AI / ML
Corporate RAG development
I build and deploy a RAG system that lets your AI assistant answer questions based on your documents and knowledge base — accurately and without hallucinations.
What is RAG
RAG (Retrieval-Augmented Generation) is an approach where an AI assistant answers questions based on your own documents and knowledge base rather than the model's "general knowledge." For every question, the system first finds the relevant fragments in your knowledge base and only then builds an answer from them. By grounding responses in real data, it significantly reduces the risk of neural network "hallucinations" — answers that sound convincing but don't match your materials. As a result, your staff and customers get fast access to information even from a very large document base.
Who it's for
What's included
Collecting documents and sources
I gather and prepare your materials from different formats and systems: PDF, DOCX, spreadsheets, databases, and internal systems. I bring them into a form suitable for search and answers.
Vector knowledge base
I store the documents in a vector database and set up automatic ingestion and updates, so the knowledge base keeps absorbing new materials and stays current without manual re-indexing.
Connecting the AI model (LLM)
I connect an AI model that builds answers from the fragments found in your knowledge base rather than from general knowledge, so the answers match your materials.
Prompts and search rules
I craft the prompts and search rules: how to find relevant fragments, how to combine them into an answer, and how to behave when the base has no answer. This is exactly what determines the assistant's accuracy and predictability.
Integrations: Telegram, web chat, API
I connect the interface that suits you: a Telegram bot, a web chat on your site, or an API to embed into your systems. The assistant becomes available right where your staff or customers work.
Deployment, testing, and optimization
I deploy the system on your server, test the answers against real questions, and optimize search and prompts based on the results. I bring the answer quality up to a production-ready level.
How it works
- 01
Goals, documents, and use cases
I define the goals, document types, use cases, and required integrations. I pin down which questions the assistant should answer and for whom.
- 02
Collecting documents and sources
I gather the documents and data sources: PDF, DOCX, spreadsheets, databases, and internal systems. I bring them into a unified form for indexing.
- 03
Vector database and AI model
I store the materials in a vector database and connect an AI model that will build answers from the fragments found in the base.
- 04
Prompts and search rules
I craft the prompts and search rules: how to find relevant fragments and how to build an answer from them so that it relies only on your data.
- 05
Connecting the interface
I connect the interface that fits your task — a Telegram bot, web chat, or API — so the assistant is available to your staff or customers.
- 06
Testing and optimization
I test the system against real questions and optimize the answers: I tune the search, prompts, and output quality based on the results.
RAG system architecture
Document sources (PDF, DOCX, spreadsheets, databases, website) go through ingestion and chunking, are turned into embeddings, and land in the vector knowledge base; on each query, the system retrieves the relevant fragments and passes them to the LLM for an answer. Access to the assistant is through a Telegram bot, web chat, or API, and the whole system is deployed on the client's server.
Document sources
Your materials in different formats and systems: PDF, DOCX, spreadsheets, databases, and website pages. This is the original knowledge base the answers are built on.
Ingestion and chunking
Documents are loaded and split into meaningful fragments (chunks) — this lets the system find and insert exactly the relevant parts into an answer rather than the whole document.
Vector knowledge base
Fragments are turned into embeddings and stored in a vector database. I set up automatic updates so new documents land in the base and search stays current.
Retrieval + LLM
For each question, the system searches for relevant fragments in the vector database and passes them to the LLM, which builds an answer from the data found. Grounding in your fragments reduces "hallucinations."
Interfaces
Access to the assistant through a Telegram bot, a web chat on your site, or an API to embed into your systems. The interface is chosen to fit the use case and audience.
Deployment and control
The system is deployed on your server. Prompts and search rules define the assistant's behavior, while evaluating answers against real questions keeps the quality under control.
Tech stack
Clients
Testimonials
The main value of this service is answers based on your data, not the neural network’s “general knowledge.” The assistant finds the relevant fragments in your documents and answers from them, so the answers match your materials and the risk of “hallucinations” is reduced. Your staff and customers get fast access to information even from a large document base, while the whole system is deployed on your server and stays under your control.
Shall we discuss your task?
FAQ
How many documents can be connected to the system? +
The solution scales: you can connect a small base of a few files or hundreds and thousands of documents. Thanks to vector search, the convenience and speed of answers hold up even on a large base — the system finds the relevant fragments rather than scanning through every document one by one.
Can the knowledge base be updated after launch? +
Yes. You can add new documents and update existing ones after launch. I set up automatic updates so materials land in the vector database automatically and the assistant answers from current data without manual re-indexing.
Will you help choose the optimal RAG architecture? +
Yes. I analyze the task, the volume of data, and the use cases, and tailor the RAG architecture to them: the way documents are split, the model and search rules, the interface, and the deployment setup. The approach adapts to your data and goals, not the other way around.
Will the AI make up answers? +
Answers are built from your documents: for every question the system first finds the relevant fragments in the base, and the model builds an answer only from them. That's why the answers are accurate and match your materials, and the risk of "hallucinations" is noticeably lower than with an ordinary chat with a neural network.
Which document formats are supported? +
Common formats and sources are supported: PDF, DOCX, spreadsheets, databases, web pages, and more. Documents from different systems are brought together into a single knowledge base the assistant answers from.
What about data security? +
The RAG system is deployed on your server, so the documents and the knowledge base never leave your perimeter. You control where the data is stored and who has access to it.
Which LLMs can be used? +
You can use cloud models — OpenAI, Anthropic, Google Gemini, DeepSeek — or deploy a self-hosted (local) model if your data can't leave your perimeter. I'll help pick a suitable model to fit your security requirements, budget, and the answer quality you need; the choice depends precisely on your constraints and goals.