AI applications need data pipelines just as analytics does, with some new components.
Ingestion
Connect to document stores, wikis, ticketing systems, databases and websites. Capture permissions and metadata along with content.
Parsing
Extract clean text and structure from PDFs, Office files, HTML and images.
Chunking and Embedding
Split documents into chunks and generate embeddings, recording the model and version used.
Indexing
Load chunks, embeddings and metadata into vector or hybrid search indexes.
Freshness
- Incremental updates when documents change.
- Deletion when sources are removed.
- Permission sync.
Re-Processing
When chunking rules or embedding models change, re-process the corpus. Design pipelines for it.
Quality Monitoring
Track parsing failures, empty documents, duplicate content and index size.
Structured Data for AI
Clean, well-documented tables with clear definitions let AI assistants answer data questions accurately.