AI Customer Support Assistant

Problem

Support teams spent significant time searching through documentation, past tickets, and internal wikis to answer recurring customer questions — leading to slow response times and inconsistent answers.

Context

A B2B SaaS company with a growing customer base, extensive product documentation, and a support team handling hundreds of tickets weekly.

Challenges

• Knowledge scattered across multiple systems
• Inconsistent answers depending on which agent handled the ticket
• Long time-to-first-response on complex questions
• New support agents required extensive training

Approach

Designed an AI support assistant that retrieves relevant documentation and past resolutions, drafts responses for agent review, and learns from approved answers over time.

Architecture

Document ingestion pipeline with chunking and embedding, RAG retrieval with metadata filtering by product area, human-in-the-loop approval workflow, and feedback loop for continuous improvement.

Implementation

Built document sync from help center and internal wiki, semantic search with confidence scoring, draft generation with source attribution, and an agent dashboard for review and one-click send.

Outcome

Support teams achieved faster first-response times and more consistent answers across agents. New team members became productive sooner with AI-assisted knowledge retrieval.

Technologies