RAG-powered support agent auto-resolving 68% of tickets.
A SaaS company’s support team was overwhelmed with repetitive L1 tickets. We deployed a RAG-based AI agent that handles routine queries using the company knowledge base, escalating complex issues to humans with full context.
The short version.
A B2B SaaS platform with 15,000+ users was receiving 800+ support tickets per week, with 70% being repetitive questions already answered in their documentation. Their 6-person support team was burning out, response times were climbing, and customer satisfaction was dropping.
We built a RAG-powered AI support agent as part of our AI development practice that ingests the company’s knowledge base, product documentation, and historical ticket resolutions. The agent handles L1 tickets autonomously, provides instant responses, and escalates complex issues to human agents with full context and suggested solutions. Customer satisfaction actually improved from 4.1 to 4.6 stars.
Repetitive tickets drowning the support team.
The support team was stuck in a cycle: most tickets were variations of the same 50 questions, but each required a human to read, understand, and respond. The real problems:
- 70% repetitive tickets: password resets, feature how-tos, billing questions, and configuration help, all documented but customers didn’t search docs
- Rising response times: average first response climbed from 2 hours to 8+ hours as ticket volume grew
- Agent burnout: 40% annual turnover on the support team; answering the same questions daily was demoralizing
- Knowledge fragmentation: answers scattered across Notion, Zendesk macros, Slack threads, and individual agents’ memories
- No after-hours coverage: 30% of tickets came outside business hours from international users, waiting 12+ hours for responses
They’d tried chatbots before: rigid decision trees that frustrated users and handled less than 10% of queries successfully.
Context-aware AI with intelligent escalation.
Unlike rigid chatbots, our AI agent understands context (here’s how we evaluate when AI makes sense), retrieves relevant information from multiple sources, and knows when to escalate. Every response is grounded in the company’s actual documentation:
- RAG pipeline: knowledge base, docs, and 50K+ historical tickets indexed in a vector database; retrieved contextually for each query
- Multi-source ingestion: automated sync from Notion, Zendesk, product changelog, and API documentation
- Confidence-based routing: high-confidence answers sent directly; uncertain queries escalated with context summary and suggested resolution
- Conversation memory: multi-turn conversations with context carried across messages; no “can you repeat that” frustration
- Human handoff: seamless escalation to human agents with full conversation history, customer account context, and AI-suggested resolution
- Feedback loop: thumbs up/down on responses continuously improves retrieval quality and response accuracy
LangChain orchestration with vector retrieval.
The system uses LangChain to orchestrate retrieval, reasoning, and response generation. Pinecone stores vector embeddings of the knowledge base, and Claude generates grounded responses with citations.
RAG pipeline | 68% auto-resolution, 90sec avg response
The knowledge base syncs every 15 minutes, ensuring the AI agent always has current information. When product changes ship, the changelog is automatically ingested and the agent can answer questions about new features within minutes of release.
“Our support team was skeptical at first. Now they love it. The AI handles the repetitive stuff and when it escalates, it gives them all the context they need. Response times are down, satisfaction is up, and nobody’s burning out anymore.”
VP of Customer Success, B2B SaaS platform
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