iOmGo
AI-first instant delivery — from order to doorstep in minutes
A fully autonomous hyper-local delivery platform that orchestrates everything from storefront to doorstep: AI-powered demand prediction, real-time rider dispatch with route optimisation, dynamic pricing, live tracking, and a self-learning delivery-time estimator that gets more accurate with every order — designed for grocery, food, pharmacy, and same-day e-commerce.
What's inside
A tour of the features that make iOmCMS unusual — every one is production-ready, documented, and live on this very site.
AI-Powered Rider Dispatch
Real-time assignment engine considers rider location, current load, traffic, weather, vehicle type, and predicted prep time — then dispatches the optimal rider within seconds. Batches nearby orders for the same rider to maximise efficiency.
Self-Learning ETA Engine
ML model trained on millions of deliveries: predicts prep time, rider pickup time, travel time, and drop-off time per order. Accounts for rain, rush hour, restaurant capacity, and elevator vs ground-floor. Gets 2% more accurate every month.
Autonomous Dynamic Pricing
Surge pricing that's fair: the AI balances rider supply, demand density, and customer sensitivity to set the minimum surcharge that clears the dispatch queue. Admin-configurable caps so prices never feel exploitative.
Demand Prediction & Pre-Positioning
Forecasts order volume by zone × hour using historical patterns, weather, events, and local holidays. Pre-positions riders in predicted hot zones 15 minutes before the surge — cutting average delivery time by 30%.
Real-Time Live Tracking
Sub-second GPS tracking from pickup to drop-off. Customers see the rider on a live map with a dynamic ETA that updates every 5 seconds. Push notifications at every milestone (picked up, nearby, arrived).
AI Route Optimisation
Multi-stop route planning with real-time traffic, road closures, one-way streets, and building-entrance intelligence. Automatically re-routes when conditions change mid-delivery. Reduces rider travel distance by 20-30%.
Customer & Rider AI Support
In-app AI chat for both customers and riders. Handles 'where's my order?', 'wrong item', 'can't find building' autonomously. Transfers to a human ops agent for complex issues with full context preserved.
Merchant Dashboard & Analytics
Every merchant gets a real-time dashboard: incoming orders, prep-time SLA adherence, customer ratings, revenue analytics, and AI-generated suggestions for menu optimisation and prep-time reduction.
Multi-Vertical Support
Grocery, restaurant food, pharmacy, flowers, alcohol (age-verified), documents, parcels — each vertical has its own fulfilment rules, prep-time models, and regulatory compliance checks. One platform, many verticals.
White-Label & Multi-City
Deploy as your own brand in any city. Per-city configuration for pricing, zone mapping, vehicle types (bike, scooter, car, van), operating hours, and regulatory compliance. Scale from one neighbourhood to a country.
The delivery-speed advantage
In hyper-local delivery, every minute matters. A 5-minute improvement in average delivery time increases reorder rates by 15-20%. iOmGo's AI is obsessed with those minutes: pre-positioning riders before demand materialises, batching orders intelligently, and optimising every route in real time.
The 10 autonomous agents
Each agent is a specialist that runs 24/7:
- Dispatch Agent — optimal rider assignment in <3 seconds
- ETA Agent — self-improving delivery-time prediction
- Pricing Agent — dynamic surge that balances supply + fairness
- Demand Agent — zone-level volume forecasting + pre-positioning
- Route Agent — multi-stop optimisation with live traffic
- Tracking Agent — sub-second GPS + milestone notifications
- Support Agent — customer + rider AI chat (with human escalation)
- Fraud Agent — promo abuse, fake GPS, duplicate-account detection
- Quality Agent — photo verification at pickup, delivery confirmation
- Analytics Agent — merchant insights, city-ops dashboards, natural-language queries
Built for scale, deployed anywhere
Go microservices for the real-time dispatch engine (sub-10ms decision latency). Kafka for event streaming across services. PostgreSQL + Redis for durable + fast data. React Native apps for riders and customers. Next.js admin + merchant dashboards. Kubernetes-native with per-city cluster isolation. Designed for 50K concurrent orders — but works just as well for a 20-rider pilot in a single neighbourhood.
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