AI on your data

What the service includes

We build an AI assistant that answers questions about your company's corporate documents, knowledge bases, regulations and correspondence archives. The "AI on your data" service means the full cycle: collecting and cleaning the sources, chunking the texts into fragments, building a vector index, connecting a language model and a web interface or a bot for employees. We write the software part and set up the data-processing pipeline, rather than selling a ready-made box with someone else's content inside. If the loop includes the client's equipment — a server, a GPU station or operator workstations — we prepare the software and configuration for that hardware, but the equipment itself stays yours and physically runs on your side. In the end you get a system that answers with facts from your files rather than general phrasings from the internet.

How RAG really works

At the core lies the RAG scheme (retrieval-augmented generation) — generating an answer with reliance on retrieved documents. First all your texts are turned into numerical vectors via an embeddings model and stored in a vector database, where fragments close in meaning lie next to each other. When an employee asks a question, the system turns it into a vector too, finds the several most relevant chunks of text and passes them to the language model together with the question itself. The model formulates the answer strictly on the basis of the passed fragments rather than on what it once learned on someone else's data. This approach gives links to sources and sharply reduces the fabrication of facts, because the answer is tied to specific paragraphs of your documents.

Where RAG came from

The term and the RAG scheme were introduced in 2020 in the paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" by Patrick Lewis and colleagues from Facebook AI Research, University College London and New York University. The paper was presented at the NeurIPS 2020 conference and showed that combining database search with a generative model yields more accurate and factual answers than the model on its own. The idea relies on the earlier history of vector search: representing words as numerical vectors came into use after the release of word2vec in 2013 by Tomas Mikolov's team at Google. By 2020 the quality of embeddings and language models had grown enough that combining search and generation in a single pipeline became practical. Today it's precisely this scheme that underlies most corporate assistants on proprietary data.

Why precision is critical

A corporate assistant is useless if it confidently outputs wrong figures or invents clauses of a regulation that don't exist. Precision here is determined not by the magic of the model but by engineering setup: how the documents are chunked, which embeddings model is chosen, how many fragments are fed into the context and how the irrelevant is cut off. Privacy is likewise tied to the software part: the "AI on your data" data can be kept within your perimeter without sending it to external services, and access can be segmented by role. Any error in the pipeline doesn't show up immediately, so we run the system on real questions and check the answers before the production launch. The quality of the assistant is the result of debugging the software and the parameters, not of a one-off installation.

Stack and tools

The base stack is RAG, vector databases, embeddings and language models (LLMs), assembled into a single pipeline. We pick the vector database for the volume and hosting requirements: it could be Postgres with the pgvector extension, FAISS for a local index, or specialized stores like Qdrant and Milvus. We choose the embeddings model and the LLM itself by the balance of accuracy, speed and privacy — from cloud APIs to models deployed locally on the client's equipment. We write the software wrapper in Python with libraries for orchestrating requests, processing documents and controlling answer quality. The stack is picked to fit your task, not the other way around: if the data can't leave the perimeter, the whole chain stays within your loop.

When the key tools appeared

The modern stack grew out of several anchor dates. Vector word representations became mainstream after word2vec in 2013 (Mikolov and colleagues, Google). The FAISS library for fast vector search came out from Facebook AI Research in March 2017 and is still used as a local index. In December 2017 the transformer architecture appeared in the paper "Attention Is All You Need" from Google researchers, and in October 2018 the BERT model was released on its basis, with which the practical quality of embeddings and language models began. On these building blocks, the RAG scheme that we use in the service came together in 2020.

Why this is for us

The combined experience of our development team exceeds 45 years in IT, and we approach the assistant as an engineering system rather than a demo for a single showing. We don't manufacture hardware and don't print boxes — we write the software part and configure the client's equipment for the specific task, whether that's a local server or a GPU station within your perimeter. Before the production launch the system goes through checks on your real questions: we look at where each answer comes from and fix the pipeline before employees start using it. The "AI on your data" service is built transparently — you see the sources of the answers and control which data goes where. This means a predictable result and a system that can be developed further, not a one-off installation.