5 days
From first design to a live MVP
WiseWoman
A daily planning system that reads the body, not just the calendar. It maps tasks to life roles, reads cycle-phase energy, and builds one schedule across work and personal life.
images/wisewoman-hero.webp
RoleProduct Designer + Builder
Timeframe5 days · Sept 2025
StackLovable · Gemini 2.5 Flash · Supabase
TypePersonal project · Live MVP
Build
AI functions
6
Each mapped to rules, an LLM or deterministic logic
Status
Live
MVP in use with real users
TL;DR
The whole story, in five cards.
Problem
Planners assume one role and constant energy. Energy shifts across the cycle, and work, home, care and self all pile into one flat list.
Approach
A planning chain, not a to-do list: cycle phase, energy, role context and task grouping come together as one daily plan.
AI
Rules handle 90%+ of the work. The LLM steps in only where rules fail, free speech and tone. The system assists, the user decides.
Build
5 days from first design to a live MVP with real users, on Lovable, Gemini 2.5 Flash and Supabase.
Takeaway
Designing for a body that changes means a plan that flexes, not one that punishes the low-energy days.
01 · The Problem Space
Where productivity systems break for women.
Planners optimise for task completion. They assume a flat, steady person with one role and constant energy. That person does not exist.
01
Energy is treated as constant
A to-do list assumes the same capacity every day. Energy shifts across the cycle, and the plan never accounts for it.
02
One flat list, many roles
Work, home, care, self. Tasks from every role pile into a single list with no sense of context or boundary.
03
Setup is the real cost
Most tools demand structure before they give value. The cognitive load of managing the planner becomes its own task.
Gap Analysis · what apps deliver against what women actually need
What apps do
What they need
Energy fluctuatesacross the cycle
Multiple roles at oncework, home, care, self
Cognitive load is the costnot the task itself
Accountability matterswithout the guilt
High-accountability planners are not the answer.
Streaks, guilt mechanics, and rigid daily quotas punish the exact days when capacity is lowest. The system needs to flex with the person, not demand the person flex to the system.
02 · Design Process
Five days, four moves.
01
Problem Validation
Built for personal use first. Secondary research came second, to test the hypothesis rather than invent it.
02
Define Direction
Set the planning chain and the assist-not-automate principle before any screen existed.
03
Build MVP
Vibe-coded on Lovable. Rules-based scheduling over full LLM to keep the MVP stable in a five-day window.
04
Iterate & Refine
Tightened the AI routing, separated voice and text systems, and fixed tone on energy guidance.
03 · Research & Pattern Synthesis
Small sample. Honest method.
No inflated numbers. Patterns were drawn from Reddit threads on productivity and women's planning, 3 direct conversations, and 12 app store audits across cycle and productivity tools. The signal was consistent enough to act on, not large enough to claim more than it was.
4.8 hrs
of unpaid work carried by women each day, on average.
OECD
7 in 10
mothers report cognitive overload from juggling roles.
IJIP
5–10%
typical long-term retention for productivity apps.
Industry benchmark
In their own words · app store and forum voices, unedited
Managing the tool is a work !Productivity app review
PMS is a thing !!Reddit, r/productivity
Not for personal managementTask app review
Organising the task is a painApp store review
04 · Market Signal & Competitive Landscape
Two markets that never met.
FemTech tracks the body. Productivity tools schedule the day. No category leader connects the two.
Cycle apps know energy shifts but do nothing with it. Planners structure the day but stay blind to the person using them. WiseWoman sits in the gap between them.
$56.5B
FemTech market, 2024
$18B+
Productivity software
$2B+
FemTech VC, recent cycle
~$300M
Serviceable opportunity
PlayerWhat it doesWhat it misses
Flo · Clue
Tracks the cycle with depth and accuracy.
No planning layer. Insight never becomes action.
Phase · Essence
Cycle-aware lifestyle and wellness guidance.
No task or scheduling system.
Todoist · Notion
Powerful, flexible planning and task management.
No body awareness. Treats every day the same.
WiseWoman
Maps tasks to roles, reads cycle energy, builds one plan.
Connects both. Body and day in one system.
05 · Core System Logic & Product Strategy
A planning chain, not a to-do list.
Every task moves through five stages before it reaches the schedule. The system reads context first, then plans.
Cycle Phase
Where she is in the cycle
Energy Pattern
Expected capacity
Role Context
Which life role a task belongs to
Task Grouping
Clustered by role and weight
Daily Plan
One schedule, work and personal
FunctionInputSystem
Task Capturestructured text input
Text
Rule-Based · AITaskCategorizerruns client-side, no API call
Task Captureunstructured speech
Voice
LLM · Geminirules cannot split free speech
Task Categorisation
Task
Rule-Based + learning loopremembers user corrections
Energy Guidance
Cycle phase
LLM · Geminitone and interpretation
Schedule Structuring
Tasks + energy
Deterministic algorithmpredictable, not generative
Behaviour Memory
Activity
Pattern memorytracks from real activity
The system assists. The user decides.
