Industrialmaintenance that plans itself.

We turn machine health signals into executable maintenance plans, automatically.

Contact Sales See how it works

Every system did its job. The machine still stopped.

The sensor fired the alert. The CMMS had no work order. The ERP had the part, on a six-week lead time. The right technician was on a double shift. Nobody had the full picture. The line went down.

06:14
SENSOR

Vibration alert fires on pump A-07

06:30
CMMS

No work order exists

07:05
ERP

Part: 6-week lead time

08:20
SCHEDULING

Technician unavailable

09:00
MANAGER

Critical call. Incomplete picture.

+3 DAYS
LINE DOWN

Unplanned stoppage.

The costs your operation carries today

01

The machine stops. The cost starts.

Unplanned downtime runs €30k–€260k per hour depending on your sector. The repair is rarely the expensive part.

02

The part has a six-week lead time.

You find out after the failure event. Now you're expediting at a 25–45% premium from a vendor you've never qualified.

03

The right technician was on a double shift.

Emergency labour runs 3–5× the planned rate. The window existed. Nobody had the full picture to plan into it.

04

Four systems. One decision. No single view.

10–15 hours a week bridging SCADA, CMMS, ERP and scheduling by hand. Every alert requires four conversations to become a work order.

The problem isn't that machines fail without warning. Nothing turns the warning into a plan.

From signal to work order, automatically.

Your APM fires the alert. Your CMMS needs a work order. Your ERP has the parts. Your scheduling tool holds the technician calendar. Talpeye connects them and turns what they know into a plan you can execute.

Know weeks before it fails.

Rolling failure probability with uncertainty bands. The model is zero-shot: it needs no failure history and no per-machine setup. The median warning lands four weeks before the failure.

4 weeksmedian warning ahead of failure

Do in minutes what used to take all shift.

Four surfaces. One decision loop. Built for the people doing the work.

A ranked list of assets by risk, failure mode, and urgency. Olly scores each asset continuously and shows only what needs a decision today.

app.talpeye.ai/dashboard

The decision layer above your existing stack.

No new infrastructure for instrumented sites. Talpeye reads from the systems you already run and writes results back into them.

01

No new hardware

Connects to your existing SCADA, PLC, and IoT sensors for instrumented sites.

02

No rip-and-replace

Sits above your CMMS, ERP, and scheduling tools. Reads from them, writes back into them.

03

Auditable by design

Every recommendation traces back to the signal that triggered it. Every decision is logged.

Reads from · Writes back to
SCADA
PLC
OPC-UA
MQTT
Modbus
CareTrack
IBM Maximo
SAP PM
Infor EAM
Oracle EAM
Microsoft Dynamics
Fiix
UpKeep
ServiceMax
SAP ERP
IoT sensors
SCADA
PLC
OPC-UA
MQTT
Modbus
CareTrack
IBM Maximo
SAP PM
Infor EAM
Oracle EAM
Microsoft Dynamics
Fiix
UpKeep
ServiceMax
SAP ERP
IoT sensors

Validated on real industrial data before any commercial conversation.

53 machines across five industries, from five named industrial operators. 27 real failures, every one caught before it happened, with a median warning of 4 weeks.

53
Machines tested
27
Real failures
100%
Detection rate
4 wks
Median warning
OperatorEquipmentIndustryResult
Metro do Porto
Air compressors
Public transit
Caught ahead of failure
EDP
Onshore wind turbines
Renewable energy
Caught ahead of failure
Fraunhofer IWES
Multi-farm wind turbines
Renewable energy
Caught ahead of failure
Petrobras
Subsea oil wells
Oil & gas
Caught ahead of failure
Scania
Heavy-truck engine components
Commercial vehicles
Works — per-site calibration needed

The coordination gap sounds familiar?

We're running proof of concepts with a small number of industrial operators. If this sounds like your operation, we'd like to talk.

We reply within 2 business days.
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