Your historian stores data.
Capture makes it mean something.
Production, quality, energy and maintenance teams all read the same data differently. The problem is not the dashboard. It is the layer underneath it.
"A dashboard does not lie. It shows exactly what the data model underneath allows it to show."
Trusted by production teams at
You have data. You don't have context.
Most production teams do not suffer from a lack of data. They suffer from data that was stored without the context needed to make it useful.
The historian captures everything. It explains nothing.
Tag values exist without batch, product, shift or machine state context. When something goes wrong, engineers spend hours reconstructing information that should have been captured in the first place.
Every team builds its own version of the truth.
Operations, quality, energy and maintenance each maintain separate dashboards with separate logic. Every meeting about numbers starts the same way: whose version is right? It rarely ends with an answer.
Root-cause analysis starts from scratch every time.
Without contextual history, no asset links, no event correlations, no shift boundaries, every investigation starts from scratch. The cause gets found eventually. The time spent finding it does not come back.
One clean dashboard
- Looks consistent across roles
- KPI definitions live inside individual reports
- Logic duplicated across tools and sites
- Every new dashboard adds maintenance overhead
- Data quality invisible to the viewer
One shared structure, properly built
- Tags with asset, batch and shift context attached
- Shared KPI definitions in one reusable layer
- Timestamp logic preserving event correlations
- Data quality labels visible at the source
- One source, multiple role-based views
How production teams stopped arguing about which numbers are right
Automotive, energy, steel: same pattern every time. Fragmented data, manual work, dashboards that disagree. Here is what Capture changed in practice.
Stellantis
High-volume multi-site production
Vanheede Environment Group
Wind, solar & grid management
Does this sound familiar?
A 45-minute demo shows exactly what the layer underneath your dashboards looks like when it is built right.
From stored data to one version your teams agree on
Dashboards become reliable when the logic behind them is built before the data reaches the screen.
Collect & contextualise
Capture connects to machines, MES, ERP, PLCs and sensors and attaches asset, batch, product, shift and event context at the moment of capture, not retroactively.
Store with structure
Each time-series value is stored with production context built in. The historian is queryable by default, no preparation required before any analysis.
Visualise from one source
Grafana, automated reports and role-specific views all pull from the same source. External BI tools connect via the data API. All teams read the same definition. No conflicting numbers.
Contextual Historian
Time-series data stored with asset, batch, shift and event context attached. No manual preparation before analysis. The context is there from the moment of capture.
Unified Namespace
One place where systems, teams and sites all read the same value. Dashboards, analytics tools and alerts pull from the same source. What one team sees, every team sees.
Raw and Cleaned Data: Both Stored
Capture preserves high-rate, unfiltered sample data for engineers and R&D alongside cleaned, structured data for operators and management. Most platforms store one or the other. Capture stores both, so your R&D team and your COO pull from the same historian at different levels of detail.
Open Dashboard Integration
Native Grafana support and an open data API for custom applications and external BI tools. No lock-in. If your team already uses a BI tool, it can query Capture directly.
Do your dashboards tell the same story?
A 45-minute session walks through how Capture structures the layer underneath your dashboards and shows what reporting looks like when every team reads the same numbers.
The demo covers
- The way production context attaches to time-series data at the moment of capture, not after the fact
- What it looks like when KPI definitions live in one place instead of scattered across reports
- Root-cause analysis when the history is already tagged and searchable, not reconstructed each time
- Grafana and external BI tools reading from one source instead of five separate feeds
- Tag structure, data quality labelling and how to query the historian without a data engineer in the room
- What reusing one shared source across lines, sites and tools actually saves in practice
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