Operations data is everywhere. Useful data is nowhere.
Most operations run across disconnected systems — ERP, SCADA, logistics platforms, CRMs, and spreadsheets nobody fully controls. Managers pull reports that are hours or days old. Decisions happen on incomplete information because getting complete information takes too long.
The data to run the operation well exists. It's just not in one place, in a form anyone can act on.
One view of the operation,
built around how decisions get made.
We build operational dashboards that connect to the systems your business already runs on — pulling live data from ERP, SCADA, WMS, logistics platforms, and custom applications — and surface it in views designed around real workflows.
Not dashboards built around how the data is stored. Dashboards built around how the operation gets managed.
How it runs.
Sources mapped
Identify every system holding relevant data: ERP, SCADA, WMS, TMS, CRM, spreadsheets, or custom databases. Understand refresh cadence and data quality upfront.
Connections built
APIs, database queries, OPC-UA bridges, or ETL pipelines built per source. Data pulled on the schedule it needs — real-time for operational, batched for reporting.
Data model defined
KPIs, metrics, and relationships defined for the operation. Normalization and calculated fields applied so data from different systems speaks the same language.
Views designed
Dashboards structured around role and decision context — what an operations director needs is different from what a floor supervisor needs in the moment.
Access controlled
Role-based visibility applied. Executives see the summary view. Managers see their domain. Field teams see what's actionable right now.
Maintained and extended
Systems change. Data sources get added, KPI definitions evolve, new teams get onboarded. The dashboard layer adapts rather than gets rebuilt.
What gets built.
Integration Layer
- REST API and webhook connectors
- Database query pipelines
- SCADA / historian bridges
- ETL and data normalization logic
Data Model
- KPI definitions and calculated metrics
- Cross-system normalization
- Refresh cadence per source
- Historical data retention policy
Dashboard Layer
- Role-based views (executive, ops, field)
- Mobile-friendly layouts
- Alert thresholds and notifications
- Export and scheduled reporting
What changes when this runs.
Decisions made on current data, not yesterday's report
Operational state visible across locations, teams, and systems simultaneously
KPIs become consistent and comparable across the organization
Problems surface before they compound into larger issues
Manual reporting effort replaced by automatic data collection
New managers and team members get up to speed faster with a shared view
Operations that run across multiple systems.
Manufacturing
OEE, production throughput, quality metrics, and downtime across lines and shifts
Logistics
Load status, on-time performance, carrier metrics, and exception rates across the fleet
Field Operations
Job status, technician utilization, parts inventory, and completion rates across territories
Multi-site Enterprise
Site-level KPIs rolled up into a single executive view with drill-down to location detail
Starting point for AI-powered operations intelligence.
A unified data layer is the prerequisite for anything more sophisticated. Once your operational data is connected, normalized, and flowing, it becomes the foundation for anomaly detection, forecasting, and AI-driven recommendations — not a future project, but a natural extension of infrastructure already in place.
It starts with seeing what's happening. From there, you can start predicting what will.
Running an operation across too many systems?
Tell us what data you have and what decisions you're trying to make faster. We'll scope out a dashboard that actually reflects how your operation works.