MissionCart
Goal-to-cart prototype that turns an occasion brief into a coordinated shopping list.
- Type
- AI / ML
- Client
- Hackathon prototype
- Role
- Product engineering · AI retrieval
- Year
- 2026
Evidence / status
Prototype. Reported outcomes await verification.
Challenge
Shopping by intent, not keyword
Planning an event can require products from many unrelated categories. The prototype needed to translate one natural-language brief into a coherent cart within a fixed hackathon window.
Constraints
- The team had 48 hours for a working prototype.
- Product retrieval needed to understand occasion-level meaning.
- The stack combined a React Native client with a Python service.
- Budget, dietary preference, and availability needed to influence results.
Approach
Key decisions
Structured occasion parsing
The language model converts a brief into event type, guest count, preferences, and budget before product retrieval begins.
Semantic catalogue retrieval
FAISS retrieves product candidates by meaning rather than relying on exact keyword matches.
Dedicated cart ranking
A separate ranking stage balances relevance, availability, and price constraints before returning the cart.
Scope fixed around a demonstrable journey
The sprint prioritised one end-to-end occasion flow and a small set of supporting controls over breadth.
Evidence
What can be stated
Current status: Prototype. Unverified outcomes are labelled instead of presented as proof.
Existing project notes report six prototype features completed during a 48-hour sprint.
Needs verificationAmazon HackOn 2026 finalist status requires owner verification before publication as proof.
Needs verification
Technology stack
- React Native
- FastAPI
- Groq
- FAISS
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