Why Fleetera
The case for a single platform.
Measuring how a fleet performs has meant choosing between a vendor's fixed analytics and building the whole stack yourself. Fleetera connects your equipment, has specialized AI agents build the KPIs your fleet is measured by, and lets you act on the result from the same place. Your SCADA and historians stay where they are. This is about what you do with the data across the fleet.
The alternative
What you'd have to build yourself
Building this yourself means standing up six systems and then running them. Each is a project in its own right, and each keeps needing attention long after launch.
Protocol drivers + edge buffering
Every site speaks its own protocol, whether OPC UA, Modbus, MQTT or REST, and the link to the cloud drops. You need a driver for each, a buffer at the site that survives restarts and long outages, and a plan for what happens when it fills.
Streaming pipeline + tiered storage
Industrial telemetry arrives fast and never stops. You need a pipeline that takes it in, fast storage for live reads, cheaper storage for years of history, and one way to query both. That is a data-engineering project on its own.
Alert evaluation engine
Thresholds are the easy part. A real fleet also needs rules for a value that stays out of band, a signal that goes stale, a change that comes too fast, and conditions that only matter together. They have to run continuously across every site without burying operators in noise, and most teams underestimate how hard that is.
Access control + audit trail
Operators and viewers need different rights, sign-in has to be secure, and every change needs a record nobody can edit afterwards. Industrial buyers ask for all of it before a site goes live.
Acting on equipment across sites
Changing a setting on one machine is easy. Changing it safely across dozens of sites, with every risky change checked before it runs and a record of who did what and when, is a system in its own right.
KPI & analytics computation
A KPI is not a dashboard query. It has to run continuously over live and historical data, apply one definition to every similar asset instead of being rebuilt per site, recompute history when the definition changes, and be stored so you can chart and export it like any raw signal. A few formulas become a standing data-engineering job.
Compare
Fleetera vs the alternatives
How Fleetera compares with a conventional asset performance platform, and with building the stack yourself.
| Capability | Fleetera | Conventional APM platforms | DIY cloud-IoT stack |
|---|---|---|---|
| Who builds your analytics | AI agents, from a plain-language brief, Early access | The vendor's fixed set | You and consultants, from scratch |
| A new KPI for an asset type | Describe it once, and it measures every asset of that type, Early access | A services engagement | A data-engineering ticket |
| Equipment coverage | One model across any vendor's equipment | Multi-vendor, but new equipment waits on the vendor | Whatever you integrated |
| Time to first telemetry | Minutes | Weeks of onboarding | Months of build |
| Protocols out of the box | OPC UA, Modbus, MQTT, REST | Varies by vendor | Build each driver |
| Setup changes without a site visit | Built in | Varies | Build it yourself |
| Fleet-wide alerting | Fleet-scoped rules, one definition | Within the vendor's alarm framework | Assemble and tune |
| History and export | Recent and long-term history, exportable | Often tied to the vendor's store | Operate it yourself |
The guides behind these rows explain each mechanism in full:
- Who builds your analytics: the guide covers what separates an agent from a scheduled job, and what stays a human decision.
- A new KPI for an asset type: the guide covers what a KPI definition has to say about missing inputs, counter resets, and history that changes after the fact.
- Equipment coverage: the guide covers what an operational data twin models, and how it differs from the two other things called a digital twin.
- History and export: the guide covers what a data fabric does that a historian and a data lake do not, including when a fabric is the wrong answer.
The cost of waiting
History you won't get back
Your equipment is producing data right now, and it only has value if you keep it. A reading you do not record today is missing from every comparison you make next year.
Every month spent stitching integrations together is a month of data that never gets collected. Start now, and the history is there when you need it.
Get started
See it for yourself
Request early access and we'll onboard your fleet, or walk through the platform with our team.