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Fabric · Connect & ModelLive

Connect and model your whole fleet.

Fabric is the industrial data ops layer: one runtime per site connects your equipment, digital twins in the cloud model it, and every reading streams into tiered history you query as one.

Connect any equipment. Manage it from the cloud.

A single lightweight runtime acquires data at the site over four native protocols, buffers it locally, and stays configured and current from the cloud.

01

One binary per site

The runtime ships as a single Go binary deployed as one container at the site. No stack to assemble, no per-protocol services to wire together.

02

Built-in industrial drivers

OPC UA with subscriptions, plus Modbus TCP and RTU, MQTT, and REST. Four native protocols, no plugins to install.

03

Store-and-forward buffer

A dual hot and cold buffer keeps recording at the site through restarts and through a lost connection, and streams what it holds once the link returns. A gateway that cannot reach the cloud is still collecting.

04

Cloud-managed configuration

Define connectors and bindings in the cloud; the runtime picks up its desired configuration through device-shadow sync in about 30 seconds. No manual edits at the edge.

05

Automatic updates

Gateways keep themselves current. New runtime versions roll out automatically, so the fleet stays on a known-good build without a site visit.

06

A private, isolated network per tenant

Every gateway connects out over an encrypted, per-tenant private link. No inbound ports, no firewall holes, and nothing at the site exposed to the public internet.

Larkspur Wind Farm · GW-01

Edge deployment

v1.6.0
StatusOnline
VPN IP100.64.0.5
RuntimeFleetera
Architectureamd64
Heartbeat8s ago
Edge Runtime
Built-in industrial drivers
delivering
OPC UA
Modbus
MQTT
REST
Store-and-forward buffer38%
Event Log
[12:03:58]REGISTEREDedge instance bound
[12:03:59]VPN CONNECTED100.64.0.5 · VPN up
[12:04:01]SERVICES STARTED4 drivers online
[12:04:31]HEARTBEATok · 768 signals reporting

A living model of your fleet.

Raw signals become named, typed variables on real assets. The twin is what makes everything downstream fleet-wide instead of per-device, from dashboards to alerts to analytics: one definition on the asset type follows every current and future asset.

Sites, assets, sub-assets

Model your operation the way it actually is: sites contain assets, assets contain sub-assets. Every reading lands on the exact piece of equipment it came from.

Templates with typed variables

Define an asset type once, with its variables, units, and structure, then stamp it across the fleet. Every instance stays consistent, and fleet-wide queries stay possible.

AI-assisted binding, human-approved

Point Fleetera at an OPC UA server and it suggests how each raw signal maps to your variables. You review the matches and approve. Nothing binds without your yes, and you can always browse and bind by hand.

Industry-agnostic categories

Wind turbines, battery racks, pumps, air handlers: categories describe any equipment in any industry, so the same model works across your whole portfolio.

Bindings
Variables mapped to connector addresses · Larkspur Wind Farm
4 AI-matched
VariableAddressEnabledSource
active_powerTurbine A07OPC UAns=2;s=ActivePowerAI
rotor_speedTurbine A07OPC UAns=2;s=RotorSpeedAI
blade_pitchTurbine A07OPC UAns=2;s=BladePitchManual
nacelle_tempTurbine A12OPC UAns=2;s=NacelleTempAI
socBESS Rack 3Modbus40 001 / holdingManual

From raw readings to a queryable history.

Telemetry is validated and kept in per-asset order on the way in, stored across hot and cold tiers, and served back through a single query that spans your entire history.

01

Validated ingestion

Every reading arrives tagged with its tenant, site, asset, and variable. The pipeline preserves order per asset.

02

Hot time-series tier

Recent data lands in storage built for industrial time series, giving fast point lookups and quick range scans across millions of readings at full resolution.

03

Automatic rollups

Hourly and daily aggregates are computed continuously, so historical trends come back instantly without scanning raw data.

04

Long-horizon cold archive

Older data rolls into a cost-efficient cold archive that keeps your history queryable for the long term, not just the recent past.

05

Tier-transparent queries

Ask for any asset, variable, and time range with a single query. The engine picks the right tier automatically and stitches hot and cold data into one result.

06

CSV and JSON export

Pull any query window out as CSV or JSON to feed reporting, notebooks, or downstream systems.

Ingestion
Validated readings per minute
streaming
18,420msg/min
Validated readings per minute
Stream engine
per-asset order
tenant-taggedper-asset order
Hot tier30d
Time-series · full resolution + rollups
12.4M readings
Cold tier3yr
Archive · long-horizon, queryable
1.31B readings
One query spans both tiers, routed by time windowCSV · JSON export

Hot and cold, queried as one.

Recent data stays at full resolution for live work; older data rolls into hourly and daily aggregates. Retention is a query window, not a delete: queries beyond the window narrow gracefully to the resolution you have, rather than erroring.

Talk to us about retention

Get your first site online.

Stand up the runtime, point it at your equipment, and watch modeled telemetry land in the cloud.

Fabric: Connect & Model | Fleetera