The Foundation for Production-Grade Analytics and AI

Two core platforms. One reliable path from data to decision.

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Why foundations matter.

Analytics and AI do not fail because of algorithms. They fail because the underlying platforms were never designed for production. Spatialedge addresses this by treating data engineering and ML operations as first-class platform concerns.

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DataSys: Reliable analytics

DataSys consolidates all data engineering with cloud-agnostic, reliable, and observable pipelines. Ensures timely, consistent, and production-ready data for analytics, BI, and ML teams.

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MLSys: Governed AI

MLSys enables rapid, reproducible model deployment, monitoring, drift detection, retraining, and safe promotions. Allowing your teams to focus on value creation without operational burden.

Scale data volume and complexity without growing your team

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DataSys Platform Overview

DataSys is the backbone of reliable analytics. It orchestrates the complete data engineering process with cloud-agnostic and highly observable pipelines, guaranteeing that data is timely, accurate, and ready for production analytics. With automated monitoring, error handling, and compliance built in, DataSys supports everything from batch ingestion to real-time event streams. Teams can scale insights while maintaining trust in the underlying data.

Unified data pipelines

Run streaming and batch data pipelines in a single, managed platform with event-driven and scheduled execution. Stream and batch processing, one control plane.

Reproducible by design

Every pipeline, configuration, and transformation is version-controlled through Git-integrated CI/CD. Full traceability and safe rollbacks.

Built-in observability

Monitor pipeline health, data freshness, and system performance through dashboards and automated alerts. Know when something breaks before consumers do.

Cloud-agnostic architecture

Deploy in cloud, on-prem, or hybrid environments without re-architecting pipelines. No platform lock-in.

Config-driven automation

Build and manage pipelines using low-code templates, custom code, or AI-assisted configuration. Standardised deployments without limiting flexibility.

Real-time streaming

Process high-velocity data streams with built-in transport, load balancing, and real-time transformation. Designed for spikes, not averages.

Anomaly detection

Continuously detect unusual data patterns and silent failures that traditional pipeline monitoring misses. Protect downstream analytics and models.

Enterprise-ready operations

Security, access control, lineage, compliance, and SLAs built into the platform. Designed for regulated, production environments.

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How data engineering teams work with DataSys

DataSys changes how data engineering teams operate day to day by removing manual coordination, repetitive work, and constant firefighting.

With DataSys, teams work in a controlled, repeatable way:

Data engineers define pipelines using standardised, config-driven templates

Changes are submitted through version control and deployed via CI/CD

Streaming and batch workloads are handled in the same operational model

Pipeline health, data freshness, and performance are continuously monitored

Issues are surfaced automatically with alerts and diagnostics

Instead of maintaining dozens of bespoke pipelines and tools, teams operate a single, coherent system.

This allows small data engineering teams to reliably manage large, complex data environments without being overwhelmed by operational overhead.

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Proven, robust, and in production today

DataSys is not an experimental platform. It is battle-tested in production environments. It currently serves as the data engineering backbone for all Spatialedge retail products, supporting high-volume, business-critical workloads.

Beyond Spatialedge’s own solutions, multiple large enterprises rely on DataSys as the foundation of their data analytics environments, using it to:

Ingest and process high-volume operational data

Support downstream analytics and machine learning

Maintain consistent, observable data across teams and systems

Its robustness comes from:

Standardised, reproducible pipelines

Built-in observability and anomaly detection

Cloud-agnostic deployment in customer-controlled environment

DataSys is designed to operate continuously, scale with growing data volumes and complexity, and remain manageable without expanding engineering teams.

Ready to Experience DataSys?

Book a demo today and discover how DataSys allows small data engineering teams to operate complex, high-volume data environments with confidence.

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Ship machine learning models to production in no time

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MLSys Functionality Deep Dive

MLSys powers the entire lifecycle of machine learning models with robust governance and automation. The platform supports rapid deployment, continuous monitoring, automated drift detection, managed retraining, and safe promotion of models into production. MLSys enables teams to focus on building value by abstracting infrastructure and operational complexity while maintaining transparency, compliance, and reproducibility throughout every stage of the ML workflow.

End-to-End MLOps Platform

A fully managed environment to train, deploy, operate, and monitor machine learning models in production.

One-Push Deployment

Deploy models from sandbox to production with automated testing and controlled promotion.

Automated Data & Model Monitoring

Continuously monitor data quality, drift, and model performance across all runs.

Production Model Endpoints

Expose models via real-time or batch APIs for integration into business systems.

Standardised ML Pipelines

Submit model logic once using defined pipeline stages and version control.

Experiment Tracking

Track experiments, parameters, and results with full lineage and reproducibility.

Automated Retraining

Retrain models on schedules or events with performance-based promotion.

Controlled A/B Testing

Safely roll out new models using champion-challenger and gradual deployment strategies.

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How data engineering teams work with DataSys

DataSys changes how data engineering teams operate day to day by removing manual coordination, repetitive work, and constant firefighting.

With DataSys, teams work in a controlled, repeatable way:

Data engineers define pipelines using standardised, config-driven templates

Changes are submitted through version control and deployed via CI/CD

Streaming and batch workloads are handled in the same operational model

Pipeline health, data freshness, and performance are continuously monitored

Issues are surfaced automatically with alerts and diagnostics

Instead of maintaining dozens of bespoke pipelines and tools, teams operate a single, coherent system.

This allows small data engineering teams to reliably manage large, complex data environments without being overwhelmed by operational overhead.

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Proven, robust, and in production today

Beyond Spatialedge’s own solutions, multiple large enterprises rely on DataSys as the foundation of their data analytics environments, using it to:

Ingest and process high-volume operational data

Support downstream analytics and machine learning

Maintain consistent, observable data across teams and systems

Its robustness comes from:

Standardised, reproducible pipelines

Built-in observability and anomaly detection

Cloud-agnostic deployment in customer-controlled environment

DataSys is designed to operate continuously, scale with growing data volumes and complexity, and remain manageable without expanding engineering teams.