Embedding Recommendations

Last updated: 2026-05-18

HokAI uses embeddings and semantic search to power Smart Match and the directory search. Here's how it works.

What Are Embeddings?

Embeddings are vector representations of text. Similar concepts map to nearby vectors. "AI coding assistant" and "developer copilot" are close in embedding space; "AI coding assistant" and "social media scheduler" are farther apart. This makes it possible to match meaning, not just exact keywords.

How HokAI Uses Them

Smart Match embeds your query and context, then matches against tool descriptions, categories, and capabilities. The closest matches are ranked and returned as your recommended stack.

Directory search works the same way. Search queries are embedded and matched against tool profiles, so you'll surface relevant results even when your wording doesn't exactly match the tool's own description.

Validation

Recommendations are validated against our tools database. Every tool in a Smart Match result exists in the directory with real data. We don't generate or hallucinate tools.

• Embeddings and Vector Databases
• How Smart Match Works
• Data Methodology