KMS Technology acquires Addepto. Read the Full Press Release
KMS Technology acquires Addepto. Read the Full Press Release
Aviation generates some of the most complex operational data of any industry. All of them run in parallel, but are rarely connected. Turning that data into decisions requires the infrastructure to unify siloed sources, the domain knowledge to understand they mean in a safety-critical context, and the implementation experience to deploy reliably into regulated operational environments. That is what KMS brings to every aviation engagement.
In aviation, technology is being absorbed into the operational core. This is what’s already happening across the industry, as measured in the numbers below
Recorder feeds, ACARS traffic, sensor streams, maintenance logs, crew platforms, and revenue systems all run in parallel with almost no shared language between them, and unlike most industries, the cost of getting it wrong here is never just financial.
Reactive maintenance is expensive, disruptive, and largely avoidable; when components fail without warning, the financial cascade far exceeds the cost of proactive intervention.
Disrupted networks compound quickly without real-time decision support across aircraft, crew, and passenger constraints.
A structural skills shortage leaves institutional knowledge concentrated in a few experienced engineers, limiting how much complexity existing teams can absorb.
Conservative stock buffers and emergency procurement tie up capital without guaranteeing the right parts are in the right place when needed.
Aircraft Health Monitoring
Operations Control Support
Flight Data & Safety Analytics
Airport Digital Twin & Ground Operations Intelligence
Passenger Experience & Knowledge Automation
MRO & Inventory Forecasting
A live health score for every tracked component, built continuously from sensor data, flight cycles, and maintenance history, flags what needs attention, when, and why, long before it becomes a delay.
Work orders generate automatically once a threshold is crossed, and the fleet-wide picture updates in real time instead of at the next scheduled check.
Getting predictive maintenance to this level takes more than a standalone model; it takes AI-native product engineering built directly into your existing maintenance systems, not bolted on beside them.
Aircraft, crew, and passenger constraints are modeled together in real time, re-ranked continuously as the situation changes, so a controller facing a cancelled rotation sees a ranked set of recovery options instead of building one from scratch under pressure.
Recovery is measured in minutes, not the hours a manual replan usually takes.
Pattern detection across full-fleet flight data surfaces the quieter parameter interactions that tend to precede safety-relevant events.
Safety teams get a prioritized list of what deserves attention, not a firehose of flagged events.
A digital twin layer brings stand assignments, turnaround status, and baggage flow together into a single live view, so ground teams see what’s actually happening on the apron — not what the schedule says should be happening. It’s the same thinking behind our luggage tracking and digital twin work with SITA and our airport stand assignment optimization work.
Generative AI trained on your own operational knowledge can field passenger questions directly and generate structured documentation on its own.
The same approach is behind an AI-powered passenger assistance bot built with SITA and an AWS-based document generation system built for an aviation coordination client.
Rolling demand forecasts, built from utilization rates, component age, removal history, and supplier reliability, turn into automated procurement triggers, so parts get ordered based on where the real risk sits across the fleet, not against a static shelf quantity nobody’s revisited in years.
Dispatch Reliability Goes Up
Catching failure risk early turns emergency maintenance into planned maintenance, protecting on-time performance and cutting the compensation and overtime costs that come with grounding an aircraft.
MRO Spend Gets More Predictable
Parts get positioned based on actual risk instead of conservative buffers, bringing total MRO cost down without leaving anyone short on inventory when it matters.
Every Engineer Performs Like Your Best One
Diagnostic time shrinks, documentation gets automated, and institutional knowledge becomes something the whole team can draw on.
Compliance Built In, Not Bolted On
Because aviation maintenance AI counts as high-risk under the EU AI Act, we build audit trails and human-oversight checkpoints into the system from the start, so it holds up under FAA and EASA scrutiny rather than failing an audit after go-live.
Let’s transform your business challenges into scalable AI solutions that deliver measurable impact.
These aren’t proofs of concept sitting in a lab. Each one is running in a live airport or airline environment today, built alongside teams who had to keep operating while we changed how they worked.
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