FoodTech · AI Delivery · Talent
From stuck founder to live AI app and an in-house team of eight.
Concept to production3 months
Context
A foodtech and catering specialist had a funded idea: an app that builds personalized meal plans from each user's preferences, goals, and medical lab data. The founder had validated demand but lacked the technical depth to build it — and the integration problem had already defeated several contractors.
The problem
- 01Pulling user data, food data, and recipe data into a single model was harder than any of the founder's previous contractors could handle.
- 02Building a recommender on top of that data needed expertise the team didn't have in-house.
- 03There were no internal engineering resources to carry app development at all.
What we did
- 01Built mock data models and simple optimization first — just to prove personalized recommendations were achievable within the medical and dietary constraints.
- 02Stood up a dedicated recommender microservice in Python, with a universal food-and-recipe catalog clustered and tuned for allergies and individual preferences.
- 03Delivered a cross-platform app in Dart/Flutter with a backend that pulled grocery chains, medical labs, and the data-science core into one flow.
- 04In a second phase, hired and onboarded an in-house team of eight and transferred all development processes to them.
Results
- 01Fully functional AI-first app live in 3 months.
- 02A standing in-house tech team in place for business continuity and further scaling — the founder is no longer dependent on outside contractors.