Bring AI
into
your product
We help you identify where AI creates practical value, integrate it with your existing technology and put the controls around it to make it usable in production.
Bring us the AI use case. We’ll build the solution around it
You don’t neccesarily need to fit your project into one of our ready-made solutions. We design and build custom AI functionality around your product, data, workflows and existing technology.
AI Features for Existing Products
Add intelligent search, recommendations, content generation, data analysis or other AI-powered functionality to your existing product.
Custom AI Infrastructure
Build the backend, APIs, orchestration and data flows required to run AI functionality reliably in production.
Custom AI Applications
Build a new AI-powered web, mobile or internal application around a specific business use case.
AI Agents & Workflows
Build agents that work across your systems, execute multi-step tasks and involve your team where human decisions are required.
AI Integrations
Connect LLMs and AI services with your existing APIs, databases, SaaS platforms and internal systems.
AI Search & Knowledge Systems
Make internal or customer-facing knowledge searchable and usable through AI.
Start with your AI use case
Choose one of our ready-to-start AI solutions or bring us a different AI challenge. We design and build custom solutions around your product, systems and business requirements.
Connect your product, APIs and data to AI platforms and agents through a custom MCP server.
Build systems that generate, maintain and structure technical knowledge using AI.
Automate repetitive business processes with AI, integrations and human oversight.
We also design and build custom AI solutions beyond these use cases: from AI-powered product features and intelligent search to custom agents, data workflows and integrations.
Your use case → architecture → implementation → production
Have something else in mind?
If there’s a viable way to solve it with AI, our team can assess the use case and design the technical approach.
Fast delivery & focused engineering effort
We use AI for routine, repeatable dev tasks to cut implementation time, while senior engineers stay focused on performance, architecture and business-critical decisions and review everything AI produces, so the software stays reliable and production-ready.
AI-supported execution
- Boilerplate code generation
- UI scaffolding
- Test generation
- Documentation drafts
- Refactoring assistance
Where we stay fully involved
- Product strategy alignment
- Business logic
- Architecture and other key-decisions
- Performance optimization
- UX-critical flows
Case studies with AI
Personalized fitness platform development with AI-assisted engineering
Making advanced product functionality technically achievable
We built a cross-platform fitness app with adaptive training flows, wearable integrations and offline-first synchronization, using AI-assisted workflows to speed up implementation across mobile and backend.
Where AI accelerated delivery:
- Personalized training flows and synchronization-heavy mobile features
- Test generation for synchronization and offline-state edge cases
- Reusable mobile UI components and feature scaffolding
AI handled the repetitive engineering, so our team could focus on complex product logic and platform reliability.
Result:
AI-assisted workflows cut implementation overhead by roughly 35%, delivering advanced functionality within the client’s existing budget and without expanding the team.
AI-assisted modernization of a high-load financial platform
Faster backend modernization without disrupting live financial operations
We rebuilt the backend architecture of a US-based lending platform under live traffic, using AI to accelerate repetitive engineering tasks during large-scale system refactoring.
Where AI supported development:
- Refactoring repetitive backend modules
- Automated test generation during incremental migration
- Documentation and codebase analysis
While AI handled routine work, our senior engineers owned the financial calculations, architecture, performance and production rollout.
Result:
AI-assisted workflows cut the estimated timeline from roughly 10 months to 7, with the platform fully operational throughout. The new backend was validated against 12 months of transaction history and stayed stable under 5,000 concurrent users.
Our principles: safe, controlled, and responsible AI usage
We don’t build AI-generated apps. We build expert-engineered apps – enhanced by AI
While many AI concepts work well in demos, building reliable applications takes careful, controlled implementation and a deep understanding of mobile and backend systems. We apply AI deliberately, only where it can be maintained, scaled and safely operated in real-world products, so your product never becomes an experiment.Controlled Delegation
- AI is used only where safe and predictable - Critical logic is always handled by senior specialists
Risk Awareness
- Risks identification and mitigation (security, data, reliability - We avoid using AI in sensitive areas
Human Oversight
- Every AI-generated output is reviewed - No production code is shipped without validation
Engineering First
- AI supports our engineers, but it never replaces responsibility - We align behavior, performance, and UX across platforms
Tell us what you want
to build with AI
NDA-secured · Talk directly to senior engineers
Have a specific AI use case or just know where you want to improve your product or operations? Tell us what you’re working on, and we’ll discuss the technical solution that makes sense for your situation.