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 initiatives rarely fail because the models are wrong. More often, they fail because the platforms beneath them were never designed to operate reliably in production.
As complexity increases, small inefficiencies compound. Data pipelines become harder to manage. Model deployments slow down. Observability gaps create uncertainty. Over time, the platform itself becomes the constraint.
Successful AI is not only about algorithms. It is about the reliability of the systems that support them.
Spatialedge was built around this principle, treating data engineering and ML operations as first-class platform concerns rather than downstream afterthoughts.
DataSys: Reliable analytics
DataSys standardises how data is ingested, transformed, and delivered across the organisation.
It combines cloud-agnostic infrastructure with built-in automation and monitoring, so your data engineering teams can focus on enabling analytics and AI while DataSys handles operational complexity.
Production ML, Simplified
MLSys standardises how models are deployed, monitored, retrained, and promoted to production.
It combines structured pipelines with built-in governance and expert ML engineering support, so your data scientists can focus on modelling while MLSys handles infrastructure and reliability.
Scale data volume and complexity without growing your team
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.
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.
Talk to us
Ship machine learning models to production in no time
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 science teams work with MLSys
MLSys changes how machine learning moves from experimentation to production by standardising how models are developed, validated, deployed, and monitored in production
With MLSys, teams work in a controlled, repeatable way:
Data scientists develop and test models in a controlled sandbox environment
Models are validated and promoted through defined stages using version control
Deployments to production are automated and reproducible
Monitoring and drift detection run continuously after release
Retraining can be triggered on schedule or performance thresholds
MLSys standardises the full machine learning lifecycle so teams can focus on improving models rather than maintaining infrastructure
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Built for enterprise production environments
When machine learning models inform critical business decisions, reliability is not optional. MLSys provides the structure and controls required to operate models in environments where accuracy, uptime, and auditability matter.
Numerous large enterprises rely on MLSys to operate production machine learning systems, using it to:
Deploy models safely into live decision environments
Monitor model performance and detect drift in real time
Retrain and promote improved models without disruption
Maintain full visibility and traceability across environments
Its robustness comes from:
Standardised, reproducible ML pipelines
Expert ML engineering oversight and operational support
Cloud-agnostic deployment in customer-controlled environment
MLSys is designed to operate continuously, scale with growing model complexity and data volumes, and remain reliable without turning data science teams into infrastructure teams.
Ready to Experience MLSys?
Book a demo today and discover how MLSys allows data science teams to deploy and operate machine learning models in production with confidence
Talk to us
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News & Insights
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Spatialedge, a Stellenbosch-based data and applied artificial intelligence (AI) solutions start-up, has secured R60 million in funding from the Hlayisani Growth Fund.
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