AI systems implementation
Controlled AI integrations with appropriate data access, permissions, evaluation and privacy safeguards.
Who this service is for
For organisations with a defined information or decision workflow where AI can support a controlled part of the work.
We start with the task, data, cost of failure and required oversight—not a model name. A secure API may be enough; other cases justify retrieval, local models or more rigorous evaluation.
Privacy, permissions, logs, quality testing and human approval are designed into the solution. A prototype has to perform on representative examples, not simply look impressive in a demo.
What you receive
- RAG systems with your documentation
- Self-hosted LLMs (Llama, Mistral, Qwen)
- Fine-tuning on your data
- AI agents for automation
- Vector databases (Pinecone, Qdrant, Weaviate)
- AI security and data privacy
What we work towards
- A validated use case with explicit quality checks
- Controlled access to internal knowledge
- An architecture that accounts for cost, privacy and operation
How the engagement works
- 01
Map the process
We document the workflow, exceptions, data sources and ownership.
- 02
Prove the risky part
A small pilot uses real data and explicit success criteria before wider rollout.
- 03
Integrate safely
The workflow is connected to your systems with permissions, logs and clear failure paths.
- 04
Operate reliably
Monitoring, documentation and handover keep the automation useful after launch.
Frequently asked questions
Must our data be sent to public AI services?
Not necessarily. Depending on risk and infrastructure, we can use enterprise APIs, European providers or locally hosted models.
How do you evaluate quality?
We define representative test cases, expected answers and failure classes, then compare prompt, data and model changes against that set.