Semantic search agent over internal knowledge — policies, procedures, product docs, Confluence, SharePoint — using Vertex AI Search and Gemini grounding. Employees get direct, cited answers in seconds instead of hunting across fragmented systems.
We implement a production-grade enterprise knowledge agent on Vertex AI Search — indexing your internal documents, enforcing your existing access controls, and surfacing a natural language search interface via web app, Slack bot, or Microsoft Teams integration. Gemini grounding ensures every answer is anchored to your actual internal knowledge, not the model's general training.
Employees spend 2–3 hours per day searching for internal information — policies, procedures, past decisions, product specs — across fragmented systems with no single source of truth.
Wiki pages go stale, SharePoint becomes a graveyard of old versions, and no one trusts the knowledge base because it's never current — so teams just ask colleagues instead.
Sales, finance, and operations each maintain their own versions of core documents, creating inconsistency in decision-making and compliance risk from misaligned policies.
Employees ask questions in plain language and receive direct answers grounded in your internal documents — not a list of links to search through.
Every answer cites the specific document, section, and confidence level — so employees know exactly where the information comes from and can verify it.
Identity-Aware Proxy and IAM integration ensures users only receive answers from documents they are authorised to see — department boundaries and classification levels respected.
New documents added to Cloud Storage, Confluence, or SharePoint are automatically indexed within minutes — the knowledge base is always current without manual curation.
The agent searches across multiple knowledge bases simultaneously — policies, product docs, technical guides, and HR materials — and synthesises a unified answer.
Vertex AI Search supports indexing from Google Cloud Storage (PDF, Word, HTML, TXT), Google Drive, Confluence, SharePoint, and any source reachable via a web crawler or API connector. During implementation we configure the data connectors based on where your knowledge lives. The index is updated continuously so new content is available within minutes of publication.
No. The system uses Identity-Aware Proxy and your existing Google Workspace or Active Directory groups to enforce access controls at retrieval time. When a user submits a query, the agent only searches the subset of documents that user is authorised to view. A confidential HR policy will never surface in a query from a general employee, even if the question is relevant.
Gemini grounding means the model only answers from the retrieved document set — it does not use its pre-training knowledge to fill gaps. This prevents hallucination and ensures every answer is traceable to a specific internal source. If the information is not in your knowledge base, the agent says so rather than generating a plausible-sounding but incorrect response.
The index is kept in sync with your source systems. Deleted documents are removed from the index on the next sync cycle (typically within 15 minutes). Updated documents are re-indexed automatically. If a user queries for information in a deleted document, the agent will indicate that the source is no longer available rather than returning stale information.
Give every employee instant, accurate access to your internal knowledge. We implement a production-ready Enterprise Search Agent in 3 weeks.