AI agents and chatbots
Design AI agents for real, controllable processes.
Do you have a repeatable task that needs more than a scripted chatbot? We build assistants and agents that search, explain or perform bounded steps with approved sources, explicit permissions and human escalation.
An agent is not simply a chatbot with a new name: it can use tools and change system state, so risk and verification increase.
Conversational answers are separated from actions that create documents, modify data, send messages or start workflows. Every tool has defined permissions, validation and limits.
What we can solve together
Architecture for useful and governable autonomy.
The level of autonomy follows the cost of failure, the quality of evaluation and the organisation’s ability to observe and intervene.
Internal knowledge assistant
Searches approved sources, cites documents and makes missing or insufficient information visible.
Customer guidance chatbot
Answers defined questions, collects context and transfers the conversation to a person when limits are reached.
Operational agent
Uses bounded tools to prepare or execute steps with validation, logs and approval where required.
Commercial assistant
Prepares context, classifies requests and proposes next steps without calling every interaction a qualified lead.
Before you invest
Four questions that keep the decision grounded in reality.
| Autonomy | What can happen without approval? | Reading, drafting, writing and irreversible actions receive different permission and review levels. |
|---|---|---|
| Data | Which sources may each user access? | Existing permissions must carry into retrieval and tool use rather than disappear behind the conversation. |
| Fallback | What happens when the agent does not know? | Refusal, clarification and human transfer are core product behaviours, not ignored exceptions. |
| Evaluation | How is useful behaviour demonstrated? | Representative tasks, critical errors, cost, latency and user feedback are measured separately. |
From the current problem to a system your team can operate.
Define use case and risk
We document user, task, tools, data and the consequence of a wrong action.
Prototype with evaluation
The minimum journey is tested on normal cases, ambiguity and edge conditions.
Pilot with human control
Real users, logs, feedback and cost or action limits enter a controlled environment.
Operate and review
Models, sources, permissions, regressions and behaviour are monitored over time.
A good fit when the task is repeatable, data access can be controlled and results can be verified.
We begin with a bounded pilot and expand autonomy only after evidence of usefulness and safe operation.
We do not automate high-risk decisions without accountable human control.
A probabilistic model can fail; uncertainty, validation and fallback must remain visible in the product.
Guides for the decision
Would you like more context before choosing a direction?
Integrating AI into a company: from use case to production
Successful AI integration starts with a testable task and continues through data, evaluation, limits, cost and operation—not just model selection.
AI agent or chatbot: architecture, risk and operations
A chatbot manages a conversation; an AI agent can use tools and change systems. The meaningful distinction is not the interface but the autonomy, consequences and controls behind it.
RAG for company data: sources, permissions and evaluation
RAG can connect a language model to company knowledge, but its quality depends on source governance, access control, retrieval, citations and evaluation—not merely embeddings.
Next step
Let’s put the solution in the real context of your business.
Tell us the objective, what you have tried and what is blocking progress. We will follow with the questions needed to define the next step.
Discuss the current situation