AI Customer Support Platform
A retrieval-grounded support assistant for NovaDesk that resolves routine tickets automatically and hands off cleanly to human agents.
Customer: NovaDesk (demo)
Read the full case studyNovaDesk needed to handle a growing support volume without proportionally growing the support team. We built an AI support layer grounded in their documentation and historical ticket resolutions, integrated directly into their existing support inbox, with a defined escalation path for anything outside its confidence range.
Challenge
Support ticket volume was growing faster than NovaDesk's support headcount, and a large share of tickets were repeat questions already answered in their documentation — but customers weren't finding those answers on their own. Existing chatbot tools they'd evaluated gave confident-sounding but frequently wrong answers, which was worse than no automation.
Solution
We built a retrieval-augmented support assistant grounded in NovaDesk's help center, product documentation, and a curated set of previously resolved tickets. The system answers with citations back to source articles, and routes to a human agent whenever its confidence score falls below a defined threshold or the customer explicitly asks for a person. Agents can see the AI's draft response and edit before sending, rather than the system replying unsupervised on ambiguous tickets.
Architecture
A Next.js admin interface sits alongside the existing support inbox integration. Incoming tickets are embedded and matched against a vector index of the knowledge base, built and kept current through a nightly ingestion pipeline. Retrieved passages are passed to the model with the ticket content to generate a grounded draft response. A confidence-scoring step, combined with simple heuristics (question complexity, customer tier, sentiment), determines whether the draft is sent automatically, queued for agent review, or routed directly to a human.
Key features
Grounded response generation
Every answer is generated from retrieved documentation, not open-ended model knowledge.
Source citations
Customers and agents can see exactly which help article an answer came from.
Confidence-based routing
Low-confidence tickets go to a human instead of getting an automated guess.
Agent review mode
Agents can review and edit AI-drafted responses before they're sent.
Integrations
Support inbox
Direct integration with NovaDesk's existing ticketing system.
Help center CMS
Nightly ingestion keeps the knowledge base in sync with published articles.
Representative outcomes
Reduced first-response time
Routine questions get an accurate, sourced answer immediately instead of waiting in queue.
Support team capacity redirected
Agents spend more time on complex tickets and less on repeat questions.
Maintained answer quality
Citation-grounded responses avoided the confident-but-wrong failure mode of earlier chatbot attempts.
Technologies
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