Building an AI-Powered Knowledge Base with RAG for Enterprise Support
Enterprise Software
What They Were Facing
NovaBridge had accumulated over 50,000 support articles, API docs, and troubleshooting guides across Confluence, Zendesk, and internal wikis. Support agents spent an average of 8 minutes searching for answers per ticket, and customers frequently received inconsistent or outdated guidance. The knowledge was there — finding it was the problem.
How We Solved It
We designed and deployed a Retrieval-Augmented Generation (RAG) pipeline using Google Cloud Vertex AI and Gemini. The system ingests content from all knowledge sources, chunks and embeds documents into a vector database, and retrieves contextually relevant passages to generate accurate, cited answers. A Claude-powered review layer validates responses before surfacing them to agents via a Salesforce Service Cloud sidebar widget. The system includes automatic staleness detection, flagging articles that contradict recent product changes.
The Impact
The RAG pipeline delivers answers with 94% accuracy as measured by human evaluation, with source citations for every response.
Average handle time dropped from 12 minutes to under 8 minutes as agents receive AI-suggested answers with relevant article links instantly.
Automated ingestion pipeline processes new and updated articles within 15 minutes, with staleness alerts for contradicted content.
A customer-facing version of the knowledge assistant now resolves 41% of inbound queries without creating a support ticket.
“We needed something that actually understood our 50,000+ support articles and could cite sources — not a generic chatbot. The RAG pipeline answers questions with 94% accuracy, and our support team's average handle time dropped by 35%. The AI infrastructure scales beautifully and keeps getting smarter as we add content.”
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