The Search Problem Inside Organizations
Knowledge workers spend 19% of their time searching for information. Traditional keyword search fails because people describe problems differently than documentation is written. Someone searching "how to handle a refund" will not find a document titled "Return Processing Policy v3.2" unless the exact words match. Semantic search solves this by understanding the meaning behind queries, matching intent rather than keywords, and returning contextually relevant results with source citations.
Natural Language Queries
Ask questions in plain English: "What is our refund policy for international orders?" The system understands the question, searches across all indexed content, and returns a direct answer with the source document linked.
Vector Database Infrastructure
Documents are converted into high-dimensional vector embeddings using leading embedding models. Stored in pgvector, Pinecone, Weaviate, or Qdrant, these embeddings enable sub-second similarity search across millions of documents.
Retrieval-Augmented Generation
Combine search with generative AI. The system retrieves the most relevant document chunks, then an LLM synthesizes a coherent answer. Every claim includes a citation so users can verify the source material directly.
Multi-Source Indexing
Index content from Confluence, Notion, SharePoint, Google Drive, Slack, email archives, PDFs, and databases. A single search interface queries across every knowledge source your organization uses.
Semantic Search Pipeline
Index
Documents chunked and embedded
Query
Natural language question received
Retrieve
Vector similarity finds top matches
Generate
LLM synthesizes cited answer
Index
Documents chunked and embedded
Query
Natural language question received
Retrieve
Vector similarity finds top matches
Generate
LLM synthesizes cited answer
Intelligent Search Architecture
How We Build Search Systems
Building effective semantic search requires more than plugging documents into a vector database. The quality of search depends ondocument preprocessing, chunking strategy, embedding model selection, and retrieval tuning.
Document preprocessing and chunking. Raw documents are cleaned, structured, and split into semantically meaningful chunks. We preserve section headers, table structures, and metadata through the chunking process. Overlap between chunks ensures context is not lost at boundaries. Different document types require different chunking strategies, and we tune these per content type.
Hybrid retrieval with reranking. Pure vector search works well for conceptual queries but misses exact matches. We combine vector similarity with BM25 keyword search using reciprocal rank fusion (RRF). A cross-encoder reranker then scores the combined results for final ordering. This hybrid approach outperforms either method alone by 15-25% on relevance benchmarks.
Access control and permissions. Not every employee should see every document. Our search systems respect your existing permission model. Documents indexed from SharePoint inherit SharePoint permissions. Confluence content respects space-level access. Search results only return documents the querying user is authorized to see.
Who This Is For
Semantic search is valuable for any organization with 500+ documents spread across multiple systems. Knowledge-intensive businesses like law firms, consulting agencies, healthcare systems, engineering companies, and financial institutions see the most immediate productivity gains. Customer support teams searching knowledge bases and sales teams searching proposal archives are common deployments.
If your team wastes time searching for information they know exists somewhere, contact us at ben@oakenai.tech to discuss building a search system that actually works.