Prototype
AI Personal Agent for G-Suite & WhatsApp
Problem
Professionals run their day across five disconnected surfaces — inbox, calendar, docs, tasks, WhatsApp — and spend more time context-switching between them than acting.
Solution
A conversational agent, reachable entirely through WhatsApp, that reads intent from a message, routes it to the right Google API (Gmail, Calendar, Drive), executes the action, and replies in plain language — no app-switching required.
Mechanism
Intent detection → API call → LLM-formatted response, with the agent holding enough context to handle a full back-and-forth, not just single commands.
Key Features
Email summarizationSmart reply generationCalendar managementWhatsApp assistantMeeting schedulingDaily briefingsReminder automation
My Role
Product discoveryWorkflow designAI agent planningAPI integration strategyFeature prioritization
Roadmap
- ✓ Gmail integration
- ✓ Calendar automation
- ◻ Google Drive search
- ◻ Voice assistant
- ◻ Multi-agent collaboration
Prototype
AI Personal Lifestyle Agent
Problem
Fitness, budgeting, travel planning, and habit tracking each live in a separate app, so no single system actually knows the user well enough to help proactively.
Solution
One assistant with persistent memory of the user's goals and patterns, generating recommendations and nudges on its own rather than waiting to be asked.
Mechanism
Goal input → behavior/activity analysis → recommendation engine → proactive notification — the loop runs continuously, not on-demand.
Key Features
Fitness trackingMeal planningExpense monitoringHabit trackingTravel planningSmart remindersGoal progress
My Role
Product visionUser persona definitionFeature roadmapAI experience designMVP planning
Roadmap
- ✓ Lifestyle dashboard
- ✓ Smart recommendations
- ◻ Wearable integration
- ◻ Voice assistant
- ◻ Family profiles
Prototype
OSRM Route Optimization for Quick Commerce Riders
Problem
Standard navigation optimizes for shortest distance to one destination — it has no concept of multi-order sequencing, rider capacity, or delivery priority.
Solution
A routing engine purpose-built for quick commerce: sequences multiple stops by priority and capacity, not just proximity.
Mechanism
Built on OSRM + OpenStreetMap rather than a metered maps API — routing and ETA calculation run on open map data, so traffic-aware ETA estimation doesn't incur a per-call cost the way a commercial maps API would at delivery-fleet volume.
Key Features
Multi-stop optimizationETA predictionRoute sequencingDistance matrixRider assignmentDelivery prioritizationMap visualization
My Role
Product strategyRouting logic designWorkflow definitionAPI planningOperations researchKPI definition
Roadmap
- ✓ Route optimization
- ✓ ETA engine
- ◻ Live traffic integration
- ◻ Fleet analytics
- ◻ AI route learning
Prototype
AI Customer Support Agent
Problem
Support teams burn hours answering the same handful of questions on repeat, which delays response time on the cases that actually need a human.
Solution
An agent that answers directly from a knowledge base and escalates automatically the moment its confidence drops below a threshold — so humans only see what genuinely needs them.
Mechanism
Query → vector search against knowledge base → LLM response → confidence check → auto-escalate or reply.
Key Features
AI chat supportKnowledge base searchTicket categorizationSmart escalationConversation historyMulti-language support
My Role
Customer journey mappingProduct discoveryAI workflow designPrompt strategyFeature planning
Roadmap
- ✓ AI chat
- ✓ Knowledge search
- ◻ CRM integration
- ◻ Voice support
- ◻ Agent analytics
Prototype
AI NPS & Customer Feedback Analyzer
Problem
Feedback arrives scattered across surveys, app reviews, and support tickets — nobody reads all of it, so recurring issues get missed until they're already a pattern.
Solution
Every feedback source feeds one pipeline that scores sentiment, clusters recurring themes, and ranks them by frequency — turning raw text into a prioritized action list.
Mechanism
Feedback ingestion → sentiment scoring → topic clustering → ranked dashboard.
Key Features
Sentiment analysisTheme detectionNPS dashboardTrend analysisAI recommendationsExecutive reports
My Role
KPI definitionProduct discoveryDashboard planningAI workflow designPrioritization
Roadmap
- ✓ Sentiment analysis
- ✓ Dashboard
- ◻ Live integrations
- ◻ Competitor benchmarking
- ◻ Predictive churn
Prototype
AI Resume & Job Fit Analyzer
Problem
Candidates get rejected by an ATS before a human ever reads their resume, with zero visibility into why.
Solution
Scores a resume directly against a specific job description before submission — surfaces missing keywords and skill gaps, then rewrites the weak sections.
Mechanism
Resume + JD → embedding-based similarity scoring → gap analysis → targeted rewrite suggestions.
Key Features
Resume parsingATS scoreSkill gap analysisKeyword suggestionsResume rewriteExport report
My Role
Product discoveryPRD creationAI workflow designUX planningFeature prioritization
Roadmap
- ✓ ATS analysis
- ✓ AI suggestions
- ◻ Interview preparation
- ◻ Cover letter generator
- ◻ Job recommendations
Prototype
AI Lead Enrichment Tool
Problem
Reps lose real selling time to manual research — company background, decision-makers, context — before every single outreach.
Solution
Automates that research end-to-end and writes the enriched profile straight into the CRM, so the rep opens a ready-to-use lead instead of a blank one.
Mechanism
Lead input → company/contact enrichment → AI-generated summary → CRM write-back.
Key Features
Company researchContact enrichmentAI lead summaryCRM integrationPersonalized outreachLead scoring
My Role
Product strategyUser researchFeature definitionWorkflow designIntegration planning
Roadmap
- ✓ Lead research
- ✓ AI summaries
- ◻ Email personalization
- ◻ Sales analytics
- ◻ Automated outreach