Vextera Impact Dashboard
Leading the 0-to-1 development of Logistics SaaS in Dubai, transforming manual warehouse chaos
into API-driven automation.
About My Role
I led the 0-to-1 build of the WMS/OMS stack and owned the full lifecycle from carrier negotiation
to warehouse workflow design. The mission was operational transformation, not just software.
What I Did (The 4 Pillars)
- 0-to-1 Platform Build: Shipped the WMS/OMS backbone and roadmap.
- API Automation: Automated 70% of dispatch tasks via carrier APIs.
- Warehouse Logic: Boosted floor productivity by 30% with zone/batch picking.
- Strategic Negotiation: Used volume data to improve unit economics.
What I Learned
- Speed is a byproduct of accuracy.
- Scale comes from removing decisions, not asking people to move faster.
- APIs automate data transfer; zone picking optimizes physical movement.
0-to-1 WMS/OMS
API Automation
Warehouse Optimization
AI Forecasting
Dispatch Errors
40% reduction
Picking Productivity
30% boost
Dispatch Automation
70% automated
Forecast Accuracy
30% lift
Case Study Decks
3 carousels · 8 slides each
Deck 1 · Logistics Automation (Shipping)
Project: Automated Shipping Workflow
Role: Product Growth Manager
Key Metric: 40% Reduction in Dispatch Errors
Eliminating the "Human Error" in Shipping
Project: Automated Shipping Workflow (API Integration)
Role: Product Growth Manager
The Operational Bottleneck
- Rapidly scaling logistics environment in Dubai.
- Manual data entry for labels and tracking.
- High dispatch errors led to returns and support friction.
What I Noticed
The floor team was blind. Every tracking number typed was a chance for error.
We had a high dispatch error rate, leading to angry customers and stressed staff.
- Bottleneck: Manual data entry
- Risk: High dispatch error rate
Technical Strategy
- Goal: zero-touch label generation.
- Carrier API integrations (DHL, FedEx, local couriers).
- Pack Complete triggers label and auto-prints.
The Path I Chose
I led a 0-to-1 integration of carrier APIs directly into our WMS. Scanning a barcode
generated the label, pushed tracking, and closed the order. We moved from data entry
to data flow.
- Stack: Carrier API integration
- Focus: Workflow automation
Execution & Rollout
- Mapped APIs for top 3 carriers by volume.
- Configured thermal printers for server-side jobs.
- Trained floor staff on scan-to-print workflow.
The Human Outcome
We automated 70% of manual shipping tasks and cut dispatch errors by 40%. Warehouse
morale improved as mistakes stopped being punished. The floor moved from high stress
to high flow.
- Automation: 70%
- Errors: -40%
Long-Term Impact
- Handled 2x peak volume during Ramadan without new headcount.
- Accurate volume data enabled better carrier negotiations.
Deck 2 · Warehouse Efficiency (Picking)
Project: WMS Picking Logic Redesign
Role: Product Growth Manager
Key Metric: 30% Boost in Picking Productivity
Optimizing Physical Movement: Zone & Batch Picking
Project: WMS Picking Logic Redesign
Role: Product Growth Manager
The "Walking" Problem
- Pickers walked miles daily but output was low.
- Single-order picking forced repeated aisle visits.
- Maximum effort for minimum output.
What I Noticed
Pickers zig-zagged across the floor, retracing steps to the same aisle multiple times
an hour. The software treated every order as an island, ignoring warehouse geography.
- Problem: Inefficient routing
- Result: Low pick rate
Solution Architecture
- Batching: group 10-20 orders with shared items.
- Zoning: assign pickers to specific aisles.
- Algorithm computes optimal snake path.
The Path I Chose
I implemented zone and batch picking so a worker could walk an aisle once and pick
for 10 orders at once. We optimized the human path, not just the order queue.
- Strategy: Physical optimization
- Logic: Zone/batching
Change Management
- Moved staff from paper lists to scanners.
- Reorganized high-velocity SKUs via ABC analysis.
- Trained teams on optimized pick routes.
The Human Outcome
Productivity surged by 30% just by removing wasted walking. Staff were less fatigued,
and we processed higher volumes without adding headcount.
- Productivity: +30%
- Efficiency: Optimized routing
Key Takeaway
- Software must mirror physical reality.
- Lowered cost per order by maximizing labor utilization.
Deck 3 · The 0-to-1 Platform Build & AI Forecasting
Project: 0-to-1 WMS/OMS & AI Forecasting
Role: Product Growth Manager
Key Metric: 30% Increase in Prediction Accuracy
From Chaos to System: Launching the WMS/OMS
Project: 0-to-1 WMS/OMS & AI Forecasting
Role: Product Growth Manager
The Pre-Product State
- Fragmented tools and spreadsheets.
- No single source of truth for inventory.
- Frequent stockouts and overselling.
What I Noticed
The system was a fragmented mix of spreadsheets and disconnected tools. There was no
single source of truth for inventory, and we were guessing stock levels daily.
- Problem: No central system
- Pain: Fragmented data
The 0-to-1 Build
- Owned lifecycle from requirements to deployment.
- Real-time inventory syncing across channels.
- AI-driven forecasting for demand prediction.
The Path I Chose
I owned 0-to-1 development, turning vague stakeholder needs into clear technical
requirements. I started with inventory visibility and a golden record for each SKU
before advanced automation.
- Strategy: 0-to-1 development
- Focus: Centralized truth
AI Implementation
- Used historical sales and seasonality trends.
- Predictive alerts based on velocity, not just count.
- Shifted purchasing from reactive to proactive.
The Human Outcome
We launched the platform that became the operating system for the business. AI
forecasting lifted prediction accuracy by 30% and gave us the data leverage to
negotiate better carrier rates.
- Launch: Full WMS/OMS
- AI Accuracy: +30%
Strategic Legacy
- Built proprietary tech that increased company valuation.
- Managed 2 interns and established product culture.