TeamCraft — Customer Operations
From ticket backlog to instant resolution.
50+
Locales supported
6
Channels (WhatsApp, Messenger, Telegram, WeChat, LINE, Web)
RTL
Arabic, Hebrew, Farsi, Urdu support
The Problem
The Ticket Triage Loop
Your support team receives 300 tickets per day. Five agents read each one, categorize it, look up the customer's account, and decide: fix it, escalate it, or send a template. 60% are routine — password resets, status checks, documentation questions. But every ticket goes through the same manual triage.
Impact: 4-hour average response for issues that should take 4 minutes. Agent morale drops. Complex issues buried behind simple ones.
The Knowledge Gap
A new product feature shipped last week. Support hasn't been briefed. Customers ask about it. Agents scramble to find documentation, ask in Slack, give inconsistent answers. The knowledge base hasn't been updated. It takes 2 weeks before the team is aligned.
Impact: Inconsistent customer experience. New feature questions generate escalations that shouldn't exist.
How TeamCraft solves this
Ticket Triage & Auto-Resolution
AI classifies incoming tickets by topic, urgency, and complexity. Routine issues (password resets, status checks, known issues) are resolved automatically with personalized responses. Complex issues are routed to the right specialist with full context.
Customer Action Execution
AI agents don't just respond — they act. Process refunds, update account details, reschedule appointments, trigger workflows in your CRM. The customer gets resolution, not a promise to escalate.
Knowledge Base Maintenance
AI monitors recurring ticket patterns and automatically drafts knowledge base updates. When the same question gets asked 10 times, the answer is codified and published without waiting for someone to write an article.
Churn Detection & Proactive Outreach
Account health monitoring based on support interactions, usage patterns, and engagement signals. AI identifies at-risk accounts and triggers proactive outreach before the customer decides to leave.