By task
By technology
By industry
Most solution pages are written in Russian; the list above links to them directly.
Digital health
Aviation manufacturing
Transport infrastructure
Wholesale distribution
Travel and booking
Marketing and web services
Car rental & sales
Urban infrastructure
Private healthcare
Digital health
Studio in-house product. Oreol is an assistant that helps patients interpret symptoms and decide whether a doctor visit is needed.
Symptom information online is fragmented — some sources alarm, others understate risk. Patients struggle to assess whether their condition needs medical attention.
Build a product that evaluates symptoms and routes the patient — strictly on clinical protocols.
The service does not replace a doctor and does not diagnose. The final medical decision remains with the specialist.
Project in active development since 2025.
Aviation manufacturing
A major Russian aviation manufacturer.
Structural element condition monitoring was based on weight measurement data. As production volume grew, manual oscillogram analysis was no longer scalable.
Develop a system for detecting anomalous patterns in time series, enabling early-stage defect detection.
The system is embedded in the manufacturing process and used as a regular diagnostics tool.
Processing handled thousands of measurements daily.
Transport infrastructure
A major transport infrastructure facility with international operations.
Regular reports were compiled manually from internal data. Preparation consumed significant engineering team time.
Automate report generation and integrate it into existing software.
Reporting became part of the system infrastructure, not a separate manual task.
Wholesale distribution
A wholesale distributor of engineering systems with a nationwide branch network.
Orders arrive in any shape: a free-form list, a photo of a sheet from site, a message forwarded from chat, or a question about what would fit. They rarely carry an exact article number. Every line is looked up by hand in a catalogue of hundreds of thousands of items, and that is the daily work of over three hundred people.
Make the matching automatic so the manager is left checking the result rather than searching for it.
One catalogue and one set of matching rules across every branch.
Travel and booking
An international travel marketplace for booking services.
Users were spending time on complex filters instead of simply describing the trip they wanted. On top of that, offers from over 100 suppliers had to be brought together in one place.
Build an AI search that parses a request written in natural language and produces personal recommendations without complex filters.
The platform owner sees bookings, revenue and registrations in one dashboard.
Delivered in 3 months.
Marketing and web services
A marketing web service with a client area.
A marketing strategy requires going through the market, competitors and audience by hand, and that takes weeks. The business also needs a working tool with versions, change history and export rather than just a document. And the result has to be ready to launch as it is, complete with a media plan, budgets and promotion channels.
Build a platform that helps a business automatically assemble tailored marketing strategies from an analysis of the market, competitors and target audience.
In production since 2026.
Car rental & sales
A small company in car rental and sales with a distributed structure.
Sales, rental and service were recorded in three separate systems. Management decisions relied on fragmented reporting, with no unified analytical model of customer activity.
Build systematic analytics and identify process optimization points.
The project became the foundation for regular data-driven management.
Urban infrastructure
A team working on urban and infrastructure analytics tasks.
Need to assess territory lighting levels at scale without field measurements.
Develop a reproducible satellite image analysis pipeline using ML.
Private healthcare
A private practitioner. The digital assistant was launched to scale the doctor's personal expertise across a higher patient flow.
A detailed first consultation runs tens of minutes per patient: history-taking, explaining the cause of the condition, recommending tests. On any inbound flow, the doctor's expertise hits a hard time limit.
Build the doctor's digital twin — a Telegram assistant that autonomously runs the dialogue with a patient, collects symptomatology, and produces a personalized review in the style and logic of the specific specialist.
The service does not replace a doctor and does not diagnose. The final medical decision remains with the specialist.
The style and approach reproduce the logic of a specific specialist rather than an averaged model.
¹ Results from selected projects. The effect depends on the process and data.
We choose a method for the task, build around evidence, and measure what the system actually does.
Audit & plan
01We break the task down: what the system should do, on what data, how to measure the result. The success metric is set before any code is written.
Pilot
02We connect to your sources, clean and label the data. Without this step the solution will not work accurately: we do not skip it for the sake of speed.
We build and test 1–2 scenarios on your data. You see a live system with real metrics before a full rollout.
Production deployment
04We embed the system into your stack: CRM, 1C, telephony, messengers. The data stays in your infrastructure.
After launch — model quality monitoring. On accuracy drops or data drift, we retrain the model and ship a new release.
We build the system from clear parts: search, classification, routing, answer templates. A language model goes only where nothing else will do, so the system behaves predictably and is easy to check.
Answers are grounded in your documents, not in what the model imagines. Every response can be verified against its source.
Before launch — a test set from your real examples. On every change — regression testing. You see numbers, not promises.
Role-based access control, audit logging, and flexible deployment — on your infrastructure or cloud, per your requirements.
Our engineers worked at leading Russian technology companies before joining Dzeta. We know how industrial systems are built from the inside and build to the same standards. We write the code and build the systems ourselves. There are no subcontractors between you and the engineers.
ML engineers, recommender systems, natural language
Industrial machine learning, sensor analytics, anomaly detection
Computer vision, natural language, AI product development
MSU
MIPT
HSE
Bauman MSTU
MARCHI
UrGAHUWe use proven models, data systems, and development tools. The architecture follows your requirements, not a preferred vendor.
How the full cost is calculated: company details
3–5 business days
We analyze your task and prepare a concrete plan for your process.
2–6 weeks
1–2 scenarios on your data. Proven results before you commit to scaling.
4–12 weeks
All scenarios, integrations, monitoring, and support.
Not unless you want it to. Data stays in your infrastructure or private cloud, without transfer to third parties. We discuss deployment architecture during diagnostics and fix it in the contract.
No. We handle everything — from design to launch. On your side, you need a person who understands the process: what to tell customers, which documents matter, how the work is structured.
That's exactly what the pilot is for. Before starting, we jointly define success criteria and metrics. If results aren't achieved — we say so directly and propose adjustments. We don't scale what doesn't work.
It depends on scenario complexity, number of integrations, and data volume. The audit price depends on the number of processes — we name it after a short call. Pilot — from 290,000 ₽. Production — custom, after pilot results.
Diagnostics — 3–5 business days. Pilot — 2–6 weeks. Full deployment — 4–12 weeks. You get the first measurable result during the pilot, before scaling.
No, our primary audience is mid-size business. We apply engineering standards but design for realistic budgets. If there's a repeatable process and a clear outcome — we can most likely help.
A short description is enough to start. We will clarify the data, workflow, and success criteria together.
Your request goes directly to our team.