Agents and copilots
Specialised assistants for support, operations, documents, sales, or clearly bounded internal activities.
AI agents
AI integrated into real work, not a demonstration.
We build agents that use real data, rules, and tools with clear boundaries and human oversight.
We start with a measurable task.
A useful AI agent has a precise purpose: finding information, preparing answers, classifying documents, updating a system, or supporting a decision. We define what it can do, what it cannot do, and where human confirmation is mandatory. Then we connect it to company sources and tools in a workflow that can be observed and evaluated.
Evaluate a use case
Applied capabilities.
We select the required components according to risk, volume, and available data quality.
RAG and knowledge
Search and answers based on approved documents, with visible sources and rules for sensitive information.
Workflow automation
Agents can read, prepare, or update data through APIs, with approvals before important actions.
Evaluation and observability
We test response quality, monitor failures, and retain evidence for controlled improvement.
From idea to a controlled agent.
We do not automate before understanding the impact.
Choose the right use case
We estimate volume, saved time, risk, and whether the required data exists. Sometimes conventional automation is the better choice.
Build an evaluable prototype
We use approved real examples and explicit criteria for relevance, accuracy, and safe behaviour.
Integrate with clear boundaries
We separate read actions from data changes, add permissions, and preserve human confirmation where it matters.
Measure in production
We monitor cost, quality, failures, and source changes so the agent remains useful over time.
Does a repetitive activity consume your team's attention?
Send an example without sensitive information. We will tell you whether the problem needs an AI agent, conventional automation, or a combination.
Discuss the project