"Our support team can now find accurate answers in seconds, not minutes. This has been a game-changer for our internal efficiency and the consistency of information we provide to customers."
The Challenge
As this SaaS company grew, so did its internal knowledge base, but it became fragmented and difficult to navigate across Confluence, Google Drive, and Zendesk. This created a major operational bottleneck, slowing down both customer support and sales enablement.
Our Solution
We deployed an Internal Knowledge Agent that acts as a single source of truth. It ingests data from all their key repositories. Now, instead of searching multiple systems, team members can simply ask the agent a question in natural language (e.g., “What are the integration limits for the Enterprise plan?”) and get an instant, accurate answer with source links.
Key Results
- 90% Reduction in Internal Query Resolution Time: Teams found answers almost instantly, freeing up valuable time.
- Improved Information Consistency: All team members provided accurate, up-to-date information to customers.
- 50% Faster New Hire Onboarding: New employees could quickly get up to speed by querying the AI agent directly.
- Reduced Support Escalations: Many common internal questions were resolved by the agent, reducing the load on senior staff.
Technology Spotlight
To deliver this solution, we leveraged a robust RAG (Retrieval-Augmented Generation) architecture:
- LLM: We utilized GPT-4 for its advanced reasoning and natural language understanding capabilities.
- Vector Database: Pinecone was used to store and retrieve document embeddings efficiently, ensuring fast and relevant responses.
- Data Connectors: Custom connectors were built for secure, real-time ingestion from Confluence, Google Drive, and Zendesk APIs.
Our Engagement Model
Our process is designed for seamless integration and continuous improvement:
- Discovery & Data Mapping: We began with a detailed audit of their internal knowledge sources and identified key user personas and common queries.
- RAG Pipeline Development: Our team developed the RAG pipeline, including data ingestion, embedding generation, and the core retrieval-augmented generation logic.
- Pilot & Feedback Loop: The agent was deployed to a pilot group for testing. Their feedback was crucial for fine-tuning the model’s responses and improving accuracy.
- Full Rollout & Monitoring: After successful piloting, the agent was rolled out company-wide. We established ongoing monitoring and a feedback mechanism to continuously update the knowledge base and refine the agent’s performance.