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Our team has been building distributed systems, mobile and web products for more than 10 years. We build systems that are meant to stay in production for years.
Turn Ideas into Systems
Not every project needs AI. Sometimes what you need is a backend that doesn’t crash under load, a cloud setup that doesn’t surprise you at scale, and engineers who think about what happens after launch.
We help companies design, build and evolve systems — whether that means creating something from scratch, integrating proven solutions or strengthening an existing product.
What we do
Turnkey Development
If you need a product built from scratch, we take it end-to-end — discovery, architecture, development and deployment.
We handle business analysis, product structuring, UX/UI design, system architecture, backend and frontend development, mobile applications, infrastructure setup and deployment. You don’t need to assemble separate vendors or coordinate between teams; we build the entire system as one unit.
Dedicated Technical Team
If you need a stable engineering unit focused on a specific part of your product, we form a dedicated team around that scope.
This can mean building and owning a separate module, delivering a complex feature set, or handling integrations that require deeper architectural work. Instead of distributing tasks across overloaded internal teams, we take structured responsibility for a defined part of the system.
The team works as a cohesive unit, not as isolated contributors. Over time, we build context, understand dependencies and stay aligned with your roadmap. That continuity reduces rework and prevents knowledge gaps.
The team works as a cohesive unit, not as isolated contributors. Over time, we build context, understand dependencies and stay aligned with your roadmap. That continuity reduces rework and prevents knowledge gaps.
Team Augmentation
If your internal team is overloaded or missing specific expertise, we provide engineers who integrate directly into your workflow.
Our engineers work inside your existing setup — following your processes, tools and priorities. This model is straightforward: you need additional capacity or specific expertise (for example, .NET, cloud infrastructure or backend architecture), and we provide someone who already knows how to operate at that level.
IT Landscape Evolution
When systems become difficult to maintain or scale, adding features doesn’t help. The foundation needs attention.
We modernise legacy applications, migrate infrastructure to cloud environments, refactor monolithic architectures and redesign data pipelines. We also implement structured data workflows (ETL) using tools like Airflow, Spark, Databricks and Snowflake, replacing manual data handling with reliable pipelines.
Our strength comes from deep technical knowledge and years of building real, production-ready products.
We understand how systems behave under load, how data moves inside an organisation and how to turn that into a reliable system that supports daily operations.
Our expertise
We build full-stack web applications where frontend and backend evolve as one system, not as disconnected layers. Our core full-stack setup is Next.js 14+ (App Router) + Node.js with TypeScript.
Frontend (Next.js / React):
Backend (within full-stack projects):
For standalone frontend projects, we also build with pure React (without Next.js) using Vite, Rspack or Turbopack. Architecture follows Feature-Sliced Design or Atomic Design, where structure matters long-term.
Frontend (Next.js / React):
- Next.js 14+, React 18+, Server & Client Components, TypeScript
- State management: Zustand, Jotai, TanStack Query
- Styling & UI: Tailwind CSS, shadcn/ui, Framer Motion
- Forms: React Hook Form + Zod / Valibot
- Testing: Vitest / Jest, React Testing Library, Playwright / Cypress
Backend (within full-stack projects):
- Next.js API Routes / Route Handlers
- Prisma ORM
- Zod validation
- PostgreSQL, MongoDB
For standalone frontend projects, we also build with pure React (without Next.js) using Vite, Rspack or Turbopack. Architecture follows Feature-Sliced Design or Atomic Design, where structure matters long-term.
For high-load, enterprise-grade systems, we work with:
Platform: .NET 8 (LTS), C# 12, selective experimentation with .NET 9
Backend frameworks:
Data layer:
Architecture & libraries:
Testing:
Platform: .NET 8 (LTS), C# 12, selective experimentation with .NET 9
Backend frameworks:
- ASP.NET Core (MVC, Minimal APIs)
- gRPC
- GraphQL
- SignalR
Data layer:
- Entity Framework Core 8
- Dapper
Architecture & libraries:
- MediatR (CQRS)
- FluentValidation
- AutoMapper
- Refit
- Hangfire / Quartz.NET (background jobs)
Testing:
- xUnit / NUnit
- Moq / NSubstitute
- FluentAssertions
For API-centric systems and flexible architectures, we use:
Frameworks: NestJS, Fastify, Express.js
Data & ORM: Prisma, Drizzle, Kysely
API layers:
Queues & async processing:
Validation & testing:
Observability & infra:
Frameworks: NestJS, Fastify, Express.js
Data & ORM: Prisma, Drizzle, Kysely
API layers:
- tRPC (type-safe APIs)
- GraphQL (Apollo Server)
- OpenAPI / Swagger
Queues & async processing:
- BullMQ (Redis)
- Kafka
Validation & testing:
- Zod, class-validator
- Jest, Vitest, Supertest
Observability & infra:
- Docker
- OpenTelemetry
- Prometheus
- Logging with Pino or Winston
We build mobile applications with long-term architecture in mind.
