.svg)
.avif)
.avif)
Industrial Technology
With the rapid development in autonomous hardware systems, manufacturers, OEMs and others are seeking for new efficiencies and streamlining systems from quote to delivery.
We work from firmware up to the user interface with which humans interact.
.svg)
Challenges we solve
Field Force Mobility
Industrial enterprises often depend on large field workforces for maintenance, inspections, and installations. Traditional paper-based workflows or disconnected apps slow productivity and increase errors.
By equipping field teams with mobile apps integrated into Snowflake via lightweight REST APIs, work orders, asset histories, and real-time diagnostics become instantly accessible in the field. Engineers can record updates, capture images, and sync telemetry seamlessly with central systems.
The outcome is higher first-time fix rates, reduced administrative overhead, and accurate, real-time visibility for decision-makers.
By equipping field teams with mobile apps integrated into Snowflake via lightweight REST APIs, work orders, asset histories, and real-time diagnostics become instantly accessible in the field. Engineers can record updates, capture images, and sync telemetry seamlessly with central systems.
The outcome is higher first-time fix rates, reduced administrative overhead, and accurate, real-time visibility for decision-makers.
Firmware Development
Managing firmware updates across distributed industrial devices is critical for security, performance, and compliance. Manual cycles often lead to inconsistent versions, downtime, and vulnerability exposure.
With a modern pipeline built on GitLab CI/CD, OTA update services, and device management platforms, enterprises can automate builds, testing, and rollouts. Updates are pushed securely over-the-air, validated with digital signatures, and tracked for completion across the fleet.
As a result, devices stay secure, compliant, and optimized—cutting downtime and extending product lifespan.
With a modern pipeline built on GitLab CI/CD, OTA update services, and device management platforms, enterprises can automate builds, testing, and rollouts. Updates are pushed securely over-the-air, validated with digital signatures, and tracked for completion across the fleet.
As a result, devices stay secure, compliant, and optimized—cutting downtime and extending product lifespan.
Predictive Maintenance
Unplanned equipment downtime is costly, disrupting production and eroding customer trust. Traditional preventive maintenance schedules are rigid and often inefficient.
Powered by Pygio Assembly, operators can stream sensor data via MQTT directly into Snowflake, where ML models built in Python detect anomalies in near real time. Assembly’s governance layer ensures data is consistent and business-ready, while AI agents highlight early warning signs of failure.
This approach increases asset uptime, extends equipment life, and optimizes maintenance costs.
Powered by Pygio Assembly, operators can stream sensor data via MQTT directly into Snowflake, where ML models built in Python detect anomalies in near real time. Assembly’s governance layer ensures data is consistent and business-ready, while AI agents highlight early warning signs of failure.
This approach increases asset uptime, extends equipment life, and optimizes maintenance costs.
Digital Twin Simulation
Understanding and optimizing industrial operations requires a holistic view of machines, processes, and environments. Physical-only monitoring provides limited insight.
With OPC UA and Kafka streaming telemetry into Azure Digital Twins and Snowflake, engineers can mirror production assets in a virtual replica. Assembly’s semantic layer enriches this telemetry, enabling simulations that model stress scenarios, performance optimization, and failure recovery—without disrupting real operations.
Digital twins powered by Assembly serve as a foundation for AI-driven decision-making and predictive control.
With OPC UA and Kafka streaming telemetry into Azure Digital Twins and Snowflake, engineers can mirror production assets in a virtual replica. Assembly’s semantic layer enriches this telemetry, enabling simulations that model stress scenarios, performance optimization, and failure recovery—without disrupting real operations.
Digital twins powered by Assembly serve as a foundation for AI-driven decision-making and predictive control.
Energy Optimisation
Energy consumption is a top cost driver for manufacturers, and sustainability targets demand continuous optimization. Yet energy usage data is often siloed and reactive.
By streaming smart meter data into a central S3 data lake and applying PyTorch-based AI models, enterprises can identify consumption patterns, detect inefficiencies, and optimize plant-wide energy allocation. Predictive models suggest cost-saving measures such as shifting loads, reconfiguring processes, or adopting renewables.
This reduces operational costs while supporting sustainability commitments.
By streaming smart meter data into a central S3 data lake and applying PyTorch-based AI models, enterprises can identify consumption patterns, detect inefficiencies, and optimize plant-wide energy allocation. Predictive models suggest cost-saving measures such as shifting loads, reconfiguring processes, or adopting renewables.
This reduces operational costs while supporting sustainability commitments.
Production Line Analytics
Plant managers need visibility into operational performance across metrics like OEE (Overall Equipment Effectiveness), downtime, and throughput. Traditional reporting is often batch-based, limiting timely decision-making.
By combining SCADA and IoT data through Azure IoT and visualizing in Power BI dashboards connected to Snowflake, managers gain a live view of production health. They can drill into bottlenecks, compare line performance, and track KPIs continuously.
In practice, this drives proactive issue resolution and supports a culture of continuous improvement.
By combining SCADA and IoT data through Azure IoT and visualizing in Power BI dashboards connected to Snowflake, managers gain a live view of production health. They can drill into bottlenecks, compare line performance, and track KPIs continuously.
In practice, this drives proactive issue resolution and supports a culture of continuous improvement.
Related Projects
Optimize supply and value chains with predictive maintenance for efficient production.
.avif)
Our Latest Insights
PYGIO at the Snowflake “Hello South Africa!” launch: embracing the AI Data Cloud locally
September 16, 2025
AI voice agent in action: a demo by Nicolas Gonatas
July 18, 2025
How AI is redefining jobs and risk in large organizations
July 14, 2025