AI coding agents are only as useful as the knowledge they can access. When an agent hits an unfamiliar question how tenant isolation works, where a data contract is defined, what the full authentication flow looks like it falls back to keyword search. It reads dozens of files, burns through context windows, and often surfaces incomplete answers. This isn't an agent capability problem. It's a documentation discovery problem.
This whitepaper documents how Covasant's engineering team solved this. Using only infrastructure already inside your Databricks workspace Delta tables, Serverless SQL Warehouse, and the Foundation Model API Auraa delivers full semantic search at $2–10/month, with no dedicated vector database, no idle costs, and no data leaving your governance perimeter.
Download the White Paper to Learn:
- Why keyword search breaks down at scale vocabulary mismatch, context loss, and the token economics that make exhaustive file reading unsustainable for production AI agents
- The Delta-native architecture: how Delta tables and Serverless SQL Warehouse compute semantic similarity on demand, scaling to zero when not in use
- Hierarchical chunking with LLM summaries why raw content chunks fail for conceptual queries and how document-level summaries improve precision while preserving a 42x token efficiency advantage
- A controlled benchmark across three approaches grep, structured index, and semantic search evaluated across 15 queries with precision, token consumption, and cost results published in full
- An honest cost comparison: Delta + SQL Warehouse vs. Databricks Vector Search vs. dedicated vector infrastructure, with annual projections at realistic usage levels