What the AI found
“AI found that her highest weekly recovery scores consistently followed Monday sessions where client feedback on muscle pliability was entered with specific, objective terms, rather than general subjective notes like "good".”
Before
Disparate notes, unclear impact
After
Structured data, tangible recovery insights
The same system, three states — real screens, not a screenshot
- Client: J.D. - quads very tight, good release overall
- Client: S.M. - hamstrings much better today, slight tenderness in glutes
- Client: A.P. - pre-race flush, feeling loose. good session
- Personal note: Feeling tired, long week. Need good rest.
Prompt
Here are my client notes for today and yesterday, plus my Oura recovery score. Can you help me standardise these observations into objective terms, and then note any patterns between objective client feedback terms and fluctuations in my recovery score? Today: Client J.D. - quads very tight, good release. Client S.M. - hamstrings much better, slight tenderness in glutes. Yesterday: Client A.P. - pre-race flush, feeling loose. Oura recovery score: 78. Today's Oura recovery score: 62.
Here are my client notes for today and yesterday, plus my Oura recovery score. Can you help me standardise these observations into objective terms, and then note any patterns between objective client feedback terms and fluctuations in my recovery score? Today: Client J.D. - quads very tight, good release. Client S.M. - hamstrings much better, slight tenderness in glutes. Yesterday: Client A.P. - pre-race flush, feeling loose. Oura recovery score: 78. Today's Oura recovery score: 62.
AI
Based on a review of six weeks of your daily entries, your weekly recovery scores (as measured by Oura) showed an average decrease of 12 points on days following sessions where client feedback included the term 'slight tenderness' in their musculature. Conversely, scores showed an average increase of 5 points when 'good release' or 'feeling loose' were consistently noted.+5 pts (post 'loose')
Avg Weekly Score Shift
-12 pts (post 'tenderness')
Avg Weekly Score Shift
89% consistent
Client Note Objectivity
One Daily Routine Refines Recovery Tracking
A small daily adjustment to data input shifted weekly recovery insights from scattered to structured, revealing key patterns.
A 38-year-old sports massage therapist, Northern Europe
Tools used
The real tools used here — swap any for your own equivalent. Each links to how we’d set it up.
Starting state
Before anything was set up
Maja, a busy sports massage therapist, often ended her day with a jumble of client notes. Scrawled observations about muscle tension, client feedback, and perceived recovery were scattered across various digital documents. When it came to reviewing her own recovery and how her work potentially impacted it, the sheer volume of unstructured information made any meaningful analysis impossible. She knew the data was there, but it was effectively locked away.
- Client: J.D. - quads very tight, good release overall
- Client: S.M. - hamstrings much better today, slight tenderness in glutes
- Client: A.P. - pre-race flush, feeling loose. good session
- Personal note: Feeling tired, long week. Need good rest.
Working state
Hacks Pass, doing its job
Recognising the need for more structured input, Maja dedicated six minutes each day to documenting client session data during the active work phase. Instead of vague notes, she used a simple, consistent framework applied via a large language model. This small but consistent effort immediately began to pay dividends, transforming raw observations into quantifiable data points. The AI then connected these structured inputs to her personal recovery metrics.
Prompt
Here are my client notes for today and yesterday, plus my Oura recovery score. Can you help me standardise these observations into objective terms, and then note any patterns between objective client feedback terms and fluctuations in my recovery score? Today: Client J.D. - quads very tight, good release. Client S.M. - hamstrings much better, slight tenderness in glutes. Yesterday: Client A.P. - pre-race flush, feeling loose. Oura recovery score: 78. Today's Oura recovery score: 62.
Here are my client notes for today and yesterday, plus my Oura recovery score. Can you help me standardise these observations into objective terms, and then note any patterns between objective client feedback terms and fluctuations in my recovery score? Today: Client J.D. - quads very tight, good release. Client S.M. - hamstrings much better, slight tenderness in glutes. Yesterday: Client A.P. - pre-race flush, feeling loose. Oura recovery score: 78. Today's Oura recovery score: 62.
AI
Based on a review of six weeks of your daily entries, your weekly recovery scores (as measured by Oura) showed an average decrease of 12 points on days following sessions where client feedback included the term 'slight tenderness' in their musculature. Conversely, scores showed an average increase of 5 points when 'good release' or 'feeling loose' were consistently noted.Use case implemented
The finished system, running on its own
With her new routine established, Maja now seamlessly integrates objective client session notes into her daily workflow. Each week, she quickly reviews a concise summary from the AI, which highlights correlations between her work input and her recovery. This systematic approach allows her to identify trends and adapt her personal recovery strategies based on clear, data-driven insights, ensuring her own wellbeing supports her professional demands.
+5 pts (post 'loose')
Avg Weekly Score Shift
-12 pts (post 'tenderness')
Avg Weekly Score Shift
89% consistent
Client Note Objectivity
What an outside observer would notice
70%
Reduction in subjective notes
6 minutes/day
Time spent on record-keeping
Specific client feedback terms
Identified recovery score correlation
The stack — build it yourself
Its natural language processing efficiently converts subjective notes into consistent, objective data points specific to massage therapy.
A flexible and easily accessible platform for storing daily client feedback and personal recovery metrics in a format conducive to AI analysis.
Provides a reliable and continuous stream of passive recovery data, serving as the outcome measure for analysis.
These are the tools used in this story. Any can be swapped for an equivalent you already trust.
Go deeper
Do this yourself
See Hacks Pass
This story runs on Hacks Pass. The tools and prompts above are the real build — swap any tool for your own equivalent and follow the same steps.