What we do
Services
We take on problems that have no ready answer yet: it has to be researched, built and taken to production. Below are the areas where our research base produces results rather than a line in a slide deck.
AI implementation · Process automation · Integration
AI implementation for business: we start with the process, not the model
An AI implementation project does not fail on the model; it fails earlier. The chosen process cannot be measured in money, or the data for it does not exist. So we start with the process and a check of the data, and decide on the technology last.
AI agents · Assistants · Task automation
AI agent development: autonomy you can trust with the work
An agent differs from a chatbot in that it acts: it chooses its own steps, calls your systems and carries the task through to a result. So the main question is not “which model” but what authority it has, how its work is checked and what happens when it makes a mistake.
Forecasting · ML · Data analytics
Predictive analytics: solutions that calculate rather than guess
A forecast is useful exactly as far as it can be trusted when a decision is made. We build models whose accuracy is measured, whose limits of applicability are known and whose behaviour when the data fails is understood.
R&D · Research · Development
Research and development (R&D) in software and artificial intelligence
Classic R&D in Russia means instruments, microwave engineering and design documentation. We do R&D where the subject of research is an algorithm, a model or an architecture: when a solution does not exist yet and has to be obtained.
Multi-agent architectures · LLM · Autonomy
Multi-agent systems: when one agent is no longer enough
A multi-agent system is not “a lot of bots”. It is an architecture in which specialised agents split up a task, use tools, remember context and work under control. The difficulty lies not in the prompts but in orchestration, observability and the limits of autonomy.
Product development · SaaS and platforms · Backend, web, mobile
Software product development: from prototype to live operation
A product does not fail on the choice of framework; it fails earlier. More features were built than testing the idea required, or a beautiful interface was put on top of an architecture that will not survive the second client. We start with what the product has to prove to the business, and build exactly the scope that proves it.
DevOps · Kubernetes · CI/CD · Observability
Infrastructure and DevOps: so that production is predictable
Infrastructure reminds you it exists at the worst moment: releases go out by hand and on Fridays, a crashed service is noticed through customer complaints, and a backup is tested for the first time when it is already needed. We make rollouts boring, failures visible before the customer calls, and recovery tested in advance.
What it costs
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