Provides an on-demand intracity delivery platform that uses real-time tracking and optimized route planning to ensure fast, secure, and cost-effective transportation of goods. The service addresses the challenges of urban logistics by offering a range of vehicle options, from bikes to trucks, tailored to handle varying sizes and weights, with transparent pricing and 24/7 customer support.
Funding
$200K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Intracity logistics face challenges in providing fast, secure, and cost-effective delivery solutions due to traffic congestion, varying package sizes, and the need for real-time tracking. Businesses require a reliable platform to manage diverse vehicle options and ensure timely transportation of goods within urban areas.
Solution
HAWQR provides an on-demand delivery platform that optimizes intracity logistics through real-time tracking and efficient route planning. The platform offers a range of vehicle options, including bikes, auto rickshaws, and trucks, to accommodate various delivery needs. By leveraging a mobile application, users can access transparent pricing, 24/7 customer support, and secure payment options. HAWQR's services cater to B2B, e-commerce, and personal delivery requirements, ensuring timely and secure transportation of goods.
Target Audience
HAWQR primarily targets businesses, e-commerce platforms, and individuals requiring on-demand delivery services within the city.
Features
- Mobile application for requesting and managing deliveries
- Real-time tracking of delivery vehicles via GPS
- Diverse fleet including bikes, 3-wheelers (autos), and various truck sizes (Tata Ace, Tata 407)
- Transparent pricing with base fees for each vehicle type
- Weight and size limits specified for each vehicle category
- 24/7 customer support for addressing queries and issues
- Secure online payment processing
- Route optimization for efficient delivery times