Data, AI & Automation
Connect information, automate repetitive processes and introduce AI where it provides clear operational value.
Build the foundation before adding complexity.
Where we typically look for opportunities
Many useful projects start with a repeated manual task, disconnected systems or information that is difficult to use efficiently.
Manual Data Movement
Employees repeatedly copy information between applications, databases, spreadsheets or reports.
Repetitive Workflows
Predictable administrative processes depend on email, documents and repeated manual actions.
Unclear AI Use Cases
There is interest in AI, but no defined business process or measurable outcome behind it yet.
Focused solutions around real processes
Data Integration & ETL
Data collection, transformation, API integration and automated movement between systems.
Workflow Automation
n8n and API-based workflows, notifications, approvals, scheduled processes and reporting.
Document Automation
Information extraction, document workflows, classification and process hand-offs.
Application Integration
Connect business applications through APIs and lightweight integration layers.
Automated Reporting
Collect and prepare information from multiple systems for repeatable operational reporting.
Practical AI Integration
LLM/API integration, summarization, internal knowledge assistants and AI-assisted workflows.
AI should support a defined business use case.
We avoid starting with a generic “AI transformation” proposition. The useful starting point is an existing process, information problem or repetitive activity. The solution can then introduce automation or AI only where it improves the outcome.
Initial solutions should be small enough to deploy incrementally and clear enough to evaluate against a real business process.