in AI citations
〔TBC〕
Technical health improvement
〔TBC〕
CASE 02 · An aesthetic clinic in northern Taiwan
A highly regulated field still has to be chosen by an AI
The challenge
Prospective patients always ask an AI "who is good for this treatment" and "what is this clinic like" before deciding — and this clinic barely appeared in the answers. The expertise and the cases existed; they had simply never been organised into trustworthy content an AI would cite. It is also a heavily regulated field, so content cannot overstate outcomes.
What we did
- Built a hard compliance gate so every piece meets medical advertising rules
- Turned clinical expertise, aftercare guidance and real cases into answers an AI can put forward
- Produced variants for what different AI engines favour, with a citation forecast before publishing
- Kept measuring share of mentions across the major AI answers and calibrated month by month
AI citations on decision-stage keywords
〔TBC〕
Non-brand organic traffic growth
〔TBC〕
Where consultation bookings come from
〔TBC〕
CASE 03 · A cross-border e-commerce brand
Several language markets — where should the effort go
The challenge
Shoppers ask an AI "how do I choose in this category, which brand is better" before buying, and this brand was not in the answer. They wanted to enter several language markets at once but had no read on how search intent and competitive intensity differed between them.
What we did
- Ran search intent analysis per language market to find the high-intent, low-competition junctions
- Turned product advantages into answers an AI recommending products will cite
- Ordered the markets by commercial value so effort was not spread thin
- Built a data flywheel so citation data from each market strengthened the next
Non-brand traffic in target markets
〔TBC〕
Cross-language AI citation coverage
〔TBC〕
Precision keyword positions held
〔TBC〕
CASE 04 · A B2B niche service provider (ESG / professional services)
Demand is growing, but competitors own the answers
The challenge
Demand was growing quickly, yet when prospective clients searched "what is this service", "how is it done" and "who does it", the answers were owned almost entirely by a few competitors. Brand terms made up an unusually high share of traffic — meaning the company could only catch clients who already knew it.
What we did
- Established that demand was being intercepted exclusively by competitors, and set educational non-brand keywords as the main growth source
- Estimated commercial value conservatively from the transferable share of competitors' non-brand traffic, and ordered the work by it
- Deployed teaching-led knowledge clusters aimed at the question stage of the search
- Ran AI citation work and technical foundations together, so the content stays an asset long term
Traffic on educational non-brand terms
〔TBC〕
Target terms appearing in AI citations
〔TBC〕
Change in where enquiries come from
〔TBC〕