AI connected to the knowledge your business uses.
We build systems that retrieve relevant information from approved company sources and use it to help answer questions. Define what the system can access, how users check the sources, and where the information will be processed.
Start with a free 30-minute call to define the sources and hosting.
Retrieval and fine-tuning solve different problems.
Retrieval supplies relevant documents when a question is asked. Fine-tuning changes a model using training examples. We choose an approach based on the task; connecting an AI system to documents does not mean training a new model.
Retrieval (RAG)
The model looks up approved documents and records at the moment it responds, then answers from those passages. This is the approach used in the systems we have built. It suits knowledge that changes and answers that should show their source.
Fine-tuning
Fine-tuning changes a model using training examples. We have not shipped a fine-tuned model as a delivered project. We would only consider it when retrieval cannot do the job, and we do not train foundation models.
It looks it up before it answers.
Each answer is assembled from retrieved passages in your approved sources, so a person can see where the information came from. Retrieval reduces unsupported guesses; it does not make every answer correct.
Ask
A real question, in plain language — from a person, your chatbot, or an app.
Retrieve
A search scans your knowledge base for the most relevant passages.
pgvectorGround
The top passages are handed to the model as context, with instructions to answer from them.
Answer
A response that can show the source it used, so a person can check it.
Same question. Very different answers.
A customer asks: "What's the refund window on my enterprise plan?"
"I don't have access to your company's specific policies, but refund windows are typically 14–30 days. Please check your terms or contact support."
"Enterprise plans can be refunded within 45 days of the invoice date; after that, account credit applies."
A system built around your sources.
Answers grounded in your data
It responds from your documents, history and knowledge — not the open internet — so a person can check the source.
Hosting you choose
Open models, a provider API, or infrastructure you control are all options. Where information is processed depends on the selected hosting, model and connected services.
The approach that fits the task
Connecting documents to a model is retrieval. Fine-tuning is a different technique and is not a default part of the build. We do not train foundation models.
Speaks your domain
Because it retrieves your terminology, products and policies, answers can follow how your documents are written — not a generic script.
Stays current with the sources
As approved documents change, the retrieved material can update — so answers are not frozen to a one-off export.
Built to plug in
The same retrieval setup can sit behind a chatbot, a support assistant, internal search, or an application.
From your knowledge to a system you can check.
Map the knowledge
On the first call we identify what the system needs to know — which sources are suitable, who may access them, and what a useful answer looks like.
Build retrieval
We connect the approved sources, set up retrieval, and agree how answers will be checked against representative questions. That is not the same as training a new model.
Agree hosting
We document the data flow — which model, which provider or infrastructure, and which connected services process information — before implementation.
Connect & maintain
We wire the system into the chatbot, agent or app in scope, and agree how sources will be updated as the business changes.
When generic AI isn't enough.
Teams sitting on years of knowledge
Support histories, documentation, manuals — retrieval can turn that pile into an answer that shows its source.
Businesses that need a defined data flow
If pasting company material into a public chatbot is not acceptable, we agree hosting, model and connected services so you know where information is processed.
Anyone building an AI product
If you need an AI feature that answers from a specific domain, retrieval from approved sources is the usual foundation.
A support assistant that retrieves from years of real conversation history.
Our founder built this in-house at a Lithuanian conference organiser: a support assistant that retrieves from three years of real customer conversations, running on an open model on infrastructure the organisation controls. That is retrieval over existing history — not a fine-tuned model, and not a Zyndix client delivery. See the AI support-agent project for the conversation flow and handoff.
3 yrs of support conversations used as retrieval sources.
Open model on infrastructure they control.
RAG retrieval over that history — not fine-tuning.
We choose the model and setup that fit your accuracy, privacy, and budget — not a one-size default.
Private AI and knowledge systems, answered.
Which documents should the system be able to use?
Bring a real question, the source that should answer it, and any hosting constraints. We’ll discuss a practical first scope.