The LLM never schedules autonomously and never overrides a task. It interprets and suggests. Every final decision stays with the person.
06 · AI Architecture
Two systems, chosen for two jobs.
Text and voice do not share a pipeline. Each input type routes to the system that fits it. This is the most important technical decision in the product.
Voice to Task · LLM path
1Web Speech API captures spoken input in the browser.
2Transcript is sent to an edge function.
3Gemini parses intent and splits multiple tasks from one utterance.
4Returns a structured task array.
5Tasks render in the UI for review.
Energy Guidance · LLM path
1Cycle phase and context are gathered.
2Sent to Gemini for interpretation.
3Two outputs: an energy read and a guidance message.
4Displayed as a phase-aware prompt, never a command.
Hybrid strategy · where work actually happens
Rules handle the everyday: capture, categorisation, scheduling.
LLM only where rules fail: free speech, tone.
Token economics
Text capture0 tokens
Categorisation0 tokens
Scheduling0 tokens
Voice parsingLLM call
Energy guidanceLLM call
$0
Cost to the end user. The platform absorbs all model cost through the Lovable AI Gateway. Routing most interactions through rules keeps that absorbable.
07 · Experience
Experience Strategy & Interaction Design
WiseWoman turns cycle awareness into everyday planning. Each screen cuts the effort of planning, matches the day's workload to the energy available, and structures tasks without manual setup.
Branding and Colours
Two violets carry the moon mark and key actions, a near-white carries text, and four deep indigo neutrals build the dark surfaces.
images/wisewoman-app-icon.svg
#8B5CF6
#B08CF5
#F2F2F2
#1A1832
#181630
#252538
#29293D
images/wisewoman-logo-on-mobile.webp
images/wisewoman-cycle-symbol.svg
Cycle Symbol in the app logo
The WiseWoman logo uses the moon as a symbol of cyclical change. Just as the moon moves through visible phases, energy and focus shift across the menstrual cycle. The mark represents adaptive planning that honours rhythm instead of forcing constant output.
Intentional Onboarding Design
The onboarding is intentionally structured to collect only high-signal inputs required for cycle-aware planning: basic identity, cycle data, and role context. Each step directly feeds the planning logic: cycle prediction, capacity mapping, and role-based task grouping. No excess preferences, no unnecessary profiling. The goal is fast personalisation with minimal friction.
images/wisewoman-onboarding.webp
images/wisewoman-scheduling.webp
Intelligent Task Capture
Users add tasks via text or voice. The system auto-categorises them by role and intent, then offers cycle-aware nudges to guide timing and workload.
Final scheduling remains user-controlled.
Task Creation & Intelligent Scheduling
Task capture is designed to reduce cognitive overhead from the start. Typed tasks are categorised by client-side rules and spoken tasks are parsed by Gemini, so every task is auto-mapped to a life role with a suggested energy intensity. This removes manual tagging and micro-decisions at the point of entry.
Over time, user edits and corrections act as feedback signals, refining classification and scheduling suggestions. The system gradually aligns with individual planning habits, making structuring feel increasingly intuitive rather than configured.
Habit Reflection & Tracking
The habit section translates completed tasks and synced events into visible behavioural patterns. Instead of manually logging habits, the system derives insight from actual daily activity across roles.
It surfaces where time is spent, how workload is distributed, and what routines are forming over days and weeks. The goal is awareness, not streak pressure: helping users understand their lived patterns rather than optimise obsessively.
images/wisewoman-habit-reflection.webp
Your Activities Dashboard
Your Activities tracks completed tasks and synced events across day, week, and month views. It visualises where time goes across roles without requiring manual logging. Users can also add small self-care actions from here.
images/wisewoman-ui-showcase.webp
08 · Reflections & Future Scope
What held, what fought back.
What Worked
+
The hybrid AI strategy kept the MVP stable and cheap to run.
+
A rule engine for text capture removed latency and token cost entirely.
+
The deterministic scheduler made the system predictable, so users trusted it.
+
Building for personal use first kept the scope honest and the hypothesis testable.
What Was Hard
–
Splitting voice and text into two systems took several iterations to get right.
–
Cycle-phase energy guidance needed careful tone, not just accuracy.
–
The five-day scope forced cutting richer pattern recognition.
–
A personal hypothesis was hard to validate with limited secondary research.
–
Keeping the LLM assistive, never autonomous, took deliberate limits at every step.
Current state
Live MVP with real users. Vibe-coded on Lovable with Google OAuth and Supabase. Functional end to end: capture, categorisation, energy guidance, and scheduling.
Future scope
Native deployment, advanced pattern recognition from longer activity history, and richer cycle-signal integration to sharpen the energy read.
It does not ask her to plan harder. It plans with her.
Need a designer to bring clarity to your workflows?Talk to me.
Open to product design roles and contract work.
Download resume- {
- "name": "Shabnam Raghavan",
- "title": "Product Designer",
- "email": "shabnam.rs.work@gmail.com",
- "phone": "+91 70226 65338",
- "link": "linkedin.com/in/shabnamraghavan"
- }