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Health & Fitness AI Case Study
Kenstin Technologies refined a health AI pipeline: exercise plans respect physiology and profile context via retrieval-grounded generation, and meal plans honor dietary rules from first retrieval through final output-fixing the leaks that broke trust.
10
Weeks
100%
Completion
4
Tech Used
Why teams choose this build
Concrete scope signals from the engagement-structured for evaluation, not vanity metrics.
Personalization axes
Profile + diet + goals
Grounding approach
Retrieval + ranked meals
Quality bar
Constraint-first pipelines
Project foundation
We start with scope clarity, challenge mapping, and execution guardrails before implementation begins.
The client’s fitness product promised personalization at scale: workouts and nutrition guidance tailored to goals, constraints, and demographics. Kenstin focused on the hardest parts-keeping recommendations coherent when users varied widely, and ensuring diet preferences were not “mostly” followed but consistently enforced.
Exercise outputs collapsed toward generic plans across user profiles, failing to differentiate in ways users expect from a serious coaching product. Meal recommendations intermittently ignored dietary constraints-an immediate credibility failure in nutrition. Both issues pointed to weak grounding and ranking, not just prompt wording.
Scope & timeline
Directional highlights for this anonymized portfolio entry-useful for understanding depth of work, sequencing, and ownership.
Delivery window
10 weeks
Personalization axes
Profile + diet + goals
Grounding approach
Retrieval + ranked meals
Quality bar
Constraint-first pipelines
Engagement note
The team executed in tightly defined milestones with weekly validation loops, keeping scope, quality, and rollout confidence aligned throughout delivery.
Weeks 1–2
Issue reproduction
Instrumented exercise and meal flows; isolated where gender and diet constraints were dropped.
Weeks 3–5
Exercise retrieval layer
Added vector-backed context and controlled generation for physiology-aligned workout plans.
Weeks 6–8
Meal ranking & diet workflow
Rebuilt diet-first filtering, substitutions, and best-match search before final generation.
Weeks 9–10
Evaluation & launch
Human review batches, automated checks on constraints, and rollout monitoring for regressions.
Execution
We separated grounding from creative generation. For exercise, a vector-backed context layer retrieves gender- and goal-relevant templates and knowledge before synthesis.
For meals, diet constraints became a hard filter and ranking signal up front, so the model could not accidentally bypass them late in the pipeline. Evaluation focused on edge profiles and strict dietary scenarios to verify consistency across diverse user cohorts.
Exercise now combines retrieval plus controlled generation for profile-specific outputs. Meals use best-match search and a diet-first workflow so restrictions propagate through selection, substitution, and final text.
The stack behaves like a system with guardrails, not a single prompt asking nicely. Instrumentation and quality checks were added to catch recommendation drift before it became visible to end users.
Users received materially more relevant workouts and meals, with fewer contradictions and higher trust in the product’s personalization story-supporting retention and reducing support complaints about “wrong diet” outputs. Product teams gained a stronger basis for ongoing tuning because failure cases were captured and categorized more clearly.
The stack is selected for reliability, maintainability, and production readiness.
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