From historian tags to decision capital
We don’t just build analytics. Connectivity, analysis and AI are the means; the goal is decision capital — trusted insight your team uses to make faster, better calls, built for return and reliability.
Analytics
Analytics built primarily in Seeq, or custom where it fits, on the data your historians already hold — with the context that makes it useful. Operators add reason codes, downtime comments and reconciliations, so their experience reaches the people making corporate decisions.
Outcomes
- Engineers analyze their process without building an analysis
- Interactive reports and live process monitoring
- Live KPI calculations, forecasting and updated schedules
- Custom add-ons for analytics and reporting
OEE & downtime
Availability, performance and quality from machine states you already record, with reason codes and downtime context captured by operators.
SPC & process monitoring
Live control charts with rules tuned per variable, and alerts routed to the people who own them.
Batch analytics
Phase-aligned overlays, golden profiles and cycle time broken down by phase and equipment.
Machine learning
Soft sensors, regression models and predictive monitoring built where engineers can inspect them.
Digital transformation
Reports and dashboards that combine process data with production, quality, financial and operator context — one certified version of each KPI, from the line to the leadership meeting.
Outcomes
- Plant and corporate looking at the same numbers
- No more monthly spreadsheet rebuilds
- Reports designed around the decisions in each meeting
Power BI semantic models
Data models shaped for time-series, assets, shifts and events, with row-level security for multi-site access.
Operations & executive dashboards
Line-level views for shift meetings and roll-ups for site and corporate leadership.
Seeq Organizer reports
Engineering reports that update themselves from live analyses and publish on a schedule.
Report automation
Shift, daily and monthly reports generated and distributed without anyone copying numbers.
Data connectivity & architecture
The foundation everything else stands on: connected sources, a consistent asset model, and enterprise systems that are governed, secured and supported after go-live.
Outcomes
- One structure shared by historian, analytics and BI
- Analyses that deploy to every like asset at once
- Systems IT is comfortable supporting long-term
Source connectivity
Historians, MES, LIMS and relational sources connected and validated end to end.
Asset structuring
Asset hierarchies built custom or aligned to ISA-95, whatever fits your industry, with templated calculations.
Naming & governance
Tag and asset naming standards, access models and change control that hold up across sites.
Enterprise deployment & support
Installation, upgrades, monitoring and ongoing support for corporate-scale platforms.
AI & ML
AI is only as good as the context underneath it. Step one is analysis and contextualization: events, batches, states and operator input tied to the process. Step two is AI and machine learning on top — agentic workflows, automated reporting and first-pass analysis your engineers review instead of redo.
Outcomes
- A contextualized data foundation AI can actually reason over
- Reports and summaries drafted automatically
- Engineers start from a first-pass analysis, not a blank page
Analysis & contextualization
The groundwork: process events, batches and operator context modeled so AI and ML have something reliable to work with.
Agentic workflows
AI agents that pull the right data, run the standard checks and hand engineers a result to act on.
Automated reporting
Shift summaries, handover notes and recurring reports drafted from the period’s events, losses and alarms.
First-pass analysis & ML
Anomaly detection, soft sensors and AI-drafted investigations that flag what changed before an engineer digs in.