Nexigna AI monitoring
// Nexigna - the AI brand of GraNext

AI that understands your plant, not just your inbox.

Plenty of companies use AI to polish text or to search. Nexigna does the opposite: AI on top of your real data, meters, sensors, EMS and logs. We link those signals, spot problems and explain them in plain language. The AI prepares, the technician decides.

01

An AI layer for technical installations

// what is Nexigna

Nexigna links machine data, alarms, history and documents. So you don't just get an alert, but context, a likely cause and a concrete next step. The examples below run on our own geothermal cascade in Munich.

02

Heat pump health monitoring

// featured

The residential complex in Munich runs on six geothermal heat pumps (2x3 cascade). Identical units, so the AI can compare them against each other. The moment one drifts off, it stands out.

Unit 1Healthy
COP (7d)4.6
Approach1.8 K
Starts/h2.1
Unit 2Healthy
COP (7d)4.5
Approach1.9 K
Starts/h2.3
Unit 3Alarm
COP (7d)4.1 ↓
Approach4.9 K ↑
Starts/h2.4
Unit 4Watch
COP (7d)4.0 ↓
Approach2.2 K
Starts/h5.8 ↑
Unit 5Healthy
COP (7d)4.6
Approach1.7 K
Starts/h2.0
Unit 6Healthy
COP (7d)4.5
Approach1.9 K
Starts/h2.2
AI analysisScheduled health review - twice a week - last run Tue 28/05, 06:00
!

Unit 3 - heat exchanger likely fouling. Approach temperature rose from 1.9 K to 4.9 K over 6 days, COP -11%. Low pressure normal, so no refrigerant indication. Units 1, 2, 5, 6 stable.

~

Unit 4 - compressor short-cycling. Starts per hour up to 5.8 (peers ~2.2). Check buffer or flow control.

Source stable. Source temperature and flow within baseline across all six units.

03

An alarm becomes an explained cause

// interlinking
1 - Alarm
High pressure - Unit 3

The signal

Unit 3's high-pressure cut-out tripped. A raw alert with no context.

2 - Interlinking

AI links the data

Pulls the data window around the alarm: condenser temperature, flow, approach temperature, 6-day COP trend, compressor starts. Compares unit 3 with units 1, 2, 4, 5, 6.

3 - Root-cause

Ranked cause

High · 85%
Condenser-side flow down + approach +3.1 K. Heat exchanger fouling.
Low
Refrigerant loss. Low pressure normal, so unlikely.
The AI prepares and checks. The technician decides. We start with health scores, anomaly detection and alarm triage, no overblown promises of fully automated maintenance.
04

Same approach, different data

// more broadly applicable
01

Battery (InduBa)

Alarm triage on BMS/PCS, monthly report and an explanation of why the battery didn't charge.

02

FarmOps field log

Turns voice notes into a structured activity log, checks for missing fields, ensures spraying compliance.

03

Energy bill parser

Reads tariffs, capacity charges and volumes, compares months, flags anomalies.

04

Quote comparison

Reads 3 to 5 quotes, pulls out kWh, kW, warranty, EMS, NOB and exclusions into a table.

05

Service and alarm triage

Groups raw alarms and logs, estimates the cause and escalates only what matters.

06

Private knowledge assistant

Answers from your own documents and project folders, with source references.

We show the principle here, not the full method. How we link, model and validate the data is our engineering. We discuss that in a conversation.

// CONTACT

AI on your data, not just your inbox.

Tell us what data you already have (sensors, EMS, invoices, documents). We'll show which workflow delivers value fastest.