Intelligence for
the distribution
network.
An enterprise intelligence platform for energy and utility networks. EUNIQ unifies meter, network, billing and service data into one topology-aware model — then forecasts demand, detects anomalies, scores risk, and tells a field crew what to act on first.
One intelligence layer · Electricity, gas and water · Any utility, any market
One architecture, every utility network
Utilities worldwide face the same three questions: where is the loss, what is about to fail, and what should the crew do today. The regulator differs, the vocabulary differs, the physical structure does not. EUNIQ is built against that structure.
The hierarchy is the same everywhere
Substation → feeder → transformer → consumer for electricity; the equivalent zone → main → district → connection for gas and water. The data model is built against topology, so a new market is a configuration exercise, not a rebuild.
Built to international standards
CIM IEC 61968 / 61970 for network and asset data, DLMS / COSEM for metering, MQTT and OPC-UA for telemetry, open REST and event APIs throughout. Standards written for utilities worldwide, not one vendor's stack.
The same problem, different names
Non-technical loss, AT&C loss, non-revenue water, unaccounted-for gas — four names for one question. Add ageing assets, electrification, distributed generation and demand volatility, and every utility is being asked to see further with the data it already holds.
Every layer is available for deployment. The platform is deliberately engineered against the hardest version of the problem — incomplete, inconsistent, partially instrumented networks — rather than the easiest.
From a raw meter reading to an action a field officer can take
Five stages, in order. The value isn't in any single model — it's that every stage operates over one shared, topology-aware representation of the network.
Ingest and unify
Feeder and DT interval readings, asset master, network topology, billing, outage and grievance records are aligned to a common time base. Every record is placed within the substation → feeder → DT hierarchy.
Resolve identity
Billing, GIS and metering systems disagree about which consumer sits on which transformer. EUNIQ reconciles those conflicting mappings into one queryable graph — the step that makes everything downstream possible.
Forecast
An LSTM/GRU sequence model learns previous-hour, day and week load, daily shape, weekday and weekend behaviour, peak periods and seasonal trend, then predicts expected load and peak 24–72 hours ahead — conditioned on the network hierarchy, not treated as a flat time series.
Detect and attribute
An LSTM autoencoder learns each asset's normal behaviour and flags deviation by reconstruction error — unsupervised, because confirmed failure labels are scarce in any utility. Residuals are correlated across the hierarchy to separate a technical condition from a commercial one.
Act, then learn
Every alert carries the signals that produced it, in a form a field officer can act on. What the engineer actually finds is recorded and becomes ground truth — so precision improves with each cycle, on the utility's own network.
One intelligence platform. Every utility.
One intelligence layer, multiple utility domains. Every module draws on the same data foundation and the same pattern engine — which is why adding a domain is configuration, not a rebuild.
EUNIQ Pattern Engine
Correlation, anomaly detection, forecasting and risk scoring — the intelligence layer every domain module runs on.
Your network, as a model a machine can reason over
Most platforms treat a meter reading as a number in a table. EUNIQ places every reading at a known point in a known network — so a signal can be interpreted in the context of the asset above it, and an anomaly can be attributed to a cause.
Billing, GIS and metering systems disagree about which consumer sits on which transformer. Resolving those conflicts into one queryable structure is what makes loss attribution, topology-aware forecasting and evidence-backed cases possible.
See it live in the cockpit ↗What EUNIQ is built on
A utility-domain algorithm library, deep learning, graph methods and agentic AI on one engineering core — assembled by a team that has shipped production systems at scale, and reviewed by doctoral advisors before anything is built.
Domain algorithms, not generic analytics
Machine learning alone does not understand a network. EUNIQ pairs it with the engineering methods utilities already trust — power-systems, hydraulic and gas-network analysis — so a model's output can be reconciled against physics rather than accepted on faith. This is the layer most analytics platforms do not have.
Sequence & representation models
Load carries structure across time. Recurrent and attention architectures learn that sequence directly instead of approximating it through hand-built lag features.
Detection without labels
Confirmed, dated transformer-failure records are scarce in every utility, in every market. Unsupervised models learn normal behaviour and score deviation, so no failure labels are required to start.
The network as a first-class object
Substation → feeder → DT is modelled as a graph, not metadata. Signals are read in the context of the level above them, and conflicting consumer mappings are resolved with a confidence score on every link.
Baselines that keep us honest
Every deep model is measured against a simpler baseline. If a gradient-boosted tree wins on an asset class, we ship the tree.
30+ specialised agents
Narrow, independently testable agents for ingestion, identity, forecasting, anomaly, attribution and feedback — orchestrated rather than fused into one opaque model.
Evidence, not verdicts
Every alert ships with the signals that produced it and the number behind each. Factors are reported as probable — a confirmed cause needs field validation only the utility can supply.
Algorithm experience, and independent review
Our founding team has delivered mission-critical systems at scale — real-time credit authorisation platforms, programmes of 2,500 engineers, and an agile engineering foundation for 30,000 people. That is where the discipline behind this platform comes from: correctness under load, reproducibility, and systems that are not allowed to fail.
Model selection and methodology are reviewed by doctoral advisors on our team, alongside subject-matter advisors with distribution-sector operating experience. We also work with university research groups — sponsored problem statements and independent validation of our models against instrumented network data.
30+ specialised agents behind one interface
Rather than one monolithic model, EUNIQ runs a library of narrow agents — each responsible for a single well-defined task, each independently testable. An orchestration layer routes work between them and assembles the result.
Data agents
Ingestion, schema inference, gap detection, unit reconciliation, timestamp normalisation, quality scoring.
Identity agents
Consumer–meter–transformer matching, conflict resolution across source systems, topology validation, orphan detection.
Forecasting agents
Per-asset model selection, feature construction, retraining triggers, drift monitoring, accuracy reporting.
Anomaly agents
Reconstruction scoring, threshold calibration, hierarchical correlation, false-positive suppression.
Attribution agents
Contributing-factor ranking, evidence assembly, confidence estimation, case generation for field teams.
Feedback agents
Inspection-outcome capture, label construction, retraining scheduling, precision tracking over time.
The same data, shaped for whoever is looking
Executives see the score and the trend. Zonal engineers see the assets. Field officers see one job and a form.
Score and trend
Composite intelligence score across loss, resilience, service and asset risk. Zone comparison, and the movement behind each number.
Asset watchlist
Ranked assets, forecast against capacity, open alerts, and a feeder-level view of where a condition is concentrated.
One job, one form
The asset, the evidence behind the alert, and a 30-second outcome form that works offline — the mechanism by which the models improve.
See the feeder-level cockpit
A worked example of the distribution transformer drill-down, built on representative feeder data.
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