Traditional search relies on exact keyword matching and basic indexing, which fails when meaning — not words — matters. Vector search represents documents as numerical embeddings that capture semantic relationships. This capability is transforming document intelligence: it helps teams find conceptually similar documents, surface hidden PII by context, and accelerate e-discovery and compliance reviews.
Embeddings are created by machine learning models that map text (or images) into high-dimensional vectors. In that vector space, semantically related items are near each other even when they share no words in common. For redaction and privacy workflows, vector search can surface documents that mention a person indirectly, use synonyms, or reference related entities — improving recall beyond keyword-only methods.
Key advantages of vector search
- Semantic recall: finds contextually related documents missed by keyword search.
- Cross-format discovery: works on OCRed text, transcripts, and metadata together.
- Facilitates clustering and topic detection for large archives.
- Enables intelligent similarity matching for de-duplication and near-duplicate detection.
Practical implementations often combine vector search with traditional filters: use fast keyword filters to narrow a corpus, then run nearest-neighbor searches on embeddings for semantic matches. Hybrid approaches balance precision, cost, and speed.
Another compelling use is proactive risk detection: embed sensitive templates and search for nearest-document matches to find potential instances of similar sensitive content across an organization. Vector search also powers smarter redaction suggestions — the system can recommend redaction candidates based on semantic similarity to previously redacted examples.
Operational considerations include embedding model choice, indexing strategy, and hardware for efficient nearest-neighbor queries. Vector indexes (e.g., approximate nearest neighbor libraries) scale to billions of vectors, but require careful tuning for latency and memory trade-offs. Ensure your privacy architecture protects embeddings as they can leak information if mishandled.
Bottom line: Vector search elevates document intelligence from literal search to semantic understanding — a game changer for discovery, redaction, and compliance at scale.
