Applied intelligence, engineered for use.
Five connected capabilities that take an organisation from opportunity discovery through reliable implementation. Each one can start on its own; together they form one delivery path.
Industrial AI and machine learning
Prediction, anomaly detection and optimisation built around the constraints of a real operation, not a laboratory dataset.
Industrial data is noisy, gapped and shaped by physical limits. We build models that respect those limits: forecasting that handles maintenance windows and seasonality, anomaly detection that separates a real drift from a sensor fault, and optimisation that stays inside the operating envelope your engineers already trust.
- Demand and load forecasting
- Equipment and process anomaly detection
- Energy and consumption modelling
- Setpoint and schedule optimisation support
- A validated model with a measured baseline
- Confidence limits and failure modes written down
- A monitoring plan for drift and retraining
Data engineering and analytics
Dependable data foundations that connect fragmented operational sources into governed, usable information.
Most industrial AI problems are data problems first. We connect historians, SCADA exports, ERP and maintenance systems and spreadsheets into pipelines with quality checks, lineage and access control, then put analytics on top that operations and finance can both read.
- Source assessment and data fitness review
- Pipelines and warehousing for operational data
- Data quality rules and monitoring
- Operational and management dashboards
- One governed source for the decisions that matter
- Quality measured, not assumed
- Analytics that survive a shift change
Intelligent process automation
Practical automation for document-heavy and knowledge-intensive workflows, with a person accountable at every point that matters.
Permits, inspection reports, procurement documents, compliance returns and shift handovers carry information that rarely reaches a system. We use document intelligence and language models to extract, classify and route that information, with review steps wherever the consequence of an error is real.
- Document extraction and classification
- Workflow routing and approvals
- Knowledge assistants over controlled internal sources
- Reporting and return preparation
- Less manual re-keying, with an audit trail
- Review points placed where the risk is highest
- Assistants that cite the source they used
Applied AI solution engineering
From a tightly scoped prototype to a production integration, with security, observability and adoption designed in from the start.
A model in a notebook is not a solution. We engineer the integration, the interfaces, the access control and the monitoring, and we plan for the people who will use it. Where a solution should stay small, we say so.
- Rapid validation sprints
- Production integration with existing systems
- Secure deployment on cloud or on-premises infrastructure
- Adoption support and handover
- A system your own team can run
- Observability from the first release
- Documentation and handover that hold up
AI readiness and governance
A clear path from a business problem to a responsible implementation: use cases, data, architecture, risk and operating model.
Before a build, the questions are whether AI is the right tool, what evidence exists, who is accountable for the outcome and what could go wrong. We run that assessment with operations, IT and leadership together and leave a plan that can be acted on, or a clear reason not to proceed.
- Opportunity and use-case assessment
- Data and infrastructure readiness
- Target architecture and build-or-buy
- Governance, risk and human-oversight design
- A prioritised, evidenced list of opportunities
- A governance model people can follow
- A first step sized to start quickly
Start with one operational decision.
An opportunity assessment focused on a single decision is the fastest way to find out whether AI is the right tool, what data it needs and what the first step should be.