I do not treat AI as an isolated chatbot added to the side of an application. I focus on integrating models into software systems where AI output can be validated, stored, transformed, monitored and used by the rest of the application.

• LLM API integrations • AI assistants • AI-powered application features • AI agents • Structured AI outputs • Automated data extraction • Classification workflows • Document processing • AI-powered search • Retrieval-based workflows • Backend AI services • API orchestration • Automated business processes • AI-assisted decision workflows • Multi-step AI pipelines

Determine what the model actually needs to do and where conventional software logic should be used instead.

Depending on the requirements, this can include direct model APIs, structured outputs, retrieval, tool use, validation or conventional automation.

Connect the model to the frontend, backend, database or other systems.

Implement appropriate validation, error handling, retries, rate limits and state management.

Monitor outputs and refine the implementation according to real-world behavior.

I can work with existing applications or build the required backend components for a new AI-powered feature.

Important: AI output should not automatically replace deterministic business rules or professional decision-making when the application domain requires human validation. The appropriate safeguards depend on the use case.

Pricing depends on the required model capabilities, integrations, data flow, reliability requirements and system complexity.