Operational Intelligence
for Systems in Motion.

Know which asset is at risk, why, and what to do about it — before it disrupts operations.

See how it works Let's talk
Customers and Partners
DB Regio logo
Angel Trains logo
Grand Central Railway logo
LNER Future Labs logo
Leonardo logo
Innovate UK logo
What We Do
Millions of records a week.
Read by no one — until
Amygda.

We connect to the systems you already run — CMMS, fault codes, technician notes, sensor feeds — and read them at scale. Every asset in your fleet gets a Risk Score, updated as data arrives, with the reasoning behind it.

No new sensors required
Explainable, not a black box
Live before every shift
Results

Validated in the field.

DB Regio logo
Rail — DB Regio, Germany
80%

of at-risk trains identified 60+ minutes before departure.

Deutsche Bahn Innovation Award · 1,600 fault codes reduced to 57 categories · 250M+ records processed
Read the case study →
Grand Central Trains logo Angel Trains logo Chrome Angel Solutions logo Innovate UK logo
UK Rail — Innovate UK Project
84%

of engine failures detected in advance, on Grand Central's Class 180 fleet.

96% less manual analysis time · 26% potential reduction in delay & cancellation costs · Chrome Angel Solutions, Angel Trains, Grand Central, backed by Innovate UK
Read the case study →
How It Works

From raw data to a risk score your team can act on.

01

Connect your data

CMMS records, fault codes, technician notes, sensor feeds — whatever you already run. No new sensors, no new hardware.

02

AI reads it at scale

Millions of records, free-text logs included — read in the time it'd take a person to open one file.

03

Get a ranked risk score

Every asset in your fleet, ranked by risk, with the contributing factors behind each score — not a black box.

Tell us what's at risk in your operation.

We'll show you how Amygda works with the sensor data and maintenance logs you already have.

Let's talk
SET‑2231 82 · At risk
Why
Rising vibration on axle 2, plus 3 open fault codes in the last 48 hours.
Updated as data arrives