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RAG development services and semantic search

Semantic search across your documents, catalogue and knowledge base, and chat with your data: a RAG system finds the right passages and answers with a link to the source instead of making things up.

from ₴50,000

Discuss your project
A RAG system finds the passages in company documents that match a question and gives an answer linked to its source
Answers linked to the source
Documents, catalogue, knowledge base
Data stays in your infrastructure
A pilot in 2–4 weeks

What RAG is, in plain words

RAG (retrieval-augmented generation) is the way an AI assistant answers from your own materials without retraining the model. For every question the system first searches your documents, catalogue or knowledge base for the passages that matter, and the model builds the answer from those passages only, showing where each statement comes from.

The search goes by meaning, not by matching words: ask “how do I return an item” and the system finds the “Exchanges and returns” section even if those words are not in the question. When your data holds no answer, a RAG system says so instead of making one up.

You can see how it works on your documents in 2–4 weeks with a fixed-price pilot.

Who it is for

Who needs a RAG system

  • Support with a large knowledge base

    Agents and the bot find the answer in articles and policies in seconds instead of scrolling through dozens of pages.

  • Companies with document archives

    Contracts, manuals and policies: an employee asks in their own words and gets an answer that links to the document.

  • Shops with a large catalogue

    A customer describes what they need, and meaning-based search finds the product even when it is called differently.

What’s included

What RAG development includes

  • Data audit

    We map where the knowledge lives: which documents and systems to connect, what is outdated and what people ask most.

  • Connected sources

    PDF, Word, Excel and Google Sheets; your website and catalogue; Google Drive, Notion, Confluence; support knowledge base and CRM.

  • Index and vector database

    We split texts into passages and store them in pgvector or Qdrant — on your server or in the cloud of your choice.

  • Semantic search

    Meaning-based search with filters by section and date — as a search box on a website or portal, or as the core of an assistant.

  • Answers with sources

    The model answers only from the passages it found and links to the document and section the answer comes from.

  • Access rights

    Every user sees only the documents they have access to, and the index stays in your infrastructure.

  • Quality checks

    We test the answers on a set of your real questions before and after launch and fix the weak spots.

  • Launch and support

    We embed it in your website, portal, Telegram or CRM and train your team; a month of support after launch is included.

Process

From audit to launch in four steps

  1. 01

    Audit and estimate

    We review your sources and typical questions, choose the model and the storage, and agree on a quality metric, budget and timeline.

  2. 02

    Pilot

    In 2–4 weeks we build a prototype on part of your documents and test the answers on real questions — at a fixed price.

  3. 03

    Development and integrations

    We connect every source, access right and channel: website, portal, Telegram or CRM. A system with integrations takes 1–2 months.

  4. 04

    Launch and support

    We train your team and set up index updates. A month of support after launch is included in the price.

Stack

RAG technology stack

We pick the model for the task and budget, and the vector database for the volume of data. When data must not leave your perimeter, everything runs on your server.

  • OpenAI API
  • Anthropic API
  • Google Gemini
  • Open-source models
  • LangChain / LlamaIndex
  • pgvector
  • Qdrant
  • Python
Case studies

How it went on real projects

The task, the solution and the result: what exactly we did and what came of it.

All cases
  • Workshop inventory app on a laptop screen: the parts table with search by meaning, next to a photo of a board
    Electronics and prototyping workshop

    Workshop: an inventory app with AI agents, search by meaning and an MCP server

    Task Keep everything the workshop holds in one place: parts and stock, documents, equipment, projects. And fill it in by voice and photo rather than with forms.

    What we did A Laravel and React app where every record has an internal code and search works by meaning. The in-app chat and external agents use the same tools, and permissions are checked on every call.

    Result The app already holds 141 parts, 235 photos described by AI and 538 vectors, and agents made 86 changes over the past week. We use it ourselves, and it is genuinely convenient.

    Web apps / SaaS · RAG and semantic search · Automation and AI agents · Backend and API · Laravel website · AI solutions Read the case

What our clients say about us

We’re confident that after working with us you’ll leave a positive review here too.

This is where it all started. Since then we've ordered two more websites here and never even looked for another developer. Every time the work is done well and on time. I recommend them to colleagues.

