Need to unify meter, SCADA, device, and market data, or build a reliable data foundation for AI? Relevant’s energy data engineering services put every source on one platform your AI can build on, with senior engineers for every kind of energy data you collect.
Relevant designs and builds a central data platform for your company: storage, compute, and one clock for every reading, sized for the sites and assets you plan to run.
We deliver data pipeline development for energy sources: connectors for SCADA systems, historians, AMI head-ends, charger networks, inverter clouds, and market feeds, each scheduled to what the source supports.
Validation, estimation, and editing (VEE) turns raw interval reads from meters and sensors into complete history. Data quality management supplies the rules each read has to pass.
Lineage records link every reported figure to the transformations and source reads behind it, and follow the ownership and access policies your data governance framework sets.
Our team builds energy analytics platforms on top of the validated data, with dashboards and self-serve BI for operations, commercial, and executive teams.
Live device and grid readings feed the energy monitoring software we build for control rooms, with live views and alerts for every site and asset.
Load, generation, and price forecasts train on history that Relevant assembles for each model: weather at the right location and resolution, backtesting sets, and a feature store that serves the same inputs in training and production.
Pipelines attach outages, maintenance visits, and curtailments to the readings around them, so predictive maintenance models learn from real failures.
An asset model built by our engineers maps every device to a single ID across vendors and systems, so each model sees one complete history per asset.
Each training dataset carries a version tied to its source data, so any model release can be rebuilt for auditors and partners.
Return pipelines bring model outputs and actual results back into the platform, so forecasts and dispatch decisions sharpen with each run.
Our team hands you a source map to approve: every data source your company runs, the build order, and the budget for each phase.
Relevant architects design storage, compute, and streaming for your data volume and help you choose the cloud that fits. You approve the design before the build starts.
Your highest-value source goes live first. Your operations and analytics teams start using its data while our engineers build the next sources.
Every sprint adds a tested, documented source to the shared platform. You choose the order from the open backlog.
Relevant hands over a documented, monitored platform, keeps the pipelines healthy after launch, and builds new applications on the same data through our energy software development services.
99% of engagements land on time and on budget. Work runs in two-week sprints against a scope fixed per phase, with an open backlog you can read at any time.
Our AI-assisted development workflow speeds up discovery, architecture, build, and QA, driving up to 50% faster delivery and up to 20% fewer bugs, with every release passing senior code review.
Pipelines keep OT and IT networks separate, with access controls and encryption aligned with ISO 27001:2022 and GDPR.
Pipelines, warehousing, quality, cleansing, and governance sit with one delivery team, so the engineers who design your data model also build and run it.
You own the code, pipelines, and data from the first commit. Every repository and cloud resource lives in accounts your company controls.
EU-based engineers work across US and UK business hours, so reviews and releases happen inside your working day.
Relevant engineers design that platform around the sources you run today and the ones your product adds next.
Yes, it can, when the platform keeps three layers apart from the start: source connectors, the shared data model, and the business rules for each market. A new inverter brand then needs one new connector. A new market adds its tariff, timezone, and reporting rules as configuration, while storage and models stay as they are.
To check your setup, trace how a reading from a new device type would reach a customer report, and list every script and table you would change on the way. If the only change is a new connector, your platform already keeps the layers apart. If you’re not sure, our engineers trace the path with you on the first call, with nothing to prepare beforehand.
Start the platform as soon as your first sites and devices report data, and keep the first phase small. Forecasting and optimization models learn seasonal patterns, so they need history that spans a full cycle of seasons. Usable history counts from the day the platform stores readings with consistent time and asset records. A company that starts early reaches its first AI project with that history in place.
The first phase covers one or two sources, storage sized for today, and the data rules later models depend on. The rest of the budget follows the AI roadmap.
To see where you stand, open your oldest stored readings and check whether each one still maps to a device in your current asset list. If they map, your history can train a first model today. If the oldest readings are hard to find, send a sample export and our team checks the mapping for you.
Each source gets its own connector, built around what the vendor exposes:
The detail with the largest effect on data quality is the timestamp. Some vendor APIs return local time without a UTC offset, and the connector stamps each reading with the site’s timezone at the moment of extraction. When the same readings also drive billing, energy software integration connects the validated reads to your billing and CRM systems.
Yes, we can. The takeover starts with an inventory of what runs today: every job, schedule, credential, and manual fix. Founder-built energy pipelines often carry fixes that live outside the code, such as a daylight saving correction applied in a spreadsheet each March, or an OEM API token tied to one person’s account.
Our engineers move those fixes into the pipeline code and the credentials into a managed secret store before changing anything else. You then choose one of three outcomes for each pipeline: keep, refactor, or rewrite. A rewrite costs less when the transformation logic exists only in an untested script.
Five numbers set the scope, and you can count them before the first call:
Our team turns those numbers into a phased roadmap within 14 days of kickoff, with scope, timeline, and budget for each phase. Contact us with your source list and device count, and the first call works from your real numbers.
IoT
AI
AI
Andrew Burak
