SYBAL
- Models → APIs
- Production deployment
- Tech lead
- Full-stack & AI
Interface illustration — [ADD REAL PROJECT SCREENSHOT]
Project details
- Type
- Client engagement
- Category
- AI / AgTech
- Status
- Live
- Region
- United States
Technology
- Python
- FastAPI
- Django
- Next.js
- PostgreSQL
- AWS
- Docker
- CI/CD
Services involved
Overview
SYBAL is a US agricultural technology company applying machine learning to crop data. Rashid LLC led the technical direction: full-stack development, converting AI models into scalable production APIs, cloud deployment and CI/CD.
The engagement also covered the part most AI companies underestimate — mentoring engineers and advising founders on architecture decisions that are expensive to reverse later.
The challenge
A trained model is not a product. It runs in a notebook, on one machine, with data shaped exactly the way the researcher shaped it — and none of that survives contact with real users and real request volume.
Getting from there to an API a customer can depend on means versioning, input validation, deployment, monitoring and a rollback story, all of which are engineering problems rather than modelling ones.
The solution
Model inference wrapped into versioned, validated APIs with predictable contracts, so the product team could build against them without knowing how the model works.
Cloud deployment with CI/CD so a model or service update ships through a pipeline rather than by hand.
Architecture guidance for the founding team, and mentoring for the engineers who would maintain the system afterwards.
Key features
- AI models converted into versioned production APIs
- Full-stack application development around the model layer
- Cloud deployment and CI/CD pipelines
- Architecture direction and engineering mentorship
Outcome
- AI models running as production APIs rather than research scripts.
- Technical direction and architecture set for the engineering team.
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