Cross-platform: Flutter / Dart
Native Android:
Architecture patterns:
Dependency Injection: Dagger / Hilt / Koin
Data & async:
Testing & CI/CD:
Cross-platform: Flutter / Dart
Native Android:
- Kotlin / Java
- Jetpack Compose
Architecture patterns:
- Clean Architecture
- MVI / MVP / MVVM
Dependency Injection: Dagger / Hilt / Koin
Data & async:
- Coroutines / Flow
- Room / Realm
- Firebase
Testing & CI/CD:
- Unit & Integration testing
- GitHub Actions / Jenkins / Circle CI
We design and operate cloud infrastructure on:
Azure:
AWS:
Infrastructure & DevOps:
Azure:
- App Service
- AKS (Kubernetes)
- Azure Functions
- API Management
- Service Bus
- Event Hub
- Cosmos DB
- Azure SQL
- Key Vault
AWS:
- Lambda (.NET / Node)
- API Gateway
- SQS / SNS
- DynamoDB
- Aurora / RDS
- ECS / EKS
Infrastructure & DevOps:
- Docker
- Kubernetes
- Helm
- Terraform
- GitHub Actions
- Azure DevOps Pipelines
Research-Level Engineering
We are a team of experienced engineers building NLP, voice and data systems for years. Our work is shaped by a strong engineering culture and guided by a scientific approach to AI — from choosing the right architecture to careful testing and evaluation. This combination allows us to deliver systems that work reliably under real-world conditions, not just in controlled environments.
- Daniil KrasnoproshinData Scientist & AI/ML EngineerDaniil’s research mindset shapes how we build AI systems — from careful data preparation and clear hypotheses to structured evaluation and a deep understanding of how models behave in real-world conditions.
He has extensive experience building NLP and voice systems, RAG-based assistants and full agentic architectures, with a strong focus on reliability, low latency and cost efficiency in production. - Ilya KislenkaFounder & CTOIlya is the founder of the company with a strong technical background.
For the past 5 years, he has been working as a CTO, focusing on delivery and technology management — from early pre-sale involvement to overseeing engineering teams, technical quality and the company’s internal IT infrastructure. With over a decade of hands-on development and leadership experience, he brings a structured, detail-oriented approach to building reliable, production-ready systems. - Gregory PapraukaSenior DeveloperGregory leads the core engineering work across Smartificial projects. With deep experience in Golang, Java, node.js and AWS, he is responsible for technical execution, system architecture and code quality across client solutions.
All production systems and integrations are built and reviewed under his supervision, ensuring they are reliable, scalable and ready for long-term operation. His focus is on performance, infrastructure efficiency and making sure complex systems remain maintainable as they grow.
Built the System. Ready to Add Intelligence?
We help teams move from solid software to AI-powered systems — without breaking architecture or adding fragile layers.
Our AI practice focuses on production-ready integration: analytics, LLM systems, voice intelligence and infrastructure around them.
Our AI practice focuses on production-ready integration: analytics, LLM systems, voice intelligence and infrastructure around them.
Our projects move forward step by step, with clear decisions and no surprises.
We approach every project as an engineering task: understanding constraints, decomposing the problem and building systems that can be deployed, supported and scaled in production.
How we work
01 —
Initial Call
Initial Call
05 —
Deployment & Support
Deployment & Support
We map the problem, understand the goal and check technical feasibility. If the task is clear, we prepare a proposal within a few days.
Once the system is live, we monitor performance, adjust where needed and support the team. When your product evolve, we help the AI evolve with it.
02 —
Pre-Cycle (for complex projects)
Pre-Cycle (for complex projects)
We run a series of calls with a business analyst. The goal is to fully decompose the product: features, data, workflows, constraints. The analyst then translates everything into a detailed brief for an engineering team.
04 —
Development & Integration
Development & Integration
We build the solution end-to-end. The development team integrates the system into the client’s product with minimal disruption.
03 —
Proposal & Planning
Proposal & Planning
We outline the approach, timeline and team setup. If the client is aligned, we move straight into execution.
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