Putting AI to work in your organisation

We help organisations put artificial intelligence to productive use: as a chat assistant for their own knowledge, as an agent system for recurring workflows and as a coding aid for development teams. We bring proven practices and make sure data protection, quality and accountability are built in from the start.

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Chat assistants that know your organisation

A language model on its own does not know your documents, policies or products. With retrieval-augmented generation (RAG), the assistant first searches your approved sources and bases its answer on them, with traceable references. Together we decide which content belongs in it, how it stays current and who may access what.

Agent systems for recurring workflows

Agents plan work steps and use tools such as ticketing systems, databases or internal interfaces. What matters is the framework around them, the harness: tightly scoped permissions, traceable logs, human approval points and tests that safeguard behaviour. The result is a system your team can trust.

AI as a coding aid for your team

Coding assistants speed up development when they are embedded in a sound workflow. We support selection and setup, rules for context, reviews and tests, and training so your team uses these tools safely and effectively.

Evaluate rather than guess

Whether an AI system helps becomes clear with your own examples. We define test cases and quality criteria early, compare variants and keep an eye on cost, response times and error rates. Data protection, and the question of which data may leave your organisation, are part of the work from the start.

What our work can deliver

Assessment of where AI genuinely adds value to your workflows
Prototype or production RAG system with references
Agent harness with permissions, logs and approval points
Setup and guidelines for AI-assisted software development
Test cases and evaluation for your AI applications
Workshops and training for your team

Three ways to put AI to work.

Chat assistant

01Question
An employee asks about an internal policy.
02Retrieval
The system finds matching passages in approved documents.
03Context
Only those passages go to the model together with the question.
04Model
The language model drafts an answer on that basis.
05Answer with sources
The answer points to the passages it came from.

Agent system

01Task
A recurring process starts, such as an incoming request.
02Planning
The agent breaks the task into individual steps.
03Tools
It uses approved tools such as a ticketing system or database, with limited permissions.
04Approval
Critical steps wait for human confirmation.
05Log
Every step is documented and traceable.

Coding aid

01Task
A ticket describes the desired change.
02Context
The assistant receives the relevant code and the project rules.
03Proposal
It proposes an implementation, including tests.
04Review
Tests and a human review decide whether it is merged.
05Merge
The change enters the project like any other.