Generative artificial intelligence is no longer an experiment. It is now a practical way to solve real business problems: analysing thousands of documents, producing automatic reports, classifying customers and supporting teams in their daily work.
My work in this area focuses on implementing RAG systems (Retrieval-Augmented Generation) that combine the power of LLMs with each company’s own data. This is not about wiring up an OpenAI API call and hoping for the best. It is about designing ingestion, chunking, embedding and retrieval pipelines that behave reliably in production.
Typical stack
- OpenAI (GPT-4o, GPT-4) — Primary models for generation and reasoning.
- LangChain / LangGraph — Orchestration for complex chains and agents.
- Vector databases — Pinecone, pgvector and Chroma for semantic search.
- Python (FastAPI) — Production backends for AI services.
Real projects
- RAG system for automated analysis of technical documentation: PDF ingestion, semantic search and executive summaries.
- Autonomous customer-classification agents for CRM workflows, reducing manual processing time by 70%.
- Content-generation pipeline with human review in the loop, integrating GPT with approval workflows in n8n.
FAQ
- What is a RAG system for a business?
- A RAG system lets a language model answer using a company’s own documents, databases and internal knowledge, rather than relying only on the model’s general training data.
- When is an LLM agent worth building?
- An agent is worth building when the workflow needs to decide steps, call tools, validate information and produce an action, rather than simply answer a single question.
- What do you deliver in an applied AI project?
- I usually deliver a product-integrated system: data ingestion, retrieval, evaluation, an API, response traceability and a clear way for the team to operate it.
If you have data you are not yet using properly, there is probably a RAG system that can turn it into a competitive advantage. Contact me at [email protected].