The same system, three states — real screens, not a screenshot
From Hazy Notions to Concrete Actions for Energy
A practitioner refines client recommendations from anecdotal evidence to data-driven insights with a 10-day AI setup.
A nutritionist running a small EU practice
Tools used
Generic categories, not brands — use whichever tools you already trust. Each links to how we’d set it up.
Starting state
Before anything was set up
Before the 10-Day Challenge, the nutritionist relied on general dietary guidelines and client-reported energy levels, often leading to recommendations that were difficult for clients to implement consistently. Tracking was manual and sporadic, residing in scattered notes and email threads. This made it hard to pinpoint specific dietary triggers for energy fluctuations.
Working state
10-Day Challenge, doing its job
Mid-Challenge, she experimented with a client’s dietary log and energy ratings stored in a Google Sheet. She used a prompt to analyse patterns of macronutrient intake against reported energy levels. The AI’s output highlighted a surprising correlation, challenging her initial assumptions about the primary drivers of fatigue for this particular client.
Use case implemented
The finished system, running on its own
With the system established, the nutritionist now routinely processes anonymised client data through her AI helper. This provides her with specific, evidence-based insights to fine-tune dietary advice, leading to more actionable strategies. Clients receive clearer, personalised guidance, boosting their engagement and adherence to the plans.
What an outside observer would notice
Reduced by 15%
Time spent drafting client plans
Increased by 20%
Client feedback on plan clarity
3-5 key changes
Targeted dietary adjustments per client
Go deeper
Do this yourself
See how she made it happen
This story runs on 10-Day Challenge. Use whichever AI tools you already trust — the shape of the work matters more than the brand.