Services overview
Custom AI software development for businesses.
We combine established software engineering with modern AI capabilities – from a single intelligent feature to a complete business or web application.
Services
What kind of AI software we build.
Our service areas reflect concrete technical problems and project types – from RAG and LLM integration to agents, web applications and process automation.
AI Agents
Agentic workflows and intelligent assistants for clearly defined tasks and business processes.
Learn moreRAG Systems
Make company knowledge in documents and data sources usable with language models in a controlled, traceable way.
Learn moreLLM Integration
Integrate GPT, Claude, Gemini or open-source models into software and business processes with the right architecture.
Learn moreAI Process Automation
Automate recurring work intelligently without losing control of the wider business process.
Learn moreAI Web Applications
Custom portals, SaaS products and business applications with AI capabilities that support a real product workflow.
Learn moreAI MVP Development
Validate new ideas quickly and turn the result into a sound technical foundation for the next stage.
Learn moreArchitecture
A strong AI solution does not start with a model.
The right technical approach depends on the task, data, reliability requirements, integrations, privacy and operations. Only then do we decide whether an LLM, RAG, an agent, conventional automation or a combination is appropriate.
Typical architecture questions
- Which information is structured and which is unstructured?
- Which systems and permissions already exist?
- Which results must be traceable or reproducible?
- How are failures detected and handled?
- What are the expected operating costs?
Evaluate existing systems first
Adding AI does not automatically mean replacing an existing platform. If current business software can be extended through APIs, databases or other interfaces, integration is often faster, lower-risk and more economical than rebuilding everything.
Make quality measurable
Generative systems need real test cases. Before production rollout, there should be explicit criteria for good answers, extractions or decisions, an understanding of acceptable errors and clear rules for when people must intervene.
Privacy and security are architecture concerns
Privacy cannot simply be added after an AI feature is finished. Data flows, providers, permissions, logging and retention need to be considered during design.
Production systems, not demos
Production AI software needs monitoring, cost controls, fallbacks and a strategy for model or provider changes. A strong prototype result is only the beginning.
FAQ
Questions about AI software development.
Direct answers to common questions before starting a custom AI project.
What does custom AI software development mean?
It means software designed around a specific process, product or organisation and enhanced with the AI capabilities that genuinely support that use case. Architecture, data models, user experience and integrations are not dictated by a rigid off-the-shelf product.
Do we need to build an entirely new application?
No. In many cases it is better to extend existing software. A rebuild is only necessary when the current architecture cannot support the requirements sensibly.
Which AI capabilities are common?
Common examples include document analysis, semantic search, RAG, assistants, agents, classification, extraction, summarisation and AI-assisted workflows.
How do you evaluate the quality of generative AI features?
With representative test cases, explicit quality criteria and separate evaluation of components such as retrieval, extraction and answer quality. Critical failure paths are additionally protected with deterministic rules, fallbacks or human approvals.
When is traditional software better than AI?
When the process is fully rule-based, deterministic and reliably solved with conventional logic. AI should be used where variable language, unstructured data or flexible decisions create genuine value.
Can you build custom AI software for an established business?
Yes. Custom AI software can be adapted to existing processes, roles, data sources and interfaces. Existing systems are often extended rather than replaced with an entirely new platform.
What does an AI software development company deliver beyond model integration?
A production project typically includes requirements analysis, UX, software architecture, backend development, interfaces, permissions, testing, evaluation, monitoring and operations. The AI capability is only one part of the overall system.
Let’s turn your AI idea into a concrete software project.
In an initial conversation, we clarify the objective, current situation and sensible next steps.
To get started, your objective, existing systems and the main open question are enough.