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“Your plant data cannot leave the plant, and your pipeline assumes it can”

Line data processed where it is born.

Same pipeline runs at the line and in the data centre without rewrite

Start a pilotHow it runs
The problem
  • OT data stays on the shop floor; cloud pipelines cannot reach it.
  • Every sensor speaks a different protocol and my pipeline is glue code.
  • Quality data is too big and too late; we find defects after the batch.
With PROTOR
  • Same pipeline runs at the line and in the data centre without rewrite
  • Reproducible quality KPIs from raw sensor streams
  • Tamper-evident quality and compliance records

From today to the first result.

What you do today

glue code between sensor protocols

Defects show up after the batch, every sensor speaks its own protocol, and OT data stays on the shop floor.

With PROTOR

One binary from the edge box to the cluster runs windowed quality KPIs on the line, resumes where it stopped, and reproduces any measurement for an audit.

Your first step

Run one line's sensor stream through a windowed quality KPI on an edge box.

one line
How it runs

Line data that resumes where it stopped. Step by step.

A checkpoint records progress, so a stopped run continues exactly where it left off.

PROTOR · Line data that resumes where it stoppedrunning
Log DataRandomDataProviderWatermark CheckpointCheckpointAdd ProcessingCompute FieldsProcessed Output CSVCSV Publisher
  1. Log DataRandomDataProvider
  2. Watermark CheckpointCheckpoint
  3. Add ProcessingCompute Fields
  4. Processed Output CSVCSV Publisher
nodes
data-gen → Generators.Random
watermark → Sync.Checkpoint
meta-tagger → ETL.Compute
csv-output → CSV.Publisher
resumes where it stopped
  1. Log DataGenerates seeded, repeatable test rows.
  2. Watermark CheckpointSaves progress so a run can resume where it stopped.
  3. Add ProcessingComputes new columns from expressions.
  4. Processed Output CSVWrites rows to a CSV file.

A real workflow from our end-to-end test suite; the designer view is illustrated.

What you get. From the first pilot.

Same pipeline runs at the line and in the data centre without rewrite

Reproducible quality KPIs from raw sensor streams

Tamper-evident quality and compliance records

Why your tools fall short.

×

Cloud IoT platforms need connectivity and raise sovereignty/egress issues

×

Time-series DBs store but do not transform or give audit trail

×

Node-RED is lightweight but not high-throughput or deterministic

What drives it
  • Quality and traceability demands
  • Edge compute growth
  • Data sovereignty and NIS2 pressure on industrial operators

Your pilot. Three steps.

1

Your data shape

Tell us what data you have, where it lives and what the result must be.

2

A scoped pilot

On your data and your hardware, with the success measure written down first.

3

A clear decision

You see the result against that measure — and keep every workflow and record.

Related roles in Regulated evidence.

Critical infrastructure and utilities OT data

OT analytics that stay in the plant.

Public-sector data platforms & smart city

City data under city control.

Cyber/SOC log analytics

Cut SIEM ingest, keep every log.

Healthcare & hospital data platforms

Hospital data, analysed in-house.

Beta · design partners wanted

Bring us your hardest data problem. Let's solve it together.

A short pilot on your data and your hardware. We agree on what success looks like before we start.

Start a pilotSee the numbers