In brief
Complex digital projects rarely fail because of a single weak component. More often, the risk appears between areas of responsibility: the business goal is defined separately, architecture evolves separately, data and integrations arrive too late, and operations are considered only after launch.
At ACG we treat such work as one connected contour. An idea has to move from diagnosis and discovery to architecture, development, deployment, observability and further evolution. That is why engineering, product, data, AI and delivery are kept close to each other in the team.
Why development alone is not enough
When a client asks for “development”, the real task is often broader: clarify constraints, align stakeholders, choose an architecture, define the smallest useful scope, fit into existing processes and avoid building a system that cannot be maintained.
This is where a systems approach matters. Code is only part of the solution. User roles, access rights, data sources, error scenarios, rules, integrations, metrics and a clear production plan are just as important.
How the team keeps the contour together
Dobrynya Avdyushin owns engineering strategy, architecture and development of complex digital systems: backend, APIs, Telegram interfaces, FinTech tools, infrastructure and DevOps practices. His focus is to make sure a solution is not only built, but ready for real operating conditions.
Nikolay Kondratenko strengthens the product and delivery side: discovery, requirements, stakeholder communication, data integrations, metrics, risks and managed production launch. This helps preserve business meaning while the project moves from idea to release.
Gleb Cheprasov covers data and AI: data platforms, AI agents, LLM, SRE, MLOps and architectures for organizational use. His focus becomes especially important when a system has to work not only with actions, but also with context, knowledge and data.
What this looks like in practice
This contour is visible across ACG projects. HACCP systems require mobile data entry, QR flows, reporting and operation without permanent internet access. FinTech tools require stable market data processing and careful communication without promises of returns. AI solutions require human control, data sources and verifiable architecture.
The shared principle is simple: we do not separate development from operations. If a solution is expected to help a business every day, it must be designed around people, processes, data, constraints and future support.
When this approach is useful
ACG is most useful when the task is more complex than “build an interface” or “write an integration”. Typical situations include creating an MVP with a clear architecture, stabilizing an existing system, turning a manual process into a digital contour, preparing a product for growth or carefully embedding AI into real operations.
In these situations, quality comes not from a single tool, but from the connection between engineering, product, data and operational discipline.