Aidena

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AI Document Processing Pipeline for Contracts & Invoices

End-to-end pipeline to extract structured data from PDFs using document intelligence, vector storage for retrieval, and workflow automation for scalable processing.

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AI Document Processing Pipeline for Contracts & Invoices

End-to-end pipeline to extract structured data from PDFs using document intelligence, vector storage for retrieval, and workflow automation for scalable processing.

high confidence

Core Stack ℹ︎

Azure Document Intelligence

Primary

Pre-built models for invoices and forms with high accuracy. Extracts tables, fields, and structured data directly from PDFs without custom training. Scales automatically with Azure infrastructure.

$0-50/month

Airbyte

Primary

Orchestrates data ingestion from document sources into data warehouse. 300+ connectors enable syncing extracted data to analytics platforms. Handles scheduling and monitoring of recurring document processing jobs.

$0-100/month

Chroma

Primary

Vector database for storing extracted document embeddings. Enables semantic search across contracts and invoices. Simple Python API for integration with extraction pipeline.

$0/month

Complete the Stack ℹ︎

dbt

Alternative

Transforms raw extracted data into clean, documented tables. Enables data quality tests and lineage tracking. Essential for scaling from ad-hoc extraction to production data pipelines.

$0-100/month

DeepEval

Alternative

Validates extraction accuracy with automated metrics. Tests hallucination and relevancy of extracted fields. Ensures quality as pipeline scales to thousands of documents.

$0/month

Getting started

  1. 1Set up Azure Document Intelligence with pre-built invoice and form recognizer models.
  2. 2Configure Airbyte to pull PDFs from cloud storage (S3, Azure Blob) on schedule.
  3. 3Create extraction pipeline that calls Azure Document Intelligence API on each PDF.
  4. 4Store extracted JSON in data warehouse (Snowflake, BigQuery, or Postgres).
  5. 5Generate embeddings from extracted text and index in Chroma for semantic search.
  6. 6Use dbt to transform raw extractions into normalized tables with data quality tests.
  7. 7Set up DeepEval to validate extraction accuracy on sample documents weekly.
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