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 you are actually measured by, and lets you act on the result from the same platform. 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
Replacing Fleetera means standing up, and then operating, six hard systems. Each is a project in its own right, and each one keeps running long after launch.
Protocol drivers + edge buffering
Every site speaks a different dialect, whether OPC UA, Modbus, MQTT or REST, and the link to the cloud drops. You need drivers for each plus a store-and-forward buffer that survives restarts and long outages, and an honest answer for what happens when it fills.
Streaming pipeline + tiered storage
Industrial telemetry arrives fast and never stops. Standing up streaming ingestion, a hot time-series store for live reads, and a cheap cold tier for history, and keeping queries transparent across both, is a data-engineering project on its own.
Alert evaluation engine
Thresholds are the easy part. Hysteresis, duration windows, staleness, rate-of-change and multi-condition rules, evaluated continuously across a whole fleet without drowning operators in noise, is a streaming-systems problem most teams underestimate.
Multi-tenant access control + audit
Isolating each customer's data, scoping roles to operators and viewers, signing tokens, and keeping an immutable audit trail is table stakes for industrial buyers. It is also unglamorous, security-critical work you only notice when it is missing.
Fleet config management
Pushing configuration to gateways at dozens of sites, reconciling drift, and gating risky commands by severity, safely and with a full record of who changed what, is the difference between a demo and something you can run in production.
KPI & analytics computation
Business KPIs don't stop at a dashboard query. Computing them continuously across hot and cold history, applying one definition to every similar asset instead of rebuilding it per site, backfilling results when a definition changes, and keeping the outputs queryable like raw telemetry turns a few formulas into a standing data-engineering workload.
Compare
Fleetera vs the alternatives
How an agentic APM platform stacks up against a conventional one, or against assembling the stack yourself.
| Capability | Fleetera | Conventional APM platforms | DIY cloud-IoT stack |
|---|---|---|---|
| Who builds your analytics | Your own AI agents, and you approve each one, Early access | The vendor's fixed set | You and consultants, from scratch |
| A new KPI for an asset type | Describe it, approve it, it applies to 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 |
| Cloud-managed edge config | Built in | Varies | Roll your own |
| Fleet-wide alerting | Fleet-scoped rules, one definition | Within the vendor's alarm framework | Assemble and tune |
| History and export | Hot and cold tiers, exportable | Often tied to the vendor's store | Operate it yourself |
Where a row rests on a mechanism, the reference guide behind it is written up in full, for engineers and operations teams rather than for a search engine:
- Who builds your analytics: the guide covers what separates an agent from a scheduled job, and where the approval boundary sits.
- 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
The data your equipment is producing right now is only valuable if you capture it. Telemetry you don't record today is history you won't have tomorrow: the baseline you can't compare against, the trend you can't see coming, the model you can't train.
Every month spent stitching together integrations and maintaining fragile pipelines is a month that data goes uncollected. Starting now means that when you're ready to act on your fleet, the history is already there waiting.
Get started
See it for yourself
Request early access and we'll onboard your fleet, or walk through the platform with our team.