HalaBasket
AI-Enabled MVP Development Delivering 37% Increase in Basket Additions
91%
AI Response to Queries
3x
Faster MVP Launch
37%
More Cart Orders
62%
Less Manual Efforts
A growing grocery delivery retailer needed clearer differentiation in a competitive market. With structured idea & validation sessions, Bytes Technolab led a product design sprint, rapid prototyping, and focused MVP development. AI integration was embedded to improve discovery and automate support. The objective was measurable growth without unnecessary complexity.
Country
USA
Duration
3 Months
Industry
Retail and eCommerce
Technologies
Python TensorFlow Vertex AI LangChain OpenAI
Services
Idea & Validation Product Discovery Prototyping & POC MVP Development AI Integration
Problem Statement
- Static search made product discovery slow and inefficient.
- Support teams handled repetitive delivery and refund queries manually.
- Fragmented systems limited personalisation and insights.
- The founders needed validated traction before scaling further.
Challenges
- Inconsistent data structures slowed AI experimentation.
- Legacy integrations restricted real-time recommendations.
- Investors expected visible progress within three months.
- Limited internal bandwidth constrained development speed.
- The MVP needed to remain lean yet commercially effective.
Solution
- Bytes Technolab initiated structured idea & validation sessions to align commercial objectives with technical feasibility.
- Through a targeted product design sprint, we identified friction in discovery and post-purchase journeys. A working prototype was tested to validate assumptions before development.
The AI-driven MVP development phase included:
- An AI-driven product discovery capability that interpreted conversational queries and returned relevant, curated results.
- An AI-powered virtual assistant embedded within the digital journey to automate delivery queries, refund processes, and standard customer interactions.
- Behaviour-based recommendation logic to improve repeat purchases and basket value.
- AI integration was implemented using modern backend frameworks and scalable cloud deployment to support future expansion.
Result
Within weeks of launch, performance improvements were measurable.
- 46% improvement in new conversions among users using AI search.
- 29% increase in repeat purchases during the initial eight-week period.
- 71% reduction in response times for common customer enquiries.
- 88% customer satisfaction for AI-assisted support interactions.
AI-powered discovery minimised browsing fatigue and increased basket additions.
The AI assistant reduced operational workload while improving customer confidence.