Artificial Intelligence

AI agents are your main competitive advantage in the market

The era of simple button chat bots has passed. Modern language models (LLMs) are capable of understanding human speech in context, handling objections, analyzing multi-page contracts, and instantly answering customer questions 24/7. AI implementation in business is not a trend, but a way to reduce operational costs for support and sales by up to 70% while simultaneously increasing service speed.

We help companies seamlessly integrate artificial intelligence technologies into existing business processes. We develop smart AI assistants, train models on your internal regulations and databases (RAG methodology), create AI agents for generating expert content, and integrate neural networks directly with your CRM and ERP systems via API.

  • AI Consultants based on GPT-4/Claude-3 for Telegram, WhatsApp, and Websites
  • RAG systems: connecting company knowledge bases to AI without risk of leaks
  • Back-office automation: AI recognition, classification, and analysis of documents
  • Auto-generation of SEO content, product cards, and posts tailored to your Tone of Voice
  • Development of multi-step AI agents (AI Agents) for complex tasks

-70%

First-line customer support cost reduction after AI bot implementation

x5

Accelerating document and contract processing and content creation workflows for employees

99%

Answer accuracy through the use of vector knowledge bases (RAG) and prompt engineering

24/7

Autonomous operation of AI assistants without vacations, sick leaves, or human factors

AI under your control

The main fear of AI implementation is its unpredictability. We solve this problem by establishing strict system frameworks (System Prompts) and validators. The bot will answer clients based strictly on the facts provided by you, politely pivoting away from off-topic subjects.

Our approach

How we build AI solutions for real business

We do not simply resell access to the OpenAI API; we build full-fledged solutions tailored to your infrastructure.

RAG methodology (Retrieval-Augmented)

So that the AI assistant knows your products, delivery terms, and prices, we do not retrain the model. We digitize your regulations, PDF files, price lists, and load them into a vector database (ChromaDB/Pinecone).

Upon user request, the system instantly finds the relevant text snippet and passes it to the model as context for the response. This eliminates AI hallucinations and inventions.

Integration with CRM and ERP

An AI bot should not just "talk". We train it to execute functions (Function Calling): check order status in 1C, book visit times, create deal cards in amoCRM/Bitrix24, or send payment links.

Your bot will become a full-fledged digital sales manager with instant response to any incoming leads.

Token cost optimization

Queries to powerful LLM models cost money. If designed incorrectly, API bills can be huge. We set up hybrid schemes: we use cheap fast models for simple questions and heavy ones for complex tasks.

We cache frequent responses and optimize prompt size, which reduces API costs by 3–5 times compared to standard integrations.

Process

Neural network implementation stages

Sequential path from business process analysis to launching ready-made AI agents on the server.

01

AI Audit & Requirements Gathering

We analyze your business processes. We find routine operations of employees and support services that can be automated with AI. We form a concept.

02

Preparation of the knowledge base

We collect your text regulations, instructions, FAQ. We split texts into semantic chunks (chunking), generate vector embeddings, and upload them to the database.

03

Prompt engineering

We develop system instructions for AI models. We define the role, tone of voice (Tone of Voice), answer formatting rules, and security limitations.

04

Logic and API Development

We build the AI assistant backend using Python / Node.js. We set up connections with communication channels (Telegram, WhatsApp, website chat) and integrate with internal systems (CRM, databases).

05

Testing and Tuning

We conduct automated and manual testing of responses. We evaluate hallucination levels, adjust prompts and knowledge base structure to achieve maximum accuracy.

06

Launch and team training

We deploy the AI solution on the server. We train your staff to work with AI, writing simple guidelines for prompt formulation for daily tasks.

Technologies

Our AI tool stack

We use modern open-source and commercial platforms to develop intelligent systems.

LLM: OpenAI, Anthropic, Llama

Main neural network cores. We select the model for the task: GPT-4o for complex logical reasoning, Claude-3.5-Sonnet for texts, local Llama-3 for complete data confidentiality on your server.

LangChain, LlamaIndex & n8n

Frameworks and platforms for building complex scenarios. They connect neural networks with external APIs, databases, email, and messengers within automated multi-step processes.

Pinecone, ChromaDB & Pgvector

Specialized vector databases. Used for fast semantic search of relevant information from terabytes of corporate documents prior to generating an AI response.

Prices

AI Solution Implementation Pricing

Cost depends on data architecture complexity, number of integrations, and models used.

Solution Capabilities AI Assistant Intelligent Bot for Single Communication Channel 85 000 ₽ Timeline: from 7 days Order Popular AI Integration End-to-end consultant bot with knowledge base and CRM 160 000 ₽ Timeframe: from 14 days Order Custom AI Agent Turnkey AI Infrastructure Development from 280 000 ₽ Timeframe: from 30 days Discuss
Integration channels (Telegram, Website, WhatsApp) 1 messenger or chat on the site up to 3 channels simultaneously All channels + internal ERP/1C
RAG Knowledge base (instructions, price lists) Basic (up to 100 pages) Extended (up to 500 pages) Complex dynamic database (from DB)
Integration with CRM (amoCRM / Bitrix24) Creating deals Complex stage change logic
Types of models used GPT-4o-mini / Claude Haiku GPT-4o / Claude Sonnet Custom Fine-Tuning of models
Document and Invoice Recognition OCR recognition and import
Employee training and support PDF Manuals PDF + 1 educational webinar Full onboarding + 1 month of support
Technical Guarantee of Stability 14 days 30 days 90 days
FAQ

Frequently asked questions about AI implementation

Here are answers to frequently asked questions. Remember: AI does not replace humans; it frees them from routine to solve truly important tasks.

  • What to do if the neural network starts "lying" (hallucinating) to clients?

    To prevent hallucinations, we use RAG (Retrieval-Augmented Generation) technology. We restrict the AI from answering based on "general knowledge". Upon a customer request, the system first searches for an exact answer in your database (uploaded regulations, price lists) and then passes this text snippet to the model with the instruction: "Answer the question based strictly on this text. If the answer is not in the text, politely state that you do not have that information and transfer the dialogue to an operator." This eliminates fabrications by 99%.
  • How much does it cost to use neural networks after integration?

    You pay model providers directly (for example, OpenAI or Anthropic) for the number of sent and received characters (token system). The cost of one full dialogue with a client on fast and optimized models (like GPT-4o-mini) is less than 1–2 rubles. We optimize prompts and cache frequent answers to minimize your API costs. If desired, we can deploy a completely free open-source model on your own server.
  • Can competitors steal data from the bot's knowledge base?

    No. Vector databases and containers with the AI assistant code are deployed in a closed cloud environment (on a dedicated company VPS) protected by a firewall. The user communicates with the model via a proxy server that provides the model with filtered data only. An external user cannot access the original PDF files or system code.
  • What is the difference between an AI bot and a standard button chatbot?

    A standard bot works based on a rigid decision tree: if the user pressed the wrong button or typed a phrase with a typo, the bot breaks. An AI bot understands natural language, slang, synonyms, typos, and is capable of conducting a free conversation. It can extract parameters from free speech (for example, understand the date and time from the phrase "I'd like tomorrow by five in the evening" and save them to the CRM).
AI Integration

Ready to automate your business with AI?

Submit a request and describe the routine tasks of your managers or support service. We will conduct a business process audit, find scenarios for AI implementation, and develop a turnkey intelligent assistant.

Order AI implementation