Teams decide based on what’s already hours old. Relevant builds custom energy data analytics platforms that connect your operational, asset, and market information at the source, so you get one live picture of the business without spreadsheet detours or off‑the‑shelf compromises.
Operational, asset, and market data sit in separate systems with no clean path between them. We design and build the warehouses, lakes, and pipelines that bring it together into one analytics‑ready foundation.
Raw generation, consumption, and asset data don’t help on their own. We turn it into the operational metrics your engineering and commercial teams use to run the business.
Every level of your organization needs a different view of the same data. Our team builds BI layers and dashboards that give operations, commercial, and executive teams live, self‑serve access without report tickets and waiting.
Decisions made on last week’s numbers carry risk. Relevant forecasting models that turn your historical and real-time data into reliable predictions, so your team can act on what’s coming instead of what already happened.
Some signals only matter if you catch them the moment they happen. We engineer streaming pipelines that surface live operational data as it moves.
Regulatory reporting built on manual exports breaks under audit pressure. We automate operational records, ESG metrics, and data lineage, so your team can stop wrestling with spreadsheets and sign off on clean, audit-ready outputs.
The real-time data streaming off your plants, grid, and control systems holds the earliest signs of trouble, if anyone can see it in time. We turn high-volume SCADA and operational feeds into live monitoring and analytics, so problems surface as they form instead of in a report the next morning.
Meter and AMI data are where revenue and demand patterns hide, buried in volumes too large to read by hand. You get analytics that make sense of consumption at scale, surfacing demand trends, anomalies, and the patterns that drive accurate billing and forecasting.
Every pump, turbine, transformer, and battery generates data on how well it is running and how quickly it is wearing out. Relevant builds analytics that turn these insights into performance benchmarks, degradation trends, and early warnings, so maintenance happens on evidence.
Energy markets move faster than spreadsheets can keep up, and the cost of a late reaction is money. We bring market, pricing, and trading data into your analytics so your team can see exposure, spot opportunities, and make pricing decisions on what the market is doing now.
Emissions, generation mix, and compliance data increasingly decide what you report to regulators and investors. You get analytics that automate ESG tracking and turn raw operational data into audit-ready sustainability reporting.
We begin by identifying the decisions you’re trying to make faster, then trace backward to the data that should inform them. What you get is a clear scope: the questions the platform needs to answer and the sources that feed them.
You get a blueprint for the layer everything else depends on, how your operational, asset, and market data gets ingested, modeled, and validated, plus the lineage that lets you trust every number downstream.
One high-value question gets answered end to end, real data in, trustworthy insight out, while the rest of the platform is on the roadmap. That early result is proof that the approach holds against your own data.
New sources, analytics, and dashboards land every sprint, each tested and documented, so your team is already working from the early pieces while the rest is being built.
Every metric is checked against ground truth, reconciled with your source systems, pressure-tested on edge cases, and confirmed at full data volume.
Our engineers keep the pipelines healthy, add new questions your team thinks of as they use it, and scale it as your data grows.
We’ve spent more than a decade building software for energy companies: SCADA integrations, asset analytics, market systems, and the operational data that powers them.
Our teams cover the full stack of energy analytics, including data pipelines, cloud infrastructure, forecasting models, and BI. Backed by 246 delivered projects and 98% client satisfaction.
Every pipeline we build carries validation rules, data lineage, and audit trails from day one, aligned with ISO 27001 and GDPR.
Senior engineers based in the EU, available across US and UK working hours. No handoff delays, timezone gaps, or junior teams hidden behind a senior facade.
2-week sprints with working software at every checkpoint. A transparent backlog, a scope that can move as priorities change, and a budget that stays under control.
Our senior-led AI framework accelerates discovery, architecture, build, QA, and documentation, delivering up to 50% faster cycles, 2× faster discovery, up to 20% fewer bugs, fixed scope from sprint zero, and 99% of engagements on time and on budget.
The discovery phase maps what data you have, where it lives, and how reliable it is, including the spreadsheets people actually run the business off. From there, we prioritize sources based on which feed the highest-value questions.
You don’t need a clean data warehouse before we begin. Unifying scattered sources, fixing the data quality issues that surface along the way, and replacing spreadsheets with auditable pipelines is part of the build.
Not necessarily. Most clients use tools such as Tableau, Power BI, Looker, Sigma, or Metabase. We build the data foundation underneath them, which is where most of the analytics work lives. On top of a well‑modeled warehouse, these tools perform reliably, and the quality of the data matters more than which BI tool you choose.
When we add net-new features, such as custom dashboards, real-time operational views, embedded analytics in operational tools, or executive KPI screens, we choose the framework that best fits the specific use case. This can include React or similar for highly interactive views. Tool choice is case-by-case, based on what you need the screen to do.
We treat data quality as platform infrastructure:
When data is missing or late, the platform shows it explicitly rather than displaying a misleading zero, and downstream calculations handle gaps deliberately. Data quality dashboards expose pipeline health to your team, so you can verify trust in the platform.
During the first 6-8 weeks. After discovery and the architecture phase, we pick one high-value question your team is trying to answer faster, generation by site, asset performance, customer consumption patterns, market exposure, whatever pays off first, and build that end-to-end against your real data.
You’re working from a real insight before the rest of the platform is built. That early result also serves as proof that the approach holds against your own data.
We architect for both explicitly. Time-series data lands in a store designed for it (TimescaleDB, InfluxDB, or columnar storage in Snowflake/Databricks, depending on use case), with downsampling and retention policies that keep recent data at full resolution and older data aggregated.
See how industrial leaders use our experience in energy software development to improve operations.
Book a 30-minute discovery call to share your systems and goals. After the call, you’ll get a clear plan covering scope, timeline, and team.
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