Kateryna Chorna Kateryna Chorna Project Manager, World Future Awards

The site we built

WORLD FUTURE AWARDS worldfutureawards.com

We came back a second time, now with the chamber's own website. I didn't have to explain anything from scratch: they remembered how we work and suggested better options themselves. And regular clients get a discount too.

Olha Kipiani Olha Kipiani Group Project Director, World Luxury Chamber of Commerce

The site we built

World Luxury Chamber of Commerce worldluxurychamber.com

I expected it to be like other companies, where you wait days for a reply. Instead, they're always available and always get back to me. Even in the evenings and on weekends, which I really appreciated.

Daria Zyu Daria Zyu Online store of Ukrainian-made goods, Nikopol

The site we built

ATIS atis-shop.com.ua

I paid exactly what they quoted at the start. When I asked them mid-project to add a page with my students' results, they named the price first and only then did it. No "that wasn't included" at the end. Others never worked that way.

Anna Holovach Anna Holovach English teacher, CAE preparation

The site we built

Anna ELT anna-elt-w.com.ua

Two days after launch, inquiries from the contact form stopped coming in. I wrote to them on a Saturday, and it was fixed by lunch. The problem was with the hosting, but no one said "that's not on us." They just sorted it out.

Oleksii Diadechko Oleksii Diadechko Attorney, Kyiv

The site we built

Attorney Diadechko’s Office diadechko.com.ua

I know nothing about websites. They explained everything in plain language, no jargon. Before handing the site over, they showed me how to change the text and photos myself and recorded a video. Now I update everything on my own. Thank you for your patience.

Mariana Diadechko Mariana Diadechko Attorney, Kyiv

The site we built

Attorney Diadechko’s Office diadechko.com.ua

The site went live a year ago, and I still message them about small things: adding partners, posting photos from the celebration. They answer as if they'd delivered it yesterday. Not once have I heard that the contract has ended.

Maria Ruban Maria Ruban PR & Partnerships Manager, World Luxury Chamber of Commerce

The site we built

World Luxury Day worldluxuryday.com

We needed the site before the season started, and the deadline was tight. They promised four weeks and delivered in three and a half. I never had to chase them. Every week they told me what was done. We started the season with the site already live.

Stepan Sokolov Stepan Sokolov Crayfish delivery in Kharkiv

The site we built

Rakovar rakovar.com.ua

Our old website brought in one or two inquiries a month. Three months after the new one launched, we get eight to ten, with no ads. We're now on Google's first page for the searches that matter. The site finally pays for itself.

Mariana Mariana Passenger transport, Odesa to Bulgaria

The site we built

Passenger transportation odessa-bulgaria-bus.com

RAG systems: frequently asked questions

Answers on how RAG differs from an ordinary chatbot, which data can be connected, where it is stored, and how much the work costs and takes. Didn’t find yours? Write to us.

How is RAG different from plain ChatGPT?

ChatGPT answers from what the model memorised in training and knows nothing about your documents. A RAG system first finds the relevant passages in your data for every question, and the model answers only from them and shows the source. If your documents hold no answer, the system says so instead of making one up.

Which data can be connected?

Documents — PDF, Word, Excel and Google Sheets; website pages and the product catalogue; Google Drive, Notion and Confluence; the support knowledge base and CRM data. Sources can be added step by step, and the index updates when documents change.

Where is our data stored, and is the model trained on it?

Documents and the index are stored on your server or in the cloud of your choice. Data sent through the OpenAI or Anthropic API is not used to train their models by default. If data must not leave your company, we deploy an open-source model on your server. Every user sees only the documents they have access to.

How long does it take?

A pilot on part of your documents takes 2–4 weeks; a system with every source and integration, 1–2 months. The timeline and the price are fixed in the contract. You can start with a fixed-price pilot: it shows the quality of answers on your data before a larger project.

How much does a RAG system cost?

From ₴50,000. The exact price depends on the volume and type of data, the number of sources and integrations, and the quality the answers must reach. You can test the idea with a pilot from ₴30,000, and a month of support after launch is included.

Can RAG work inside a chatbot, a website or a CRM?

Yes. We embed search and answers in your website, internal portal, Telegram or CRM. If you need a bot that answers customers from your knowledge base, that is chatbot development: there RAG works inside the AI bot.

Shall we discuss which knowledge to connect first?

Tell us where your documents live and what people ask most often — we’ll suggest a pilot and name the price and timeline. No obligation.

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