01
OT-Native Connectors
Native OPC UA, MQTT, Modbus, and historian connectors with sub-second writeback into the warehouse — no separate time-series DB to operate.
Solutions · ManufacturingGENEDATA / 01
From the plant floorto the bigger picture.
Connect equipment, production, and supply-chain data to put operational decisions in their full business context.
Shared context · Lineage · Governance
Connected
Your sources
Equipment signals
Production records
Supply-chain data
Connected intelligenceIndustrial intelligence
Governance
Business impactOperational decisions
Shared contextLineageGovernance
02
Predictive Maintenance
Pre-built models for vibration, temperature, and pressure anomalies — auto-tuned per asset, with maintenance work orders pushed to your CMMS.
03
Supply Chain Visibility
ERP + EDI + carrier feeds unified into a tier-N supplier graph for risk scoring, alternative sourcing, and emissions reporting.
Built On
The products behind this solution.
OT data and IT data have different shapes, owners, and network constraints — the platform's job is to make them one record without disturbing the plant floor.
GeneFlow
Connects to PLCs, historians, and MES over OPC UA, MQTT, and Modbus, buffering at the edge through network loss.
Learn moreGeneCatalog
Maps tags to an ISA-95 asset hierarchy so a reading is attributable to a machine, line, and site rather than an opaque tag name.
Learn moreCortex SQL
Runs golden-batch comparison, yield analysis, and supply-chain risk scoring across OT and ERP data together.
Learn moreCortex AI
Predicts failure per asset against its own baseline and pushes work orders into the CMMS teams already use.
Learn moreIndustry use-case map
Manufacturing workflows across one governed operating model.
These reference workflows connect the business decision to the data, controls, platform surfaces, and people required to operate it in production.
ManufacturingReference workflow
Predictive maintenance
Learn each asset's operating baseline from sensor, historian, maintenance, and production records, then route verified risk into maintenance planning.
How the work moves
- 01
Connect the signal
Sensors, historians, and asset telemetry · MES, quality, and production records · ERP, suppliers, logistics, and CMMS
- 02
Apply control
Data quality contracts · Operational monitoring · Point-in-time versioning
- 03
Build and deliver
GeneFlow Engineering · Data Science & MLOps · Observability & Operations
- 04
Decide and act
Condition-based interventions tied to asset context and production priority.
Participating roles
Platform surfaces
Business outcome
Condition-based interventions tied to asset context and production priority.
Explore the full industry solutionOT/IT Architecture
The plant floor and the ledger, on one path.
OT data historically stops at the historian and IT data starts at the ERP, with a spreadsheet in between. Landing both on one substrate is what makes a maintenance prediction traceable to the work order it triggered and the margin it protected.
Deployment Path
Tap the line, model the asset, predict, act.
Nothing on the factory floor is replaced. The platform reads from the equipment and systems already running, and writes back into the tools operators already use.
Tap existing equipment
Connect to PLCs, historians, and SCADA over OPC UA, MQTT, and Modbus. No controller changes, no line downtime to integrate.
Model the asset
Map tags to an ISA-95 asset hierarchy so a reading is attributable to a machine, line, and site rather than an opaque tag name.
Predict per asset
Vibration, temperature, and pressure models auto-tune to each machine's own baseline rather than a fleet-wide threshold.
Act in the existing workflow
Predictions become work orders in your CMMS and planning signals in your ERP, so the insight reaches the person who acts on it.
What You Get
Built for plant reality, not a reference architecture.
Intermittent connectivity, thirty-year-old equipment, and a hard requirement never to disrupt production are the normal operating conditions.
Store-and-forward edge
Edge gateways buffer locally through network loss and reconcile on reconnect, so a WAN outage costs no telemetry.
Per-asset baselines
Each machine is modelled against its own history, so an old press and a new one do not share a false threshold.
Golden-batch analysis
Compare any production run against the best historical batch across every process variable to find yield loss.
Tier-N supplier graph
Map dependencies beyond direct suppliers to see which sub-tier disruption actually threatens a line.
Energy attribution
Attribute consumption to line, product, and shift, turning an aggregate utility bill into a per-unit cost input.
ISA/IEC 62443 alignment
Segmented, read-oriented OT connectivity aligned to 62443 zones, so integration does not widen the attack surface.
Who Benefits
Who this is for.
Operations, quality, and supply chain usually argue from three different datasets. This gives them one.
Plant Operations
Replace the maintenance calendar
Move from fixed-interval servicing to condition-based intervention, with predictions tied to each asset's own behaviour.
Unplanned downtime becomes a scheduled window.
Quality
Find the variable that moved
Compare a failing run against the golden batch across every process variable rather than the handful the historian charts.
Root cause in hours instead of a shift review.
Supply Chain
See the disruption two tiers down
Model supplier dependencies beyond tier one so a sub-supplier event surfaces before it stops a line.
Sourcing decisions made with lead time to spare.
What Changes
What changes when OT and IT share a substrate.
The historian and the ERP each hold half the answer, and the join is usually a person with a spreadsheet.
| Dimension | Before Genedata | With Genedata |
|---|---|---|
| OT data | Trapped in per-site historians | Streamed to one governed store, still available locally |
| Maintenance | Fixed intervals plus reactive repair | Condition-based, per-asset predictions |
| Quality analysis | Manual comparison of a few charted variables | Golden-batch comparison across all variables |
| Supplier risk | Visibility stops at tier one | Tier-N dependency graph with risk scoring |
| Connectivity | A network outage loses the window | Edge buffering reconciles on reconnect |
MQTT · OPC-UA · KafkaOT Ingest
Store-and-forwardEdge Sync
Site · Line · Machine hierarchyAsset Models
Segmented, read-orientedOT Connectivity
The next step
From sensor to ledger, on one substrate.
Genedata Manufacturing gives operations, quality, and supply chain teams a single, governed view of every asset, line, and shipment — turning data fragmentation into measurable yield, uptime, and margin.
FAQ
Manufacturing, answered.
What plant operations and IT ask before connecting the factory floor.
Do we have to change anything on the line?
No. The platform reads from PLCs, historians, and SCADA you already run over standard protocols. There are no controller changes and no line downtime to integrate.
What happens when the site loses connectivity?
Edge gateways buffer locally and reconcile on reconnect, so a WAN outage costs no telemetry — which matters because the sites with the worst connectivity are usually the ones you most want data from.
Is this safe from an OT security standpoint?
Connectivity is segmented and read-oriented, aligned to ISA/IEC 62443 zones, so integration does not widen the attack surface into the control network.
How do predictions avoid false alarms across mixed-age equipment?
Each asset is modelled against its own history rather than a fleet-wide threshold, so a thirty-year-old press and a new one are judged on their own behaviour.
Can we see supplier risk beyond tier one?
Yes. ERP, EDI, and carrier feeds build a tier-N dependency graph, so a sub-supplier disruption surfaces before it stops a line rather than after.
Related content
Architecture
OT Integration Architecture
Protocol support, edge buffering, and ISA-95 asset modelling.
CatalogPredictive Maintenance Models
Vibration, thermal, and pressure models with per-asset tuning.
GuideSupply Chain Graph
Building and scoring a tier-N supplier dependency model.
RelatedObservability
The signal layer underneath predictive maintenance.
Take the next step
Connect one line.
Instrument a single production line and see prediction to work order in a working session.