Sensor Intelligence for smart buildings
Your buildings are talking. We make them worth listening to.
FrostLogic Explore reads from your BMS, energy meters, and IoT sensors, and returns a prioritised queue of decisions — anomaly detection, forecasting with confidence bounds, continuous compliance, and what-if simulation. Grounded in your data. Nothing invented.
Trusted by operators across the Nordics
The real bottleneck
It's not sensors. It's the decisions you can't make from them.
Modern commercial real estate generates more telemetry per hour than a 1995 power station did in a year. The instinct, when you see that flood, is to buy more dashboards. It is exactly the wrong move. The bottleneck is no longer measurement. It is the act of deciding — calmly, with confidence — what each signal means and what to do about it.
Too much data.
A 40-building portfolio commonly produces 50 000+ time-series points an hour. No human operator can read that. A dashboard that shows all of it is doing the operator a disservice: the signal-to-noise ratio of "everything green" is zero, and the buzzer of "everything alarming" is ignored within a week.
Not enough trust.
Sensors drift. They get stuck. They go offline silently. They contradict their neighbours. A model trained on bad inputs produces confident, wrong answers — the kind of wrong that's expensive specifically because it looks right.
No clear next action.
Most platforms show what is happening. Operators need to know what to do next, in what order, and why. The right interface is not a dashboard. It is a queue — twenty items, prioritised, each with a clear suggested action. The dashboard is the question. The queue is the answer.
A modern building has more than enough data. It is short on decisions per kilobyte. Sensor Intelligence is the layer that raises that ratio.
What Explore does
From raw telemetry to ranked actions.
One platform. Three jobs. All grounded in the data you already have.
See what your sensors are hiding.
Anomaly detection across six methods, with severity scoring and one-click triage. Critical drift surfaces before it becomes a callout.
Plan with confidence.
Accurate forecasting with explicit confidence bounds, plus what-if simulation for any operational change before you commit to it.
Prove compliance, continuously.
Live evidence for BREEAM, LEED, Nordic Swan and more — collected from the sensors you already have, not reconstructed at audit time.
Pattern-based AI
Four kinds of reasoning, one engine.
No single technique survives contact with a real building. The Frostdynamics™ engine layers four — each catching the failure modes of the others — into a single decision pipeline.
Detection from distribution.
Z-scores, seasonal decomposition, change-point detection. Cheap, fast, and correct on the obvious cases — a step change, a regime shift, a clear outlier. Wrong on the cases that hide in the noise. That's why we don't stop here.
Constraints from thermodynamics.
A heat-mass-energy balance model cross-checks every raw signal against what physics actually allows. If supply-air temperature claims to have moved 4°C in 30 seconds with no input, physics says no — and the platform trusts physics over the probe.
Cause, not symptom.
Causal filtering distinguishes the one upstream change that just happened from the twelve downstream signals it inevitably moves. The operator sees one ticket — the root cause — not a storm of alerts about its echo.
Baselines that move with you.
Site-specific learned baselines update as occupancy patterns shift, tenants change, equipment ages. They learn the building you have now. They never override the physics — that's the whole point of layering.
Each method has a known failure mode. The engine plays them against each other so the failure modes don't compound — they cancel.
Anomaly detection
Six methods. One prioritised queue. One lifecycle.
A traditional BMS fires every alarm at the same weight. Within weeks, operators learn to mute the buzzer — and real faults disappear into the noise. The prioritised queue inverts the model: every anomaly is scored, classified, and dropped into a single ranked list. Twenty items, not two thousand. Each one carries a suggested action and a clear "why".
Sensor drift
Slow, sustained deviation from the expected baseline. The probe is still online. The values are still "in range". But the gap is growing — and if you train a forecast on this signal, you'll be confidently wrong by month three. Statistical-only platforms miss this. Physics-aware methods catch it on day three.
Stuck values
The probe returns the last good reading forever. The variance is zero, which a competent statistical method flags instantly — but BMS alarms, configured on thresholds, do not. A frozen sensor is a worse failure than a dead one, because the dashboard says nominal.
Energy spike
A statistically improbable consumption jump that cannot be explained by occupancy, weather, or scheduled load. Classified by severity, mapped to the smallest upstream equipment group that could have caused it. Surfaces fault-current, set-point drift, and badly behaving plant before it shows up on a bill.
