AI Development
Build intelligent systems that learn, adapt, and automate.
Is Your Business Ready for AI?
Answer 3 quick questions and we'll tell you where AI can make the biggest impact for your business.
Many companies want to use AI but lack the expertise to build it into their work. A system people rely on every day needs clean data, a careful integration and checked answers.
We first check whether AI is the right tool for the task. Where it is, we build it into your existing systems, test it on your real data and hand over the code.
What the data says
AI use is growing fast, most companies that hold back say they lack the expertise, and the law now sets rules for how AI meets people.
of EU enterprises with 10 or more employees used AI technologies in 2025, up from 13.5% in 2024
Eurostat, 2025of EU enterprises that had considered AI but did not use it named a lack of relevant expertise, the most common reason, in 2025
Eurostat, 2025the EU AI Act's transparency rules took effect. People using AI systems such as chatbots should be made aware that they are interacting with a machine
European Commission, 2026Independent data. Each figure links to its original source.
Who it is for
Companies with manual, document-heavy work
Document processing, internal search or report drafts added to the workflow your team already runs, with clear rules on where data goes.
Founders adding AI to a product
A working AI feature inside your product, not a stand-alone chatbot, tested on real data before users see it.
Institutions and international organisations
AI for internal operations, documented for review and deployed on the infrastructure you choose.
What's Included
LLM integrations
Language models built into your existing workflows: document processing, contract review, support replies and internal search.
Answers from your own data (RAG)
Retrieval-augmented generation connects a language model to your documents, databases and records. Each answer can point to the source it came from.
AI workflow automation
Multi-step processes such as document routing, data extraction and validation, and report drafts. A person approves the steps where a mistake is costly.
Machine learning models
Classification, anomaly detection, forecasting and recommendation models, trained on your data and deployed to your infrastructure.
AI in internal tools
Search, filters, summaries and suggested inputs inside the dashboards and tools your team already uses.
AI readiness assessment
A structured review of where AI can help your operations, and where it cannot, before anything is built. You receive it as a written report.
Technologies
The tools we use on this work. Every stack is chosen per project, in the written scope.
- OpenAI
- Claude
- Gemini
- Kimi
- LangChain
- pgvector
- Pinecone
- Qdrant
- Redis
- Elasticsearch
- Python
- FastAPI
Our Process
AI Readiness Assessment
We review your data, processes and goals to find where AI helps, and where it does not. Nothing is designed before this step is done.
Architecture Design
We choose the model, design the data pipeline and plan the integration. Security and compliance are reviewed here, and every decision is written down.
Prototype & Validate
A working prototype runs on your real data before full development. You see where it is right and where it fails.
Production Deployment
The system is deployed to your cloud or your own servers, with data schemas, API specs and a maintenance runbook.
Monitoring & Iteration
We track output quality in production and watch for quality drops as your data changes. Ongoing tuning is available under a Support & Maintenance agreement.
Frequently Asked Questions
Ready to get started?
Tell us about your project. We research before we quote, and you receive a written scope and an itemized estimate — no guesswork, no invoice for the thinking.
- Fixed-scope contract
- Full IP ownership
- Reply within one business day