AI engineering for e-commerce, CMS & internal workflows.
I integrate controlled AI capabilities into existing web applications, shops, and content systems with structured outputs, traceable data sources, validation, and human approval.
Applied AI on a full-stack foundation.
My background across PHP, Symfony, frontends, APIs, databases, CMS, e-commerce, and DevOps provides the foundation for AI capabilities that can become dependable parts of real products. Models are connected to data sources, rules, user interfaces, and operational processes rather than treated in isolation.
The AI Daily Insights, product content, and AI SEO assistant case studies document three different application patterns.
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 readiness means controllable behaviour.
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 avoids unnecessary model calls. Persistence, input versioning, and logs keep results traceable. Permissions, privacy, cost, and latency are considered during system design.
Working principle
AI should perform a clearly defined task inside a reviewable system. Critical actions remain bounded by permissions, validation, and human decisions.
Further technical detail is available in the project case studies and full-stack profile.