Technical overview
How I integrate AI into existing products
As an AI engineer I integrate LLMs into products that already have users: chatbots, RAG over private documents, semantic search and workflow automation. The goal is a clear outcome - fewer support tickets, faster document triage, or catalog search that understands natural language.
I work with OpenAI and Claude APIs, orchestration layers, vector databases and Node or Python services with prompt checks and limits. Keys stay in environment variables; prompts are versioned; fallbacks exist when the model is unavailable or returns a weak answer.
RAG pipelines index your knowledge base so answers cite internal content instead of inventing policy. Local or private LLM setups can sit beside CRM data when residency or cost matters. Web, Telegram and WhatsApp assistants can share one backend. I prototype on a small dataset before full integration into Laravel, React or WordPress.
Common AI tasks I help with
- How to run a local LLM next to CRM data without leaking records
- How to build RAG over internal docs with source citations
- How to integrate a support chatbot into an existing help desk
- How to add prompt guardrails and block unsafe outputs
- How to version prompts and roll back a bad change
- How to add semantic search across a product catalog
- How to triage inbound email with an LLM and human escalation
- How to wire Telegram or WhatsApp bots to the same backend as web chat
- How to handle model timeouts with fallbacks and retries
- How to measure support load after launching an AI assistant