A pharma supply chain forecasting model had been in development for nine months. We rebuilt the pipeline with GxP-aligned validation, audit logging, and a kill-switch the QA team owns. Deployed to production in 90 days with a validation package the regulatory team accepted without revision.
Engineering for systems where downtime is measured in megawatts, not error rates.
IoT data pipelines, time-series analytics, and predictive maintenance systems for energy and industrial operations. We build infrastructure that handles sensor volume at scale and answers operational questions in real time.
The engineering problems we see most in energy and industrial.
These come from engagements with energy companies, industrial SaaS platforms, and OEM manufacturers who need to turn sensor data into operational intelligence.
Sensor data volume that breaks existing pipelines
IoT fleets generating gigabytes per hour. Batch pipelines built for daily summaries. The data is arriving, but nothing downstream can use it in time to act on it.
Predictive maintenance that does not predict reliably
Models trained on clean historical data that underperform on live sensor streams. Too many false positives - technicians stop trusting the alerts. Too many misses - equipment fails anyway.
Time-series infrastructure that cannot answer what is happening right now
Query latency that makes real-time dashboards impractical. Data retention policies that keep too much or too little. Downsampling that loses the anomalies you actually need to detect.
Operational data siloed from analytical systems
SCADA systems that cannot talk to your data warehouse. OT/IT integration that was promised in the procurement and never fully delivered. Engineers who cannot get data without filing a ticket.
Edge compute that is not reliable enough for operations
Models deployed at the edge that drift without retraining. Connectivity gaps that leave edge nodes making decisions on stale inference. Update rollout that requires on-site visits.
Reporting that takes days when operators need answers in minutes
Compliance reporting that is manual and error-prone. Carbon accounting workflows that require a spreadsheet and two days. Operators who cannot get the data they need without waiting for IT.
A grid monitoring platform was processing sensor data in 4-hour batches. Operators needed 5-minute resolution to act on anomalies before they cascaded. We rebuilt the ingestion layer on a streaming architecture and shipped the anomaly detection model with configurable alert thresholds the operations team controls directly.
A solar energy operator needed real-time visibility at individual panel level across commercial and residential PV plants — replacing costly manual fault-finding. We built SolarWatch, a multi-tenant SaaS IoT platform with AI-driven error pattern detection, real-time module telemetry, and full Dispatcher 2.0 compliance for the German grid.
A legacy energy analytics system was too slow for operational use — multi-minute load times for historical datasets, sluggish dashboards, and a codebase that blocked new feature development. We replaced it with a modern web platform: 2× faster data processing, 60% faster report access, and 30–50% improvement in dashboard interaction speed.
A hydrogen and renewable energy company's technical teams were spending hours searching across regulations, engineering specs, and project documents to answer routine questions. We built a multi-agent RAG system with rigorous hallucination controls — cutting retrieval from hours to seconds, enabling 85% faster analysis, and surfacing insights locked in PDF and image-embedded documents.
What we build for energy and industrial teams.
These are the categories of operational systems we build most often. Most start with a Product Pilot to validate the architecture before committing to a full build - especially important for systems where data quality is uncertain.
Learn about Product Pilot ->- IoT data pipelines and real-time event streaming
- Time-series databases and analytics (InfluxDB, TimescaleDB)
- Predictive maintenance ML with live sensor feeds
- SCADA and OT/IT integration
- Edge compute and model deployment at the field
- Energy analytics and operational dashboards
- Carbon accounting and ESG data infrastructure
- Anomaly detection for industrial systems
What clients say about working with us.
Building operational data infrastructure that has to work?
Book a 30-minute technical call. Bring your sensor volume, your latency requirements, and what the operations team is asking for that they cannot get today.
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