Some tasks can't be solved with rules. Sorting inconsistent records, pulling structured fields out of free text, classifying entries by meaning rather than keywords, summarizing documents at volume — that is where an LLM earns its cost.
I integrate Claude or GPT into your existing workflow through their APIs: batch processing, structured JSON output you can rely on, error handling for the cases where the model is unsure, and cost control so a large run doesn't surprise you.
Live example in my portfolio: a production Flask app that reads uploaded letters and PDFs, explains them in plain language, and extracts action items — Claude vision reads images directly, no OCR pipeline to maintain. You can open it and try it.
I also say no when an LLM isn't the right tool. Plenty of problems are cheaper and more reliable as plain code, and I'll tell you when that's the case.
What I need to start: a description of the task, a sample of the input data, and the output format you want.