Platform

Document ingestion pipeline from extraction to search

Extraction, enrichment, embedding, and indexing pipeline for retrieval-ready document systems.

Context

The core problem was moving messy, heterogeneous documents into a form that could support both analytics and search. The pipeline had to serve downstream AI use cases, not just storage.

Architecture

I treated ingestion as a sequence of explicit transformations: extraction, normalization, enrichment, embedding generation, and indexing. Each stage made the next stage more reliable.

Flow

Raw Documents
  -> Extraction
    -> Normalization
      -> Enrichment
        -> Embeddings
          -> Search Index

Key components

PDF and document extraction layer
Normalization and schema alignment stage
Enrichment and embedding generation
Search indexing into retrieval-ready structures

Tradeoffs

Raw document fidelity versus normalized schema consistency
Batch throughput versus retrieval freshness
Embedding cost versus search usefulness

Lessons

Data quality and retrieval quality belong in the same design conversation
Schema normalization should happen before vectorization, not after
Operational simplicity matters as much as model sophistication in ingestion systems