B2B machinery manufacturer: from 50 to 300 monthly GEO inquiries in three months.
Buyers could only learn about the manufacturer’s products after contacting sales. We moved that knowledge onto crawlable pages, where buyers and AI systems could find it earlier.
PROJECT-REPORTED · B2B MANUFACTURING · INDUSTRIAL MACHINERY · 3 MONTHS
The website had few useful content assets, and much of the product and capability information was only available after contacting the team. Over three months, we brought that information onto crawlable pages so buyers and AI systems could understand the manufacturer’s capabilities earlier in their research. Project-reported monthly GEO inquiries moved from 50 to 300, and reported AI impressions reached three times the baseline. Inquiries and AI impressions were tracked separately.
50 → 300
monthly GEO inquiries over three months
6×
the starting monthly inquiry count (calculated: 300 ÷ 50)
3×
reported AI impressions relative to the baseline
3 months
from the starting point to the reported result
Before
Knowledge behind sales
Product information was only available after contacting the team.
50
During · 3 months
Three layers of work
Knowledge written up, published on crawlable pages, built for research.
3
After
Monthly GEO inquiries
Reported AI impressions also reached 3× the baseline.
300
What the numbers show
Three months after the work began, project-reported monthly GEO inquiries had moved from 50 to 300.
50 → 300
monthly GEO inquiries
- 50 at the start
- 250 added by the end
Monthly inquiries grew six times
300
monthly GEO inquiries at the end of the project
Project-reported monthly inquiry endpoints over three months. 6× is calculated as 300 ÷ 50.
Where we started
The website had few useful content assets. Buyers could only access much of the product and capability information after contacting the team.
What we changed
We ran 3 layers of work. They are the same layers we build for every client.
- The knowledge layerProduct and capability information that had lived behind a sales conversation was written up as useful content assets.
- The access layerThat information was published on crawlable pages, so search engines and AI systems could read it directly.
- The buyer layerPages were built for the research stage, so a buyer could understand the manufacturer’s capabilities before the first sales conversation.
What happened
AI impressions reached three times the baseline
3×
reported AI impressions vs. the baseline
Relative comparison, baseline = 1×. Tracked separately from inquiries.
| Measure | Starting point | Reported result | Change |
|---|---|---|---|
| Monthly GEO inquiries | 50 | 300 | 6× (calculated) |
| Reported AI impressions | 1× (baseline) | 3× | 3× |
How we measured this, and how to read it
Instrument.
Project reporting for monthly GEO inquiries and AI impressions, tracked as two separate measures over three months.
What is excluded.
The figures do not establish closed revenue or the contribution of any single tactic. Absolute impression counts were not supplied, so impressions are shown relative to the baseline.
The measurement setup behind every figure on this page
3
months from starting point to result
2
measures, tracked separately: inquiries and AI impressions
1×
baseline used for the relative impressions comparison
B2B
machinery manufacturing, client anonymized
Project-reported monthly inquiry endpoints over three months. Inquiries were tracked separately from AI impressions; the figures do not establish closed revenue or the contribution of any single tactic.
What these figures do not show.
They do not establish closed revenue, a guaranteed outcome or the contribution of any single tactic. AI platforms decide their own answers, and results vary by prompt, context and time.
Who the client is.
The client is anonymized. Figures come from the project’s own reporting, using its own measurement scope and period.
Questions buyers ask us about this
How long until we see the first signal?
Technical changes such as crawler access and structured data can be picked up within weeks. Content and outside mentions take longer, because an engine has to find and read them first. We agree the review points before work begins.
How do you know a lead came from AI?
We look in three places: analytics, where assistants such as chatgpt.com and perplexity.ai appear as traffic sources; the inquiry form, where we ask how people found you; and the first call, where buyers often name the question they asked.
What exactly is the KPI?
Visibility on an agreed set of buyer questions: the share of AI answers to those questions that name your brand. The question set is agreed before the work starts and kept stable, because changing the questions moves the number without changing what the engines say.
Do you guarantee results?
No. AI platforms make their own decisions, and answers vary by prompt, context and time. We show your baseline first, keep the question set stable, document what we observe and review the numbers with you every month.
Will our results look like these?
Not necessarily. Each figure here comes from one project, with its own market, starting point, scope and measurement period. It shows what the work involved, not a forecast for your business.
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