I connect LLMs to your real systems — so AI stops being a chat window and starts doing work: reading documents, extracting data, drafting replies, updating your database, calling your APIs.
WHAT I AUTOMATE
• Document processing — extract structured data from PDFs, invoices, forms, even handwritten records (a live demo of this is on my profile)
• Email and text workflows — summarize threads, classify intent, draft responses held for human approval
• Data pipelines — classify, tag, enrich, and route records with an LLM in the loop
• API orchestration — the model calls your tools and services with function calling, with deterministic guardrails deciding what it may and may not do
HOW I BUILD IT
The LLM is one component, never the whole system. Validation, retries, logging, and hard rules around every model call — so the automation is reliable enough to run unattended. Structured outputs (JSON you can trust), cost controls, and honest failure handling instead of silent wrong answers.
TECH
OpenAI / Gemini / Claude, LangChain / LangGraph, function calling, Python / FastAPI, Node.js, webhooks, PostgreSQL, serverless (AWS / Cloudflare).
PROOF
Two live demos on my profile — an AI document-extraction product and a grounded chatbot with cited sources. Production automation delivered for clients under NDA, including an LLM summarization API on serverless infrastructure.
Tell me which manual process is eating your hours, and I'll reply with a plan, a timeline, and a fixed quote. Small paid pilots available.