An AI assistant that knows everything in your company documents
Ask in plain language and get an accurate answer with its source: document, page, article. Like ChatGPT, but for your knowledge.
Answers in seconds
Instead of 30 minutes searching folders and waiting for a colleague.
Always with a source
Every answer cites the document, page or legal article behind it.
Your data stays with you
Private deployment, access control and the option of local models.
Faster onboarding
New hires learn with an assistant that answers questions 24/7.
Your company knowledge is scattered across thousands of PDFs, Word files, emails, policies and folders. People spend hours searching or ask a colleague who does not know either. We build a private AI assistant that has read all your documents, understands questions asked in everyday words and answers only from your documentation, with a link to the exact source. Technically this is called RAG (Retrieval Augmented Generation): the system first finds the relevant parts of your documents, and only then does the AI write the answer. That is why it does not make things up, and you can always check where the information comes from.
What the assistant can do
Search by meaning, not keywords
Finds the answer even when the question and the document use different words.
Source citations
Shows the exact passages, documents and articles, with a link to verify.
Thousands of documents
PDF, Word, Excel, scanned documents (OCR), websites, Confluence, SharePoint and databases.
Automatic updates
New and changed documents are processed automatically and available immediately.
Per-user permissions
Everyone sees only the documents they have access to, just like in your systems.
Analysis and comparison
Comparing contract versions, summaries, compliance checks and extracting key clauses.
Multiple languages
Question in English, document in German or Serbian, answer in the user's language.
Fits your tools
Web app, Teams, Slack, Intrascope, your intranet or an API for your systems.
Local models
For the most sensitive data the solution runs on your server with open source models.
Technologies
How your AI assistant is built
- 1
Knowledge base analysis
We review document structure, formats, quality and typical user questions.
- 2
Preparation and indexing
Cleaning, meaningful chunking, metadata and a vector database for semantic search.
- 3
AI layer and testing
We pick the model, tune retrieval and measure accuracy on a set of real questions.
- 4
Interface and integrations
Chat interface, permissions, history and connections to your systems.
- 5
Production and improvement
We go live, track unanswered questions and keep raising quality.
In practice: AI for tax and legal regulations
For a large consultancy (the client is confidential) we built an AI assistant over a huge base of tax and legal regulations, official opinions and internal documents. Consultants ask in natural language and get answers referencing the exact laws, articles and documents. The MVP for the internal team was delivered in 2 months, and the complete system for commercial use in 5 months.
Read more
Frequently asked questions
What is RAG in simple terms?
RAG is a technique where AI searches your documents before answering, takes the most relevant parts and writes the answer based on them. It is like asking a colleague who has read all the documentation and always tells you where they read it.
How is it different from ChatGPT?
ChatGPT answers from general knowledge and can make things up. An AI assistant over your documents answers only from your documents, cites the source, respects permissions and keeps data where it belongs.
What does data vectorization mean?
Turning text into numbers that describe its meaning. This lets the system find document passages that mean the same as the question, even when they use different words.
How many documents can it handle?
From a few hundred to hundreds of thousands. For large bases we use hybrid search and re-ranking to keep answers precise.
Does the AI make up answers?
We design the system to answer only when it finds a basis in the documents, and otherwise to say it does not know. Every answer has a source, so users can always verify.
Where is our data stored?
Your choice: an EU cloud, your own server or fully local with open source models. The commercial AI APIs we use do not train models on your data.
How much does an AI document assistant cost?
It depends on the number and type of documents, integrations and infrastructure requirements. Smaller assistants start at around €5,000, and large systems with thousands of regulations and several roles are delivered in phases. Monthly infrastructure and model costs are usually €100 to €500.
How long does it take?
A first prototype on your documents is usually ready in 2 to 4 weeks, an MVP for the internal team in about 2 months, and a complete system in 3 to 5 months.
Which industries benefit most?
Law and tax, consulting, insurance, banking, healthcare, manufacturing (technical documentation), IT support, public administration and any company with a large internal knowledge base.
Free fixes from our knowledge base
Step-by-step guides for the problems we solve most often as part of this service.
How much time does your team spend looking for information?
Send us a description of your documentation. We will build a prototype on a sample of your documents so you see the result before deciding.