Industry - Energy & Industrial

Engineering for systems where downtime is measured in megawatts, not error rates.

Your sensor data is arriving but nothing downstream can act on it in time. Your predictive maintenance model is generating false positives and your operators have stopped trusting the alerts. Your SCADA data and your analytics warehouse have never been properly connected. We build operational data infrastructure that answers the questions your operations team needs to act on now.

Where we help

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.

Building a new energy or industrial platform from the data layer up

Operational software built with the right time-series infrastructure, clean OT/IT integration, and a data model designed for sensor volume is faster to extend and easier for operators to trust. The alternative is three years of patches on top of a batch pipeline that was never designed for real-time operations.

Solutions that require subject matter experts

Some engagements span industry verticals and technical challenges together. Explore the full solutions map to find the right match.

More about solutions →
Proof of work

What comparable engagements delivered.

Industrial SaaS - Mid-market
Forecasting model to production: 9 months to 90 days

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.

GoKafkaAzure
Read the case
Energy analytics - Growth
Real-time anomaly detection on 2M sensors/hour

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.

PythonKafkaTimescaleDBAWS
Read the case
Solar / PVIoT monitoring
Module-level PV monitoring with AI error detection — Dispatcher 2.0 compliant

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.

PythonIoTAWSTimescaleDB
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System types

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
Client feedback

What clients say about working with us.

We had a great experience working with the Insoftex team. They played an important role in delivering a modern application for power quality and energy generation analytics, owning both front-end development and automated QA. They built a flexible, user-centric dashboard with configurable widgets, making it easy to analyze data across devices, parameters, and time ranges. Insoftex combines strong technical expertise with a clear focus on quality and delivery. A reliable partner I'd confidently recommend.
Shimon Yannay

Shimon Yannay

Head of Software Development · Israel

We are delighted to acknowledge that Insoftex skillfully programmed our frontend using React, meticulously bringing our design to life. Their adherence to our timelines and effective communication ensured a seamless and productive collaboration.
Ingmar Kruse

Ingmar Kruse

CEO · Sun Sniffer · Germany

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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