AI system development and implementation

Business automation
with AI

We examine your processes, estimate the impact and deploy AI where it pays off: document processing, customer enquiries, phone calls and engineering selection. Integrated with your systems, with control over your data.

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Case studies

Already working for our clients

Products in production, with industry-specific logic, integrations and a specialist responsible for reviewing the output.

Selection desk lower rows
Fan and damper manufacturer · in production use

Vent Selector

Equipment selection from HVAC schedules: fans, fire/smoke dampers, and accessories in the manufacturer’s own catalog

10× engineering department throughput published YYTECH case result; method and sample not disclosed

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Documents screen with the result of project M-601 (ventilation module).
Equipment manufacturers · HVAC and electrical

Spec Selector

Equipment selection platform for manufacturers: from project schedules to priced line items

10–20 minutes selection per document with the system (instead of 1–2 hours by hand) partner’s preliminary estimate; not a measurement; not a US benchmark

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Working screen: assistant chat, estimate, and row inspector
Substation and switchgear manufacturing

Transformatorz

Compact substation cost estimating from tender documents and the manufacturer’s templates, with an AI assistant in the project chat

60 min manual project estimate in Excel assessed when the system was scoped, not a measurement

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RFQ details screen
Industrial components distribution · Europe

RFQ Intake

AI intake for a distributor’s sales inbox: emails, attachments, and nameplate photos become structured requests for quotation

99% spam detection accuracy

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Overhead view of two conveyors with ready-meal trays; red boxes with track IDs, green line boundaries
Food processing plants · video analytics

Computer Vision

Video analytics for food processing plants: overhead cameras count product, check the state of workstations, containers, and machines, and measure carcasses

5 computer vision systems in operation YYTECH, October 22, 2026; number of systems, not a performance metric

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Admin panel inbox with a customer conversation and the contact card
Lubricants distribution · Instagram DMs

AI Consultant

An AI consultant in a lubricants distributor’s Instagram DMs: it matches engine oil and other fluids to the customer’s exact car and OEM approval

99% of requests the consultant handled on its own 572 of 579, Sep 28–Oct 11, 2026

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Dark screen with two audio tracks and turn captions
Voice AI · phone calls · five projects

Voice Agents

AI agents that handle phone calls like a live operator, follow your business rules, and log each outcome in your CRM or ERP

5 voice AI projects in operation per YYTECH; number of projects, not an efficiency metric

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More projects

  • AI consultant inside HRM & PayrollEnterprise HR · Europe

    An assistant built into the enterprise system interface explains features and retrieves the data and statistics users request. Onboarding for new users takes 2 weeks instead of two months.

  • Instagram chatbot for a furniture manufacturerFurniture · Instagram

    Answers common questions in Direct about lead times, materials and how the company works, clarifies the request and moves the conversation towards a call with a designer. Managers receive a qualified enquiry.

How we launch your project

From a business need to a working AI system

These are successive stages. First we estimate the impact, then test it on your data, and only then scale the solution.

  1. 01 1–2 weeks

    AI audit

    We examine your process, data and constraints to identify where AI can deliver practical value and where a simpler solution costs less.

    DeliverableOpportunity map, impact assessment and roadmap

  2. 02 4–6 weeks

    Pilot

    We build a working prototype using your data and connect a key system to test the idea before a major investment.

    DeliverableWorking prototype and validated metrics

  3. 03 Project-specific

    Implementation

    We integrate the solution into your operations, launch it in production, monitor response quality and add new scenarios.

    DeliverableProduction system, integrations and SLA

Impact assessment before development Metrics before and after implementation Data control at every stage
Discuss your project
About us

An engineering team from Belarus

YYTECH was founded by IT engineers with experience launching production systems. We design AI solutions as software products: architecture, integrations, testing and data control, so the result works within your process and can be verified.

We are a resident of the Hi-Tech Park. Our clients include companies from Belarus, other CIS countries, Europe and the USA. Our projects have no geographical boundaries.

Eugene Yukhnevski
Eugene Yukhnevski CEO 12+ years in IT
Valentin Marchuk
Valentin Marchuk CTO 7+ years in IT
FAQ

Frequently asked questions

Have another question? Email us at info@yytech.by or message @yytech_by_bot — we will help with your specific needs.

How long does implementation take?
An AI audit takes 1–2 weeks. We usually deliver a working pilot prototype within 4–6 weeks. Full implementation depends on the number of integrations and the complexity of your processes.
How do you handle data security?
We work in accordance with the Belarusian Law on Personal Data Protection and the requirements of the National Personal Data Protection Centre. Data is usually anonymised before processing. For particularly sensitive scenarios, we deploy models, speech recognition and speech synthesis within your infrastructure. Access is controlled and actions are logged.
Which systems do you integrate with?
We integrate with almost any system: Bitrix24, amoCRM and other CRMs, YClients, 1C, IP telephony, calendars and messaging apps including Telegram, WhatsApp and Instagram. If there is no ready-made integration, we build one for your infrastructure.
How does AI differ from conventional automation?
Conventional workflows follow fixed rules. Language models understand natural speech, unstructured documents and unusual requests, handling tasks that previously required an employee.
What happens if the model gives a wrong answer?
We design the process with clear limits: when data is insufficient or confidence is low, the system stops and hands the question to a specialist. Responses are logged, and ambiguous cases are reviewed and incorporated into the rules.

Tell us about your process — we will assess the impact

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