LLM Data Pipeline — Python + Claude

Most teams sit on piles of raw, messy data — exports, spreadsheets, scraped records — that nobody can actually analyze. Cleaning and categorizing it by hand is slow, inconsistent and expensive, so decisions end up made on gut feeling instead of data.

A Python pipeline that uses the Claude API to turn raw records into structured, analysis-ready datasets:

Classification — every record gets a consistent category from a controlled taxonomy

Extraction — key fields are pulled out of unstructured text into clean columns

Enrichment — records are completed with derived and external data

Validation + retries — every output is checked against a schema; failures retry automatically, so bad data never reaches the final dataset

Analysis-ready output — structured data delivered where the team already works

Records are processed in batches through prompt templates designed per record type. Claude returns structured JSON that is validated before being accepted — anything that fails the schema goes back for an automatic retry. Every run is logged with records in, failures and retries, so data quality is measurable instead of assumed.

Python · Claude API · scraping & enrichment · schema validation with automatic retries · analysis-ready exports

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