RAG pipelines
Retrieval-augmented generation over your own content, with embeddings in PostgreSQL via pgvector — semantic search without standing up a separate vector database.
Available for contract, freelance and full-time engagements
AI Integration, RAG Pipelines & LLM Features
Adding an LLM to a product is easy; making it reliable and appropriate to the data is the actual work. I build AI features that retrieve from your data rather than guessing — including fully local pipelines for cases where the source material cannot leave the building.
What this covers
LLM-powered workflows
Claude API integration for automating conversations and business workflows, as built for the VoxBox marketing and lead-generation platform.
Local and privacy-first AI
Pipelines that run entirely on your own hardware through Ollama, so proprietary data never reaches a third-party API — the architecture behind CodeAtlas.
Structure-aware indexing
AST-based parsing so indexing follows real structure rather than arbitrary text chunks, which is what makes retrieval return the right context.
Tools and technologies
RAGpgvectorClaude APIOllamaLangchainVector DBNext.jsTypeScript
Proof of work
Projects where I have already done this, written up as case studies.
CodeAtlas
Privacy-first local AI tool that semantically indexes codebases and answers questions about them.
Read case study: CodeAtlasVoxBox Marketing
AI-powered customer engagement platform automating conversations, leads and marketing workflows.
Read case study: VoxBox MarketingEmotiReads
Book recommendations driven by real-time webcam emotion detection.
Read case study: EmotiReadsChatBot
AI chatbot on the Gemini API with a Laravel backend.
Read case study: ChatBotQuestions
Can we use AI on our data without sending it to a third party?
Yes. CodeAtlas is built exactly for that constraint — it semantically indexes codebases and answers context-aware questions with the whole pipeline running locally through Ollama and pgvector, with no reliance on cloud AI services.
What is RAG and do we need it?
Retrieval-augmented generation retrieves relevant passages from your own data and gives them to the model as context, instead of relying on what the model already knows. You need it whenever answers must reflect your specific, current or private content.
Do you need a dedicated vector database?
Often not. If you already run PostgreSQL, pgvector gives you semantic search in the database you are already operating and backing up. That is the approach I used on CodeAtlas.
Other services
LET'S TALK SPECIFICS
If ai integration & rag is what you need, tell me about the system, the constraints and the timeline.