Clear context
Each case should clarify where the company started, what constraints mattered, and what kind of support was actually needed.
- starting point
- constraints
- goal
Results and real cases
AI case studies: real results across marketing, operations, and automation
Some work is easier to understand through results than promises. Here we collect context, the work itself, and the outcomes that became visible.
Details
Why the case studies matter
A result without context says very little. Each case should make the starting point, the work itself, and the final outcome easy to understand.
Concrete intervention
The point is not just to say AI was used, but to explain what was actually done across content, data, process, or channels.
- work performed
- stack
- operating rhythm
Readable outcome
Numbers, timing, and visible change make it easier to judge whether the work created real impact.
- numbers
- timeframe
- observable impact
Details
Two visible examples
Below are two different contexts where the work led to readable results, with very different operating dynamics and goals.
Stipendee.it
Organic growth support for an editorial B2C product through ongoing research, content, and optimization work.
- 100k+ monthly users
- research and content
- continuous optimization
Caffe Verri
More iteration across channels and better data reading to support stronger commercial decisions.
- +40% sales in 6 months
- data-driven reading
- commercial execution
A method that adapts to context
Every engagement changes with the channel, team, stack, and objective, but the point stays the same: find where to intervene and keep execution consistent.
- different context
- clear priorities
- operating rhythm
FAQ
FAQ
Can we publish this with only a few cases?
Yes, if the cases are clear and specific. Two strong proof points are better than a longer but vague list.
Is one page enough, or should each case have its own page?
At the beginning, one page is the pragmatic option. Once there is enough material, separate case pages become worthwhile.
Should we include precise numbers?
Whenever possible, yes. Numbers, timeframe, and intervention type make the page much more credible.
Other contexts we work in
Other contexts we work in
If you want to see where this way of working applies more specifically, these are the next pages to read.
AI agent for ecommerce
AI agent for ecommerce and Shopify teams: small MVP across content, SEO, GA4, Search Console, Ads, or store operations, with team handover.
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AI agent for marketing
AI agent for operational marketing: MVP across SEO, content, analytics, newsletters, or paid, with setup, training, and internal operation.
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AI training for companies
AI training for companies: practical workshops on Claude, ChatGPT, AI agents, role-based use cases, and self-managed adoption.
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Contact
If this kind of work could be relevant for your company, the next step is to look at your actual process and bottlenecks.
Send us your stack, team, and the workflow that currently gets stuck. We will reply with a possible first MVP and what your team needs to operate it internally.