Building LLM Applications
RAG architectures, agents and MCP, output evaluation, security and on-premise LLM deployment.
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Software Development
Generative AI & LLM Solutions
We build solutions on large language models (LLMs) that speed up access to corporate knowledge, understand documents and make your employees' work easier. We have deep experience with on-premise deployments where data never leaves your premises, as required in regulated industries such as banking.
What we offer
- Enterprise knowledge assistants and employee support assistants
- Document intelligence: contract, report and form analysis, information extraction
- Customer and investor assistants
- AI agents: tool-using solutions that run workflows, integrated with your systems via MCP
- Deployment in the cloud (AWS) or fully on-premise, with guardrails
When you need it
- Employees spend too much time finding corporate information
- You have heavy document reading, summarising and checking workloads
- You want to use GenAI but cannot send data to external services
How we work
Use case
We select the GenAI use case with the highest value.
Prototype
We build a rapid prototype on your real documents.
Evaluate
We measure and improve accuracy, security and cost.
Deploy
We roll out the solution with access controls and monitoring.
What you get
- A GenAI application running in production
- Evaluation set and quality report
- Security and access-control design
- User and administration documentation
Related courses
AI Agents & Process Automation
What are AI agents and what work can they take on? Automating repetitive business processes with no-code agent and automation tools, with security and human oversight.
Learn more →Using Generative AI Safely
What to watch out for when using ChatGPT and similar tools: protecting corporate data, privacy regulations, spotting hallucinations, shadow AI and internal usage policies.
Learn more →Frequently Asked Questions
Can we use GenAI without sending data to external services?
Yes. Solutions can run entirely on-premise using open-weight models.
What if the assistant gives a wrong answer?
This risk is managed through citations, regular measurement against evaluation sets and human review for critical uses. No LLM solution is 100% accurate.
How are access rights protected?
The assistant is designed to answer only from documents the user is authorised to see, integrated with enterprise authentication.
What should the first step be?
Choose the highest-value use case and build a rapid prototype with your real documents.
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Read more →What Is RAG? Building an AI Assistant on Your Company Documents
RAG lets language models answer based on your organisation's own documents. How it works, how it differs from fine-tuning, what to watch when building it and how to measure quality.
Read more →What Are AI Agents? How Companies Are Using Them
AI agents don't just chat; they use tools and run workflows. How agents differ from chat assistants, enterprise use cases, the risks and how to get started.
Read more →Let's talk about your project
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