10,000 documents. Staff spending 15% of their day searching. Now they just ask.
An online education company had a decade of internal documentation that nobody could navigate efficiently. moat8 deployed a closed LLM search system that generates direct answers — no external data, no security risk.
10,000 documents. No way to find the right answer without reading six of them.
The company had accumulated 10,000+ internal documents — onboarding manuals, process guidelines, product specifications, compliance policies, technical runbooks. Employees needing specific information had to search, open multiple files, cross-reference, and often still escalate to a colleague or the support team. Up to 15% of working time was spent on internal information retrieval. External AI tools were prohibited for security reasons, so there was no off-the-shelf solution.
Closed LLM deployment. Employees ask in plain language, get structured answers.
Document corpus ingestion
All 10,000+ documents parsed, chunked, and embedded into a private vector index
Closed deployment architecture
Entire system runs on internal infrastructure — zero data leaves the company perimeter
Natural language query interface
Employees type questions as they would ask a colleague — no query syntax, no folder navigation
Generative answer synthesis
LLM agent retrieves relevant chunks and composes a direct, structured answer with source citations
Confidence and source transparency
Every answer includes the source documents it drew from — employees can verify or dig deeper
Incremental update pipeline
New documents added to the index automatically — knowledge base stays current without manual curation











