AI Support Agent for SaaS
Client
Confidential SaaS Client
Timeline
5 weeks
Category
Support
The Problem
The client's support team of 6 was drowning in repetitive Tier 1 tickets - password resets, integration how-tos, billing questions, and basic API debugging. Response SLAs were slipping to 18 hours, CSAT was dropping, and the team had no capacity to handle complex Tier 2 issues that actually needed human judgment.
The Solution
We fine-tuned an OpenAI model on the client's full documentation library using LangChain's RAG pipeline, then deployed it as a chat widget on their support portal and a /help command on their Discord server. The agent resolves 60% of tickets without escalation, learns from resolved cases, and hands off complex tickets to humans with full context already written.
How We Did It
Knowledge Base Ingestion
Scraped and chunked 800+ pages of documentation, API references, changelogs, and resolved support tickets. Built a vector index in Pinecone.
RAG Pipeline
Built a LangChain retrieval chain that pulls the most relevant documentation chunks and injects them into the model context before generating a response - keeping answers grounded and accurate.
Escalation Logic
Designed a confidence-scoring layer that detects low-confidence answers, billing disputes, and account-specific issues, and routes them to Zendesk with a pre-filled ticket summary.
Multi-Channel Deployment
Deployed as a web chat widget (Intercom integration) and a Discord bot with slash command support - one shared knowledge base, two interfaces.
Tickets Deflected
Support Coverage
Average CSAT Score
Tools & Technologies Used
The Service Behind This
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