Project Overview
SupportIQ's product was taking off — and so was its support queue. The same questions arrived hundreds of times a day, response times slipped, and headcount costs climbed in lock-step with growth. They needed leverage, not just more agents.
CodeHypes built a retrieval-augmented AI agent grounded in their own documentation and connected to their CRM. It answers around the clock, takes real actions, and hands off to a human with full context when a case genuinely needs one. We wrapped it in an agent console so the support team could supervise, correct and improve it over time.
The agent now resolves 72% of tickets autonomously and cut first-response time by 80% — turning support from a cost centre under strain into a fast, scalable experience.
The Challenge
A scaling SaaS company was drowning in repetitive support tickets, with slow response times and rising costs as volume grew.
Discovery
We audited ticket data to find the high-volume, automatable intents, then designed the RAG pipeline and human-in-the-loop guardrails.
Development
Month 1
Foundation & core
Month 2
Features & data
Month 3
Polish & QA
Launch
Go-live
Growth
Optimize & scale
Integrations
Final Product
Drag to compare the experience before and after.
Results
Lessons Learned
Grounding the model in real docs (RAG) and keeping a human in the loop made the agent trustworthy enough to deploy to real customers.
Client Testimonial
“It handles the repetitive 70% flawlessly and knows when to bring in a human. Our response times have never been better.”