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Artificial Intelligence · Major Project
Document Analysis with LLMs
Extracts structured fields and summaries from PDFs into a table
3-4 weeks build
Overview
Turns batches of PDFs such as invoices, offer letters or research papers into structured records. You define a schema of fields, the system parses each document and asks an LLM to fill that schema, and every value is validated before storage. Low-confidence fields are flagged for review, and each document also gets a short summary.
What makes it stand up
- 01User-defined extraction schemas validated with Pydantic
- 02Batch processing with per-document status tracking
- 03Field-level validation and a review queue for doubtful values
- 04Short structured summary generated for every document
- 05Field accuracy measured against a hand-labelled sample
Module breakdown
- 01
Parsing
PDF text and table extraction with an OCR fallback.
- 02
Schemas
Define fields, types and rules for each document type.
- 03
Extraction
Structured-output prompts with retries on invalid JSON.
- 04
Review
Flagged fields shown beside the source page for correction.
- 05
Export
Searchable table view with CSV and JSON export.
After this, you will be able to
- Explain structured output and why validation is essential
- Defend your field-accuracy evaluation method
- Demo extracting fields from a fresh batch of PDFs
Technology stack
- Python
- FastAPI
- LangChain
- Pydantic
- PostgreSQL
- React
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 3-4 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support