Tepat Grupa
Owners ask about their apartment and get answers from project manuals and technical documentation, with the source shown.
AI assistants that answer from your own documents
RAG means the assistant first finds the relevant passage in your material, then writes the answer from it. Staff and customers get an answer with its source attached.
Signs you need one
Searching takes longer than reading.
The answer is somewhere in hundreds of pages of manuals, policies and old emails.
The same questions reach the same experts.
A few people answer for everyone, and their own work waits.
General AI tools guess.
A general chatbot doesn't know your products or your rules, so it fills the gaps with something that sounds plausible.
New staff take months to find their way.
Knowledge lives in documents and in people's heads, and neither is easy to ask.
How we build it
Gather the sources
Which documents count, who owns them and how often they change.
Decide who may see what
Access follows your existing permissions, so an answer only uses documents the person may open.
Prepare the content
Documents are split, cleaned and indexed for vector search, and the index updates when they change.
Build the assistant
Retrieval, answer generation and source links, inside a tool people already use.
Test the answers
We check it against real questions and fix gaps in the sources as well as in the system.
Where RAG is used
Company knowledge base
Policies, procedures and how-tos you can ask about in your own words.
Technical documentation assistant
Specifications and manuals for engineers, installers or service teams.
Customer support knowledge
Support staff, or customers directly, get answers from product and service material.
AI agentsContract and document search
Find the clause, the deadline or the obligation across many agreements.
Assistant for employees
One place to ask about HR, IT and internal processes.
Where we have built it
What we build with
RAG is an architecture, not a product. The parts are chosen to suit your documents, languages and privacy requirements.
- OpenAI
- LLM
- Embeddings
- Vector search
- LangChain
- Document parsing
Common questions
When the answers depend on your own documents, there are too many of them to search by hand, and a wrong answer has a cost.
RAG doesn't train the model on your documents; it looks them up when a question is asked. Which provider processes the data, and where, is agreed before we build.
The assistant should say so rather than guess, and it can pass the question to a person. That behaviour is set and tested before launch.
Yes. Current models work well in Latvian, Swedish and other European languages, and the assistant can answer in a different language from the source document.
Related services
All servicesAI agents
Agents that carry out a defined task inside your systems and hand over to a person when needed.
AI automation
Automating the work between your systems: enquiries, documents, data and notifications.
AI solutions for real estate
Platforms, assistants and automation for property developers and managers.
Where does your team lose time looking for answers?
Tell us which documents they search. We'll outline what an assistant built on them could answer.