I rebuilt a company's internal search from keyword-based to semantic.

The CEO's response after trying it: "Why does it feel like the search finally understands what I'm asking?"

Keyword search: user types "revenue growth Q3" — finds documents containing those exact words. Misses the document titled "Third Quarter Financial Performance" that has exactly the information they need.

Semantic search: user types "revenue growth Q3" — finds documents about third quarter financial results regardless of specific wording. Because it understands meaning, not just words.

The implementation is surprisingly accessible now. Embed your documents using a good embedding model (text-embedding-3-large or an open source alternative). Store embeddings in a vector database. At query time, embed the query and find the nearest documents.

But here's what separates good semantic search from great: hybrid search. Combine semantic similarity with keyword matching. Because sometimes the user wants exact matches ("error code XR-4472") and sometimes they want conceptual matches ("how to fix authentication issues").

Add metadata filters (date range, department, document type) on top for precision.

The total build time for a solid internal semantic search: about 2 weeks for an experienced engineer. The impact on organizational productivity is disproportionately large.

If your company is still using keyword search internally, this is the highest-ROI AI project you can pitch.