AI Knowledge Base Search
Give your team instant access to the answers they need. AI-powered semantic search understands questions in natural language and retrieves precise answers from your entire knowledge base.
The Problem
Why this matters
Enterprise knowledge is scattered across SharePoint sites, Confluence wikis, Google Drive folders, Slack channels, and individual inboxes. Employees waste an average of 1.8 hours per day searching for information, and 40% of the time they cannot find what they need. Traditional keyword search fails when people use different terminology, and critical institutional knowledge leaves the organisation when employees depart. The result is duplicated effort, inconsistent decisions, and frustrated teams.
The Solution
How AI solves this
AI-powered knowledge base search uses semantic embeddings and retrieval-augmented generation to understand the intent behind questions and retrieve precise answers from your entire knowledge ecosystem. Rather than returning a list of documents, the system delivers direct answers with source citations, enabling employees to get the information they need in seconds. The platform indexes content from any source and keeps its index continuously updated.
Benefits
What you gain
Instant Answers
Employees ask questions in natural language and receive direct, cited answers instead of wading through lists of documents.
1.8 Hours Saved Daily
Eliminate the time employees spend searching, asking colleagues, and recreating knowledge that already exists somewhere in the organisation.
Unified Knowledge Access
Search across every knowledge source — SharePoint, Confluence, Drive, Slack, email — from a single interface.
Preserve Institutional Knowledge
Capture and make searchable the expertise of long-tenured employees, reducing knowledge loss from turnover.
Source-Cited Responses
Every answer includes citations to the source documents, enabling users to verify information and explore deeper context.
Access-Controlled Results
Search results respect existing document permissions, ensuring users only see information they are authorised to access.
Process
How it works
Content Indexing
Documents from all connected sources are processed, chunked, and embedded into a vector database. The index updates incrementally as content changes.
Query Understanding
When a user asks a question, the system parses the intent, expands relevant terms, and converts the query into a semantic search across the vector index.
Retrieval & Ranking
The most relevant document chunks are retrieved and re-ranked based on relevance, recency, and source authority to identify the best answer sources.
Answer Generation
An LLM synthesises a clear, direct answer from the retrieved content, including inline citations to the source documents.
Feedback & Improvement
User feedback on answer quality is captured to continuously refine retrieval accuracy and identify knowledge gaps in your documentation.
Industries
Who uses this
Technology
Tools we use
FAQ
Frequently asked questions
We support all major enterprise platforms including SharePoint, Confluence, Google Drive, Notion, Slack, Microsoft Teams, and local file systems. Custom connectors can be built for proprietary systems. The platform ingests PDFs, Word documents, presentations, spreadsheets, web pages, and more.
The search system inherits permissions from your source platforms. When a user searches, results are filtered based on their access rights in the underlying systems. This ensures that confidential or restricted documents are only surfaced to authorised users.
Incremental indexing runs on a configurable schedule — typically every 15 minutes to one hour. Critical sources can be configured for near-real-time indexing. Most organisations find that hourly indexing provides a good balance between freshness and processing efficiency.
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