Yes. We integrate payment providers including Stripe, Razorpay and PayU, along with mapping services, analytics platforms, CRM tools and cloud infrastructure. If the business already uses specific tools, we build the integration rather than replacing them.

It depends greatly on the scale. A single restaurant ordering app is at a different price point than a multi-vendor marketplace with automated dispatch, fraud detection etc. The key cost factors are the platform coverage, complexity of features and design guidelines. We price projects individually — share your requirements and we'll put together a realistic estimate.

A straightforward food delivery app generally takes two to four months. A fully custom platform with advanced features takes longer, and the exact timeline depends on scope. We define milestones at the start of every project and communicate changes to the schedule before they become problems.

The AI features integrated into a food delivery platform depend on the business model, operational requirements, and growth objectives. Some of the most commonly implemented capabilities include:

• Personalized food recommendations
• Intelligent search and discovery
• Predictive reorder suggestions
• Automated delivery dispatch
• Real-time ETA prediction and tracking
  

Not every platform requires every feature. The right AI implementation depends on the specific challenges the business is looking to solve and the outcomes it wants to achieve.

Yes, and the improvements are specific. Smarter routing reduces delivery time. Automated dispatch removes the delay between order confirmation and driver assignment. Demand forecasting helps kitchens prepare ahead of peak periods. Each one makes a modest individual difference — across thousands of orders, that adds up.

Yes. We've worked with independent restaurants, chains, cloud kitchens, food trucks, and multi-vendor marketplaces. The architecture is meaningfully different between these — we scope and build each one to match how that business actually operates.

The system tracks order history, browsing behaviour, and timing patterns for each user. It uses that data to surface more relevant restaurants, dishes and offers over time. The recommendations become more accurate as more orders are processed.