All services

Core service

AI that works on your data, under your control

We develop intelligent systems for predictive analytics, natural language processing and intelligent automation, with particular depth in Retrieval-Augmented Generation (RAG): private, grounded AI that answers from your own documents instead of guessing from public training data.

What we deliver

AI-Driven Solutions, end to end

Retrieval-Augmented Generation (RAG)

RAG connects a large language model to your own knowledge base. Instead of relying on generic training data, the system retrieves relevant documents, passages or records and uses them to generate accurate, source-grounded answers with citations.

Document ingestion & chunking

We build pipelines that ingest documents in many formats (PDF, Word, web pages, databases), then clean, split and structure them so the retrieval layer finds the right context every time.

Embeddings & vector search

Using embedding models and vector stores such as Qdrant, we turn your content into searchable semantic vectors so users can ask in natural language and get results based on meaning, not just keywords.

Private data & LLMs

Secure, private LLM deployments using Ollama and Qdrant vector databases, so enterprises can leverage AI while keeping full control of their sensitive data.

Experience with leading AI models

Hands-on expertise with closed models such as Anthropic Claude, ChatGPT, Gemini and Groq, plus open models such as DeepSeek, Llama, Mistral and Qwen, so we integrate state-of-the-art AI into real business solutions.

Evaluation & grounding

We measure retrieval quality, answer faithfulness and hallucination rates, then add guardrails, re-ranking and human feedback loops so answers stay accurate, citable and safe.

Intelligent automation

Natural language processing, document understanding and agentic workflows that remove manual steps from high-volume processes.

Predictive analytics

Forecasting, scoring and anomaly detection models built on your operational data and monitored in production.

How we work

A predictable path to delivery

  • Use-case framing with a clear business metric and data assessment
  • Design the RAG architecture: sources, chunking, embedding model, vector store and LLM
  • Proof of concept on real documents to validate retrieval quality and answer accuracy
  • Production deployment with guardrails, evaluation, observability and feedback loops
  • Private or cloud hosting depending on your data sensitivity requirements
RAG
Grounded, citable answers
Private
On-premise LLM option
GDPR
EU data residency