Industries Environment & Green Energy
AI Engineering and software development for environment and green energy companies.
Clean energy, powered by cleaner data.
This sector runs on measurements nobody watches being taken: a satellite pass, a probe in a river, a meter on an inverter. Whatever gets built on top of them has to survive a gap in the record, an auditor asking where a figure came from, and a decision that is needed before the next reading arrives.
AzulfyMonitoring Pollution From Orbit
The platform EU municipalities watch their air, water and soil on. We built its ingestion and Earth Observation pipelines, turning Copernicus Sentinel products into measurements, and the maps and alerts a council reads them on.
- 20+
- Municipalities and organizations
- 10K+
- Measurements processed
- 20+
- Air, water and soil indicators
Why environmental companiestrust us with delivery.
A reading nobody can trace back to its source is worth nothing to a regulator, and a forecast that misses the morning is settled on the imbalance market. This is why we pay special attention to the most sensitive parts of the system.
The record has holes in it
A pass is clouded out, a probe drifts, a meter goes quiet for three days. The gaps are the normal condition here, so a system has to reason over an incomplete record and say where it is guessing.
A reading is only as good as its lineage
Every number here ends up in front of a regulator, an auditor, or a council that has to act on it. Which pass it came from, which correction was applied, and which version produced it have to travel with the value.
Thresholds are law, not preference
A limit value is set in legislation, and crossing it starts a clock somebody is accountable to. Rounding, averaging, or resampling quietly can change whether a breach happened at all.
The data is heavy before it is useful
One scene is gigabytes and a territory is thousands of them. Processing has to sit next to the archive, and what reaches a browser has to be tiles and aggregates rather than the source.
What green energy demandsfrom engineering and AI.
A forecast that misses by an hour and a reported figure an auditor cannot trace both cost real money. These four are where engineering and AI earn their place in a green energy business.
01Reporting that survives assurance
Sustainability figures are audited now rather than published. A number in a CSRD or GHG Protocol disclosure has to be reproducible months later, from the same inputs, with a record of every restatement in between. That is a pipeline property, decided when the system is designed, not a spreadsheet somebody reconciles at year end.
02Generation forecasts priced by the half hour
Wind and solar output is a weather problem before it is a modelling one, and the error is settled financially on the imbalance market. A forecast earns its place only when it beats persistence on the same horizon your trades are made at, measured against that baseline continuously rather than once at acceptance.
03Fleet telemetry that means the same thing twice
Inverters, turbines, and meters arrive with a different SCADA per manufacturer and tag names that mean different things at different sites. Normalising that into one asset model is usually the actual project, and skipping it is why a dashboard works at one site and nowhere else.
04The map is the interface
In this sector the answer to almost every question is a place, which makes the frontend a real engineering problem rather than a chart library. Tiles, time sliders, and layers over millions of features have to stay responsive on a laptop in a council office, or the analysis underneath them never gets looked at.
Real-life stories of triumph.
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Green energy companies always combine backend processing, automation and frontend visualization into a single workflow. The biggest lesson was that reliable monitoring is just as important as the data pipeline itself because users depend on timely environmental alerts. Looking back, investing in observability made ongoing maintenance much easier.
Nortik’s Impact
Features across Azulfy’s satellite environmental monitoring platform: ingestion and Earth Observation processing pipelines, reporting and alerting workflows for municipalities, and the geospatial frontend they arrive on.
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Frequently asked questions
Everything environmental and energy teams usually want to know before building on measurement data. Not here? Ask us directly.
Yes, and it is the work behind the case study on this page. For Azulfy we built the ingestion and Earth Observation processing pipelines that turn Sentinel products into air, water and soil measurements, plus the maps, reports and alerts a municipality reads them on.
Earth Observation data does not arrive as rows. It arrives as scenes, with revisit intervals, cloud masks, atmospheric corrections and projections to reconcile before anything is comparable across time or across a border. That reconciliation is most of the engineering.
It is the normal condition, and a system that hides it is worse than one that has gaps. Clouded passes, offline sensors and dead meters get handled explicitly: a value that was interpolated is labelled as interpolated everywhere it travels, including into a report.
What matters is which decisions are allowed to depend on a full record. That is a design choice made with your team up front, not something discovered when a regulator asks how a month with four missing days was averaged.
That is the requirement this sector is built around, so lineage is carried rather than reconstructed. Every published value keeps the inputs it came from, the processing version that produced it, and the corrections applied on the way.
The practical test is restatement: when a source is reprocessed or a method changes, the platform has to show both the old figure and the new one and explain the difference. Systems that overwrite in place cannot pass an assurance review, and retrofitting that later means reprocessing the archive.
We build the systems around it, and we are careful about the distinction. Weather ingestion, asset and telemetry modelling, backtesting harnesses, and the serving path a trading or dispatch decision actually reads are all engineering problems we do every week.
On the model itself the honest answer is that it has to be measured against persistence on your own horizon before anybody trusts it, and monitored for drift afterwards. We have not published a forecasting engagement in this sector, and we would rather say so than let a case study on this page imply one.
That is exactly the shape of the work here. Azulfy and Amini are both platform companies, and in both cases we built features inside their product rather than a product of our own: pipelines, processing, reporting, and the geospatial frontends their customers use.
Amini is the wider version of the same problem, a data layer covering field-level agriculture through to sovereign infrastructure across 25+ countries, where the frontend has to make territory-scale data explorable visually instead of queryable.
You do. Code, trained models, pipelines, and documentation are yours, along with the infrastructure access to run them without us.
We hand over a codebase your own team can pick up. Nothing we build is designed to keep you dependent on us.
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