AI Engineering for intelligent applications.
I build and integrate AI capabilities that extend existing data, applications, and business processes – from intelligent assistants to automated analytics and decision-support workflows.
Integrating AI into real products.
My full-stack background across backends, frontends, APIs, databases, CMS, e-commerce, and DevOps provides the foundation for my AI work. The result is not an isolated AI demo, but an integrated capability that can operate reliably inside a real application.
The focus is applied AI: connecting models and AI services with existing data sources, deterministic rules, interfaces, and user experiences. Structured results, traceable evidence, and human control remain part of the architecture.
From data to reliable AI capabilities.
Technical focus areas for AI-powered applications, automation, and data-informed decision support.
LLM Applications & Assistants
Context-aware AI capabilities and intelligent assistants with clear tasks, controlled data access, and structured results.
- Generative AI & LLM APIs
- Prompt & context engineering
- Structured outputs
AI Workflows & Automation
Repeatable processes in which AI analyses data, processes content, or generates recommendations – scheduled, event-driven, or initiated on demand.
- Workflow orchestration
- Tool & API integration
- Human in the loop
AI Analytics & Decision Support
Connecting operational data and analytics signals with AI-supported interpretation, prioritisation, and traceable recommendations.
- Data aggregation
- Insights & prioritisation
- Evidence-based outputs
Integration & Data Engineering
Embedding AI into existing web applications, CMS platforms, and shop systems through maintainable APIs, data pipelines, and backend processes.
- APIs & databases
- CMS & e-commerce
- Backend integration
Production AI & LLMOps
Production-minded implementation with validation, caching, logging, access control, monitoring, and manageable cost and latency.
- Evaluation & observability
- Security & privacy
- Deployment & operations
AI workflows designed for daily use.
Analytics and insight workflows
Data is collected and normalised on a schedule, then translated by a language model into prioritised observations and recommendations.
Intelligent assistants
Assistants combine domain context with structured outputs and connected data or tools while retaining control over critical actions.
Content and SEO workflows
Technical signals, content, and measurable data are captured separately, placed into semantic context, and prepared for human review.
Workflow or agent?
Predetermined processing steps are implemented as an AI workflow. Agentic patterns are appropriate where a system should choose tools or next actions within clearly defined boundaries.
Production capability, not an AI demo.
The real engineering work starts when a prototype needs to become a dependable part of a product. Inputs need traceable data sources, outputs need a validatable schema, and failures need controlled fallback behaviour.
Caching prevents unnecessary model calls from user interfaces. Persistence, input versioning, and logs make results more reproducible. Access control, privacy, and limited data sharing are considered as part of the system design.
Production workflows can include the OpenAI Responses API, structured JSON outputs, the GA4 Data/Admin API, Google OAuth 2.0, service accounts, protected cron endpoints, and Symfony Console. The concrete selection depends on the data source, security requirements, and operating model.
Current focus areas also include retrieval-augmented generation, semantic search, tool-enabled workflows, multimodal applications, and systematic AI evaluation.