Tailored AI systems with full control of your data
AI integrations, RAG & private LLMs
Use large language models on your internal data – GDPR-compliant, with answers you can verify instead of invented ones, and integrated seamlessly into your software landscape.
The problem
Why off-the-shelf AI often falls short for businesses
Concerns about data protection and losing trade secrets to public models
No knowledge of internal documents, ERP data and processes
High error rates (“hallucinations”) as long as answers are not tied to structured data
WORKTOPY's answer
Three building blocks for AI on your data
Retrieval-augmented generation (RAG)
Connecting LLMs to your knowledge bases, your wiki (Confluence, for instance), your ERP or a vector database – for precise answers that name their source. Search sees only what the person asking may see.
Private & private-cloud LLM deployment
Running open-source models (Llama or Mistral, for instance) in your own cloud infrastructure or on premises. No data flows to third parties.
Custom AI middleware & API integration
Integrations in Python or Go that bring AI features straight into your internal web applications, your CRM or your shop.
How we work
From use case to production
Use case & data
Which question should be answered, from which sources, for whom? We check data quality and permissions before a model is chosen.
A prototype that is measured
A prototype on real data, assessed against a set of typical questions – so quality is a number rather than an impression.
Integration & operation
Connection to your systems, monitoring of quality and cost, and a way to pick up new documents automatically.
Technology stack
What we work with
Python and Go
LangChain / LlamaIndex
PostgreSQL with pgvector, OpenSearch, Qdrant
Docker
AWS or your own GPU hardware
Ready for an AI that understands your language and your data?
Let us look at which architecture fits your requirements best in a 30-minute first call.
Book a no-obligation architecture call