Energy intelligence from data you already have

One analytics engine, three data situations. What it can tell you depends entirely on how often your meters report — so each route below states its ceiling as plainly as its capability.

Use case 1

Enterprise — sub-metered

Large estates with a BMS or aM&T system already in place

Not built

Circuit-level attribution and event-level diagnostics. The step up that needs devices on site.

Resolution
1 Hz, sub-metered circuits
Data route
BMS / aM&T integration, or dedicated sub-meters

What it can tell you

  • Everything the meter-data tier does
  • Attribute load to circuits and plant, not just to a site
  • Event-level diagnostics — “the air handling unit started at 04:12”
  • Demand and power-factor analysis

What it cannot

  • Be delivered without hardware and on-site engineering
  • Compete on onboarding friction — that advantage is the other tiers’
What this needs →
Use case 2

Enterprise — meter data only

UK SME and enterprise multi-site operators with no energy team

Live

Waste findings, drill-down and statutory reporting from data the customer already has. No hardware, no install.

Resolution
Half-hourly settlement data
Data route
Consented DCC access, supplier API, or CSV export

What it can tell you

  • Separate always-on baseload from trading load
  • Quantify after-hours waste and schedule mismatch in £/year
  • Detect baseload drift and step changes, weather-normalised
  • Re-price consumption under an alternative tariff

What it cannot

  • Tell you which appliance — 48 samples a day cannot resolve one
  • Distinguish actual from estimated reads where the source omits the flag
Open dashboard →
Use case 3

Residential

Households with a UK smart meter

Ready — needs data

The same engine on a home meter. Occupancy hours replace opening hours; after-hours waste becomes “energy used while the house should be asleep”.

Resolution
Half-hourly, or ~10 s with a Consumer Access Device
Data route
n3rgy consumer API (consented DCC)

What it can tell you

  • Separate standing load from occupied-hours load
  • Flag overnight consumption that should not be there
  • Detect a rising standing load — a failing appliance
  • Run entirely on the same detectors, configured differently

What it cannot

  • Do appliance-level disaggregation from half-hourly data
  • Weather-normalise without a local weather series configured
  • Produce numbers as large as a commercial site — expect tens of pounds, not thousands
What this needs →

System status

What is loaded right now, including the things that quietly break a demo.

Analysisready — 13 findings
AI advisorgemini-3.6-flash
Emission factorsdesnz-2025 (unverified — reports watermarked DRAFT)
Market packgb (unverified)