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Reduction in Ticket Volume
AI Agent

How SwiftTech Support Reduced Ticket Volume by 65% With an AI Agent

Industry

SaaS / Enterprise Software

Size

500-1,000 Employees

Location

Austin, TX

Background

SwiftTech Support manages a complex IT infrastructure platform used by Fortune 500 companies. Their support team was overwhelmed by “Level 1” queries—repetitive questions about configuration, password resets, and error codes that were already answered in their documentation. Customers were frustrated by 24-hour wait times for simple answers, and support engineers were burning out from answering the same questions repeatedly.

The challenges

The VP of Customer Success wanted to automate support but refused to use a standard “chatbot.” Standard bots were too generic and often hallucinated, providing incorrect technical advice that could cause liability issues. They needed a solution that was strictly “grounded” in their own technical manuals—an AI that would say “I don’t know” rather than guess, and that could cite the specific documentation page where the answer was found.

We didn’t want a chatbot that talks; we wanted a chatbot that reads. This system acts like our best engineer is sitting there 24/7, quoting our own documentation back to the customer.

Sarah Jenkins

VP of Customer Success

The Solution

I built a custom RAG (Retrieval-Augmented Generation) pipeline using Python, LangChain, and a Vector Database.

We wrote scripts to scrape and index over 5,000 pages of SwiftTech’s technical documentation, Confluence pages, and PDF manuals into a Pinecone vector database.
Unlike keyword search, this system understands intent. If a user asks "How do we fix error 503?", the system retrieves the specific troubleshooting steps for that error, even if the phrasing is different.
We engineered the prompt to force the AI to include a direct link to the source document for every answer it provides, ensuring trust and transparency.

The Result

The AI Agent was deployed to the help center widget and Slack support channel.

The AI successfully resolved 65% of incoming queries without human intervention.
Average "Time to Resolution" for Tier 1 issues dropped from 4 hours to 2 seconds.
For the 35% of complex issues the AI couldn't solve, it seamlessly summarized the conversation and handed it off to a human engineer, saving them 10 minutes of triage time per ticket.

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