AI and LLM Solutions: RAG Assistants

What you get

Enterprise assistant (RAG)

An in-house assistant that reads your documents, contracts and knowledge base, and gives every answer together with its source.

LLM integration

Claude, GPT or open-source language models connected to your existing software, your CRM or your website.

Document and data analysis

Information in invoices, forms, contracts and emails is extracted automatically and entered into spreadsheets and your systems.

Customer service assistant

An assistant that answers frequently asked questions on your website, on WhatsApp and by email, and hands the conversation over to one of your staff when needed.

Smart automation

Workflows that classify incoming requests, summarize them, route them to the right person and prepare a draft reply.

Security and GDPR compliance

Where the data is processed, who can access what and what is sent to the model are planned from the start; if needed, the model runs on your own server.

From data to prediction, from prediction to discovery.

Data: gathering the traces

In 1846, astronomers measured small deviations in the orbit of Uranus. We gather your documents, records and e-mails the same way and make them readable.

Model: doing the maths

From those deviations, Le Verrier worked out where the unseen planet had to be. A language model likewise draws the answer from the patterns in your data.

Discovery: an answer with its source

When the telescope was turned to the calculated spot, Neptune was there. Your assistant, too, answers within seconds and shows its source.

Why Neptune?

In the 1840s, astronomers noticed that Uranus was straying from its calculated orbit in small but persistent ways. From those deviations, Urbain Le Verrier worked out on paper where an unseen planet had to be. When a telescope was turned to that spot on September 23, 1846, Neptune was there. That is what AI does: it infers what cannot be seen directly from the traces in the data.

What do we build?

Question-answering assistants that work on your company documents
Customer service and WhatsApp assistants
Invoice, contract and form reading (data extraction)
Email and request classification, summarization and routing
AI features added to existing software

How does it work?

First we choose the task where AI will genuinely save time: the place where your team answers the same questions and reads the same documents every day. We set up a quick pilot with a small dataset and measure the accuracy of the answers together with you. If the results are good enough, we take it live with security, access control and system integrations, and keep monitoring answer quality as usage grows.

Technology

For language models, Anthropic Claude, OpenAI GPT, or open-source models such as Llama and Mistral; Supabase (pgvector) or MongoDB vector search for semantic search across documents; Python, Node.js, Go and Rust for integration services and data processing. Which country and which server the data is processed on is decided together at the start of the project.

Frequently asked questions

Is our data used to train the AI model?

With enterprise APIs, the data you send is not used to train the model by default. For stricter requirements, open-source models can run on your own server.

What is RAG, and why is it needed?

RAG (Retrieval-Augmented Generation) means the model searches your documents before it answers. That way the assistant answers not from general knowledge but from your company's up-to-date information, and cites its sources.

What happens if the AI gives a wrong answer?

The assistant is set up to answer only from your sources and to say so when it isn't sure. For critical operations, the final approval always stays with one of your staff.

Which models do you use?

Depending on the job: Anthropic Claude, OpenAI GPT, or open-source models such as Llama and Mistral. The choice weighs cost, speed and data privacy together.

How long does setup take?

A first assistant working on your documents is usually ready to try within 2–4 weeks. Projects involving system integration and automation take 1–3 months.

The planet that was calculated before it was seen.