Why Naive Vector Chunking Hallucinates on Enterprise Data
Splitting PDFs every five hundred characters and running cosine similarity against a standalone vector store works on five documents and collapses at fifty thousand. Fixed-size splits sever markdown tables from column headers, pure dense embeddings miss exact part numbers and legal clause IDs, and post-retrieval permission filtering leaks sensitive executive files across workspaces.
Eternitech's RAG and vector search engineers build production retrieval pipelines anchored on PostgreSQL pgvector HNSW indexes, BM25 full-text search, Reciprocal Rank Fusion (RRF), and cross-encoder reranking—giving your product sub-second, citation-verified answers without introducing a fragile second database to synchronize.
Production Retrieval Architecture We Deliver
- Layout- & Heading-Aware Ingestion: Structural parsers for PDFs, DOCX, HTML, Confluence, Notion, and codebases that preserve section breadcrumbs, tables, and metadata—testable in our RAG Chunking & Overlap Visualizer.
- Pre-Retrieval Row-Level Security (RLS): Tenant, workspace, and user ACL filters enforced directly inside PostgreSQL transactions before vector scoring executes.
- Hybrid Dense + Lexical RRF + Reranking: Fusing semantic embeddings with exact keyword/SKU matching and top-twenty cross-encoder reranking to push Recall@5 above ninety percent on complex domain corpora.
- Inline Span Citations & Abstention Logic: Grounded generation prompts that bind every factual claim to verifiable document spans and abstain cleanly when evidence falls below confidence thresholds.
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Zero-Drift Synchronization With Live SaaS Data
Knowledge bases rot the moment source records update or get deleted. Directed by senior operators in Miami and Tel Aviv, our engineers wire Change Data Capture (CDC) and idempotent document-hash queues so edits, permission revocations, and GDPR deletions propagate to embeddings in seconds.
Frequently Asked Questions
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