
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.

The Complexity of Orchestrating Large-Scale Agri-Data
The client needed a centralized system to: Trigger and manage ML and deep learning jobs in a consistent way.
Process and store large volumes of agricultural data efficiently.
Work with both relational and non-relational data stores, depending on the workload.
Schedule and monitor long-running jobs without manual intervention.
Ensure the platform was stable, maintainable, and production-ready.
Without this foundation, scaling AI solutions across different products and datasets would have been difficult and costly.

Cloud-Based AI Execution Platform
We delivered a cloud-based AI execution platform with the following capabilities:
KEY FEATURES IMPLEMENTED:
- 01
AI-Driven ETL and Workload Execution
The system allows users to submit jobs via APIs, execute data-intensive AI workloads, and automatically persist results for downstream use. This created a repeatable and reliable workflow for running ML models in production.
- 02
Scalable AWS Infrastructure
We built the platform using core AWS services such as EC2, AWS Batch, Lambda, S3, RDS (PostgreSQL), and DynamoDB. Each component was selected based on performance, scalability, and cost considerations.
- 03
Job Scheduling, Queueing, and Visibility
Background jobs are scheduled and queued automatically, with monitoring in place to track execution progress, performance, and failures. This gave the team clear insight into system behavior at all times.
- 04
API-First System Design
All core operations, including data upload, job execution, and result retrieval, are exposed through APIs. This made the platform easy to integrate with existing ML pipelines and future applications.
- 05
Operational UI
We also delivered a lightweight user interface built with a modern frontend framework, allowing non-infrastructure users to monitor jobs and system status without touching cloud resources directly.
Project Interface Showcase
Client Testimonials
What Our Clients Say
Feedback on how our innovative solutions help achieve business results.

Xedrum delivered a production-ready AI-powered application within the agreed timeline and budget, including a working release on Apple TestFlight. The team was highly proactive in resolving issues, adapting to evolving requirements, and ensuring the AI functionality aligned with real user needs. Throughout the engagement, Xedrum consistently treated the project as a priority and demonstrated a strong understanding of both technical and business concerns.
Celine LeibfriedCEO at Unscripted
Xedrum successfully implemented a robust system that allows users to upload data, execute automated jobs, and retrieve results efficiently. The team delivered all components on schedule, maintained clear communication through weekly and ad-hoc meetings, and demonstrated professionalism throughout. The final solution met both technical and operational expectations.
Tomer PeretzCTO at Osirix
Xedrum's engineers worked closely with the client's project manager, integrating seamlessly into the existing workflow. The team consistently delivered planned milestones, maintained strong communication through regular online meetings, and brought a personal, collaborative approach that made them feel like an in-house AI team rather than an external vendor.
FredericCTO at Touch2Seen
Xedrum operated as an extension of the client's internal team, taking ownership of both delivery and day-to-day execution. Their engineers quickly understood the product context, collaborated across time zones, and proactively contributed ideas to improve the solution. The team maintained consistent velocity, adapted to changing priorities, and helped move the product forward without adding management overhead.
Arnon ZamirCTO at Aristo
Xedrum successfully implemented a robust system that allows users to upload data, execute automated jobs, and retrieve results efficiently. The team delivered all components on schedule, maintained clear communication through weekly and ad-hoc meetings, and demonstrated professionalism throughout. The final solution met both technical and operational expectations.
Richard BatesCEO at Acumen Data
Xedrum delivered a production-ready AI-powered application within the agreed timeline and budget, including a working release on Apple TestFlight. The team was highly proactive in resolving issues, adapting to evolving requirements, and ensuring the AI functionality aligned with real user needs. Throughout the engagement, Xedrum consistently treated the project as a priority and demonstrated a strong understanding of both technical and business concerns.
Celine LeibfriedCEO at Unscripted
Xedrum successfully implemented a robust system that allows users to upload data, execute automated jobs, and retrieve results efficiently. The team delivered all components on schedule, maintained clear communication through weekly and ad-hoc meetings, and demonstrated professionalism throughout. The final solution met both technical and operational expectations.
Tomer PeretzCTO at Osirix
Xedrum's engineers worked closely with the client's project manager, integrating seamlessly into the existing workflow. The team consistently delivered planned milestones, maintained strong communication through regular online meetings, and brought a personal, collaborative approach that made them feel like an in-house AI team rather than an external vendor.
FredericCTO at Touch2Seen
Xedrum operated as an extension of the client's internal team, taking ownership of both delivery and day-to-day execution. Their engineers quickly understood the product context, collaborated across time zones, and proactively contributed ideas to improve the solution. The team maintained consistent velocity, adapted to changing priorities, and helped move the product forward without adding management overhead.
Arnon ZamirCTO at Aristo
Xedrum successfully implemented a robust system that allows users to upload data, execute automated jobs, and retrieve results efficiently. The team delivered all components on schedule, maintained clear communication through weekly and ad-hoc meetings, and demonstrated professionalism throughout. The final solution met both technical and operational expectations.
Richard BatesCEO at Acumen Data
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Hi, I'm Danylo Melnychuk
CEO at Xedrum
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