Client Success Story
Cloud AI & ETL Platform for Agricultural Machine Learning
Project Overview
Scalable Infrastructure for Agricultural Intelligence
A cloud-native system capable of orchestrating AI workloads, managing data pipelines, and storing results for an AgriTech company specializing in machine learning for field crop analysis.
AgriTech
Israel
3 months
$30K
Strategy & Execution
AWS Architecture & ML Pipeline Design for AgriTech
From the start, we approached this engagement as an AI infrastructure project, not just a backend implementation.
Our focus was on: Creating a robust execution layer for AI and data pipelines, capable of handling compute-heavy jobs.
Designing a modular system architecture on AWS that could later be adapted to other cloud providers if needed.
Supporting the full lifecycle of AI workloads, from data ingestion to execution and output delivery.
Introducing strong observability and control mechanisms for background jobs and long-running processes.
Working in tight collaboration with the client's technical leadership through structured sprints and regular reviews.
Rather than over-optimizing for a single model or workflow, we built a flexible foundation that could support evolving AI use cases in agriculture.
Hi, I'm Danylo Melnychuk
CEO at Xedrum
Don't wait — let's talk today.
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