Forecast deviation
Reality has diverged from the forecast beyond its confidence band. Either the forecast is wrong (model issue) or the building is behaving in an unforeseen way (operational issue). The platform tells you which — and acts on the second, retrains on the first.
Sensor offline
Reporting cadence has stopped or degraded. The fix is usually trivial — but a missing signal corrupts every model that relied on it, silently. Catching this early is mostly about responding to the absence of data, which is harder than responding to data.
Cross-signal incoherence
Two sensors that physics says must move together start contradicting each other. Each is in its individual band — the BMS lets both pass. But one of them is wrong, and the historical accuracy of each will tell you which. This is where the most subtle, most expensive faults live.
Anomaly lifecycle
Every resolved anomaly becomes training data for the next one. Operator overrides, reclassifications, and false-positive flags all feed back into the model — so the queue gets more relevant the longer you run it.
Forecasting & AI reasoning
Forecasts you can interrogate.
A forecast is only useful if the operator believes it. Belief comes from two things: a confidence band that tells the truth about uncertainty, and a reasoning layer that can answer "why this number, not that one?" in language the operator already uses.
Multi-horizon, with honest uncertainty.
Forecasts run at four horizons by default — 1 hour, 24 hours, 7 days, 90 days — each with an explicit confidence band. Wider band, less certainty: the operator can see at a glance whether to act on a number or watch it.
Conversational reasoning, grounded.
Ask the engine a question in plain language. Get an answer grounded in your telemetry, with the underlying metrics linked. Nothing is invented — if the data isn't there, the system says so. This is the discipline most building AI lacks: it's the difference between a useful assistant and a confident liar.
- Every answer cites the sensors and time-ranges that produced it.
- If a required signal is missing, drifted, or stale, the system raises it before answering.
- Conversational history is replayable for audit.
Compliance & ESG
Compliance that runs on real data, not quarterly audits.
Explore maps your sensors to certification criteria and watches them continuously. Evidence is collected as it happens, not reconstructed once a year. Breaches alert in real time. Auditors get an export, not a scramble.
Nordic Swan today. BREEAM, LEED, and EU-aligned frameworks on the same foundation.
Industries served
Sensor Intelligence, mapped to your sector.
Smart Buildings & Facilities
HVAC, anomaly, comfort, compliance.
ExploreEnergy & Utilities
Grid anomalies, demand forecasting.
ExploreManufacturing & Heavy Industry
Process telemetry, predictive maintenance.
ExploreHealthcare & Life Sciences
Critical-equipment monitoring and compliance.
ExplorePublic Sector & Infrastructure
Multi-site portfolios under one decision layer.
ExploreLogistics & Supply Chain
Environmental and fleet telemetry.
ExploreDeployment · Security · Data residency
Two ways to run it. Your choice on residency.
Enterprise — customer-hosted
Runs in your Kubernetes cluster or private cloud. Your infra, your SLAs, your governance. You own data and trained models.
Managed SaaS
Hosted by us on Hetzner's EU-based, ISO 27001 certified data centres. GDPR-native. Fast onboarding, no infra setup on your side.
Grounded by design
No PII — building and operational sensor data only. Grounded inference, deterministic guardrails. No vendor lock-in.
Resources
Field-tested perspectives.
Why this year, not next
A worse year to wait than the last one.
Sensor intelligence has been quietly cheap-to-deploy for a while. The reason operators are landing it now is that the cost of not deploying it has stopped being abstract.
CSRD has teeth now.
Continuous-measurement disclosure isn't a future requirement anymore — it's a current one. Buildings that can't produce continuous evidence are filing weaker reports, and stakeholders are reading them more carefully every quarter.
Margin compression on operations.
Energy is no longer a back-of-budget line item. A 4% silent drift in a 40-building portfolio is a six-figure annual cost, not a footnote. Catching it on day three instead of month six is the difference between a service ticket and a budget meeting.
Thresholds are tightening.
BREEAM In-Use, LEED O+M, Nordic Swan — every framework has moved toward continuous-evidence weighting in the past two recertification cycles. Annual-sample evidence still works, but it scores worse than it used to. That gap will not narrow.
See what your building data is telling you.
Book a 20-minute demo. We'll connect to a sample of your data and show you what Explore surfaces — live.
Senior engineer on the first call. No procurement-style intro round. Reply within one working day.