Structured Insight Extraction From Unstructured Product Reviews

Added Jun 2025 3 design docs

E-commerce businesses treat reviews as a star rating and a wall of text, yet inside that text customers specify exactly which product attributes delight or disappoint them, including sizing, battery life, shipping condition, and durability. Because the information is unstructured, it never reaches the dashboards where decisions are made. Turning free-form reviews into structured, queryable records is the missing step between customer voice and business action. The intern builds an extraction tool that performs this transformation. The backend is a Python Flask service, and LlamaIndex provides the document handling: reviews are ingested and indexed so extraction runs over organized data rather than raw dumps. OpenAI models perform the structured extraction, pulling out defined fields for each review such as sentiment, product aspects, specific complaints, praised features, and comparison references, with the intern designing the extraction schemas and the prompts that keep model output consistently parseable. Aggregation endpoints then serve the structured results, letting a business see which aspects drive negative sentiment per product line. The project demonstrates the structured-extraction pattern that underpins commercial AI data products: schema design, reliable LLM output formatting, index-backed processing with LlamaIndex, and delivery through a clean Flask API.

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