Wellness & Fitness · Personalized fitness plans
The PDF your members got at sign-up is still the program they're on
Banao builds adaptive fitness plan AI that updates each member's program based on actual attendance, session logs, goal progress, and physiological signals — not the static targets set at induction that no longer fit where they are.
The system runs on member data your platform already holds: check-ins, class history, body composition records, and app interactions. It generates and revises plans that trainers can review and override, so each member gets a program that responds to them and the trainer stays in the loop.
Hummcare— member personalization and engagement logic built into a live wellness platform.
The first call is free · 45 minutes · no obligation
What we build
What a Banao fitness plan AI deployment includes
Personalization is not a single feature. It is goal intake, progress tracking, adaptive logic, trainer review, and member-facing delivery — we build and integrate the full stack.
Goal-matched program generation
Each new member's program is generated from their stated goals, fitness baseline, available time, and any contraindications logged at intake — not a tier-one template applied to everyone who ticked 'weight loss'.
Attendance-driven plan adaptation
When a member misses three sessions in a week, their plan recalibrates rather than accumulating a deficit they can never close. Frequency, volume, and progression pace adjust to what the member is actually doing.
Progress signal integration
Body composition scans, wearable data, in-app performance logs, and check-in frequency feed a single member profile. The plan engine reads all of them, not just the one metric the front desk tracks today.
Trainer review and override layer
Every AI-generated or AI-revised plan goes through a trainer-facing review queue. Trainers approve, modify, or replace the recommendation — the model accelerates their workload, not replaces their judgment.
Member-facing plan delivery and check-in
Members receive their current program through your existing app or a white-label interface — with session-by-session instructions, logging prompts, and weekly progress summaries. No separate tool to install.
Nutrition alignment for platforms that carry it
For wellness platforms with a nutrition product, the plan engine links caloric and macro targets to the training load — so a high-intensity week is not paired with a deficit that stalls recovery.
Receipts
Where personalized plan AI is already running
Metrics shown dotted (··) are being confirmed in our case-study verification process — published only once verified.
Member personalization and engagement built into a live wellness platform
Hummcare needed a member experience that went beyond generic wellness content. Banao built personalization and engagement logic that matches each member to a program based on their profile, activity, and goals — integrated into the live platform.
Dogfooding
We run our own operation on AI before deploying it on yours
Banao is a ~300-person engineering company. InterviewGod screens every engineering hire we make — assessing candidates against a defined competency profile, not a gut call. Vikaas runs our own demand-generation pipeline. We have built AI that operates on high-stakes decisions inside our own business.
A personalized plan system has to adapt correctly, in production, with real members. The standard we hold ourselves to — AI that works on our own people before it goes near yours — is the same standard we hold any fitness personalization build to.
Screens Banao's own engineering hires against defined competency profiles every week.
Runs Banao's own demand-gen and re-engagement pipeline end to end.
The honest version
When AI-driven plan personalization is not the right investment
Personalization AI is not always the highest-value build. We will say so:
- Low member volume: below roughly 500 active members with logged session data, the personalization signal is too thin to improve on what a competent trainer does manually. We will tell you the threshold.
- No digital session logging: if training sessions are not logged in a system — paper cards, verbal-only updates — the plan engine cannot adapt to what it cannot see. Data hygiene comes before the model.
- Trainer resistance: a plan AI that trainers ignore or override en masse is noise. If there is no appetite to integrate AI into the trainer workflow, the problem is change management, not technology.
- Single-tier membership: if every member is on the same program by design — a fixed group class product, a single bootcamp format — there is no plan to personalize. We will tell you when the product model does not support the build.
How we start
How we start — audit the data before we write the model
We assess your member data quality and trainer workflow before quoting a personalization build. If the data is not there, we tell you.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit your session logs, goal intake records, and check-in history to establish what the personalization engine can actually learn — and where the data gaps are. We hand back a feasibility assessment and a build scope. Yours to keep. If you proceed, the Sprint fee is credited against the build.
- 02
Build
Model training on your member history, integration with your platform or app, and a trainer-facing review queue. Nutrition integration is included where your platform carries it.
- 03
Production & continuous adaptation
Live scoring and plan revision in production, with trainer workflow dashboards and drift monitoring. Quarterly reviews against member retention and engagement outcomes.
FAQ
Frequently asked questions
What member data does the plan engine need to start?
At minimum: session logs with dates and formats, a goal or intake record per member, and check-in history. Wearable and body composition data improve accuracy but are not required to start. The Discovery Sprint establishes what your specific dataset supports.
Does it replace trainers or reduce headcount?
No. The system accelerates the trainer's plan creation and revision workload — generating a draft that the trainer reviews, adjusts, and approves. A trainer who manages forty members can review AI-generated updates rather than writing forty plans from scratch each month.
How does the plan adapt when a member misses sessions?
The engine detects the attendance gap and recalibrates volume and progression pace to meet the member where they are, rather than assuming they will catch up. A flagged gap also queues a retention alert so the front desk can reach out.
Can we run this alongside our existing booking and app platform?
Yes. We integrate with your existing member management, booking, and app infrastructure rather than replacing it. The plan engine reads from and writes to your current data layer, so members and trainers keep using the tools they already have.
How do we measure whether personalization is improving retention?
We instrument an engagement and retention measurement layer from the start — comparing 60-day and 90-day retention rates for members on adaptive plans against those on static programs. This is part of the production deliverable, not a follow-on project.
Get started
Show us your member data — we'll tell you what the plan AI can learn
Bring a sample of your session logs and goal intake records to a 45-minute call. We will tell you whether personalized plan AI is worth building for your member base — and what it would take.
Book a Discovery Sprint