Artificial intelligence & intelligent systems
We design AI capabilities around concrete business processes: what the system must read, decide, propose and have validated. The goal is never to add a model — it is to remove an identified piece of work.
What we hear most often.
A volume of text, documents or requests beyond what manual handling can absorb
Internal knowledge scattered across tools and hard to retrieve
Repetitive drafting, sorting and summarising work
An appetite for AI without a clear view of the value it would create
What we put in place.
Grouped by domain rather than presented as a flat list, so it stays readable.
AI strategy
Framing where AI creates measurable gain before any code is written.
- AI opportunity assessment
- AI product architecture
- Model selection
- LLM integration
- Prompt and system architecture
- Retrieval systems
- Evaluation and quality measurement
- AI governance
Business assistants
Copilots connected to the real context your teams work in.
- Internal assistants
- Sales assistants
- Support assistants
- Knowledge assistants
- Operational copilots
- Administrative assistants
AI agents
Tool-using agents that execute tasks, with a human in the loop.
- Tool-using agents
- Workflow agents
- Multi-step task execution
- Agent orchestration
- Human approval checkpoints
- Structured output
- Event-driven agents
Generative AI
Generating, rewriting and structuring business content.
- Assisted drafting
- Summarisation
- Classification
- Extraction
- Rewriting
- Customer communication
- Structured content generation
Document intelligence
Turning documents into usable data.
- Document classification
- Metadata extraction
- Invoice and document processing
- Contract information extraction
- Semantic search
- Document summarisation
- Knowledge bases
- RAG architectures
Search & knowledge
Finding the right information, with the right permissions.
- Full-text search
- Semantic search
- Indexed business knowledge
- Contextual search
- Permissions-aware retrieval
What you actually receive.
- A written framing of the use cases kept — and those set aside
- A documented AI architecture: models, data, guardrails
- Features integrated into your tools, not an isolated prototype
- An evaluation protocol to measure quality over time
A project, a process, a product to build?
Describe your context. We come back to you with a technical read and the options available.