AI Agents for Working with Data
RAG systems, parsers, analytical pipelines — how I build tools for teams.
Most data in most companies is never analyzed. Not because the tools are missing — because the distance between “the data is somewhere” and “here's a clear answer” is too many steps. AI agents close that gap.
RAG over documents
Load 200 pages of contracts, policies, and specs — and ask questions in plain language. “What SLAs are in the contract with client X?” → answer in 3 seconds instead of 20 minutes of manual searching.
Trend parser
Pulls posts from TikTok, Twitter, and Telegram by keyword every day, clusters them by topic, ships a digest. The team reads for 5 minutes instead of monitoring for an hour.
Analytical pipeline
CRM data → normalization → LLM-based categorization → a table of insights. Replaces what an analyst used to do by hand in Excel.
A tech stack that works: Python + LangChain for orchestration, Claude or GPT-4 for inference, Chroma or Qdrant for embedding storage.
The main point: an agent isn't magic. It's a structured pipeline with a language model on one of the steps. If the pipeline is poorly designed, no agent will save it.