Build robust document ingestion pipelines for AI knowledge bases. Covers PDF/Word/PPT parsing, OCR for scanned documents, chunking strategies, embedding generation, vector database storage, and handling 100K+ documents at enterprise scale.
Enterprise document ingestion involves: parsing PDFs/Word/PPT with structure preservation, OCR for scanned documents, intelligent chunking (semantic vs fixed-size), generating embeddings via models like text-embedding-3-large, and storing in vector databases (Pinecone/Weaviate/Qdrant). Boolean & Beyond builds pipelines handling 100K+ documents with incremental updates, metadata extraction, and access control — typically taking 2-3 weeks to productionize.
Building an enterprise AI knowledge base sounds straightforward — just feed your documents to an LLM. In practice, document ingestion is where most enterprise AI projects fail or deliver poor results.
The challenges are real: PDFs with complex tables lose their structure during parsing. Scanned documents from legacy systems need OCR. PowerPoint presentations mix text with diagrams that carry meaning. And a 200-page policy document needs intelligent chunking so the right paragraph surfaces when an employee asks a specific question.
Getting ingestion right means your AI assistant gives accurate, sourced answers. Getting it wrong means hallucinated responses that erode employee trust in the system. Indian enterprises often deal with additional complexity — bilingual documents, government compliance forms with specific formatting, and legacy systems storing data in non-standard formats.
Enterprise knowledge bases contain documents in dozens of formats. Each requires a different parsing strategy.
PDFs (the most common and most challenging):
Microsoft Office documents:
Web-based sources:
Pro tip: Always preserve document metadata (filename, author, creation date, department) during parsing. This metadata powers access control and helps the LLM cite sources accurately.
Chunking — splitting documents into retrieval-sized pieces — directly determines whether your AI gives precise answers or vague summaries.
Fixed-size chunking (simple but limited):
Split text every 500-1000 tokens with 100-200 token overlap. Easy to implement, works reasonably well for homogeneous documents. Problem: it splits mid-paragraph, mid-table, and mid-argument, destroying context.
Semantic chunking (recommended for most use cases):
Split at natural boundaries — headings, paragraph breaks, topic changes. Use heading hierarchy to maintain section context. A chunk from "Section 3.2: Leave Policy for Contract Employees" carries its section title as context, dramatically improving retrieval relevance.
Recursive chunking:
Start with the largest meaningful unit (a full section), then recursively split only if it exceeds the token limit. Preserves context better than fixed-size while staying within embedding model limits.
Agentic chunking (advanced):
Use an LLM to analyze document structure and create semantically meaningful chunks. More expensive (requires an LLM call per document) but produces the highest quality chunks. We use this for high-value documents like legal contracts and compliance manuals.
Our recommendation: Semantic chunking with heading-based boundaries for 80% of documents. Agentic chunking for high-value documents where retrieval accuracy is critical. Target chunk size of 300-500 tokens for text-embedding-3-large, 200-400 tokens for Cohere embed-v3.
Once documents are chunked, each chunk needs to be converted into a vector embedding for storage and retrieval.
Batch processing architecture:
For initial ingestion of large document sets (10K+ documents), batch processing is essential. Use a job queue (Redis Queue or Celery) to manage embedding generation. Rate limiting against embedding API quotas prevents 429 errors.
Embedding pipeline flow:
Processing speed benchmarks:
For 100K documents (approximately 2M chunks after splitting), expect:
The initial ingestion is just the beginning. Enterprise documents change constantly — new SOPs are published, policies are updated, product documentation evolves.
Change detection strategies:
Update pipeline:
When a document changes, the system must: re-parse the document, re-chunk it, generate new embeddings, and replace the old vectors in the database. This must happen atomically — users should never see partial updates where some chunks are old and some are new.
Deletion handling: When a document is archived or deleted, all associated chunks and vectors must be removed from the vector database. We tag each vector with its source document ID, making bulk deletion straightforward.
Recommended update frequency: Real-time for critical documents (HR policies, compliance docs). Daily batch for general documentation. Weekly for archived/reference material.
At enterprise scale, the ingestion pipeline needs to handle tens of thousands of documents reliably.
Infrastructure for scale:
Common failure modes and mitigations:
Testing retrieval quality:
After ingestion, evaluate retrieval quality by testing 50-100 representative queries against expected source documents. Target 85%+ retrieval accuracy (correct source document in top 5 results) before going live. We maintain evaluation datasets for each client and run quality checks after every major ingestion update.
Boolean & Beyond has built document ingestion pipelines processing 500K+ documents for Indian enterprises. We handle the messy reality of enterprise data — mixed-language documents, legacy formats, compliance requirements, and government forms with specific layouts.
Our production pipelines achieve 90%+ parsing accuracy across document types, with intelligent chunking that preserves context for high-quality retrieval. We include monitoring, error handling, and incremental update capabilities from day one — not as afterthoughts.
Typical pipeline deployment takes 2-3 weeks from document audit to production, with ongoing optimization as your document corpus grows. Contact Boolean & Beyond to discuss your document ingestion requirements.
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Boolean & Beyond
Enterprise AI Copilot & Internal Knowledge Base · Updated 20 Mar 2026
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Boolean and Beyond
825/90, 13th Cross, 3rd Main
Mahalaxmi Layout, Bengaluru - 560086
590, Diwan Bahadur Rd
Near Savitha Hall, R.S. Puram
Coimbatore, Tamil Nadu 641002