AI-Powered Document Intelligence for Enterprise Search

Organizations sitting on hundreds of thousands of documents had no good way to find what they needed, a real, recurring problem across enterprise and public sector clients.

Traditional keyword search wasn't surfacing the right documents, and teams were losing time hunting for information buried in large document repositories.

Balancing search relevance and speed at scale, while the underlying approach itself needed to evolve. Starting with basic context matching wasn't going to be the end state.

[ADD VERIFIED DETAIL: specific title/role, team structure]

The system started with basic context matching, then evolved into semantic search with AI-generated document summaries.

[ADD VERIFIED DETAIL: specific retrieval architecture, embedding/vector store choices, team workspace design]

[ADD VERIFIED DETAIL: why semantic search over keyword search, why specific AI agent design]

The platform has grown into a full product with team workspaces and AI agents, evolving from a rough idea into something people actually depend on.

Watching a product mature from a rough idea into something people depend on doesn't get old, and it reinforced that the right search approach depends on evolving with real usage, not guessing upfront.