AI Agent for Chat Support — Built for Breeze
Automate Chat Support for teams using Breeze. Arahi AI agents handle the workflow end-to-end — no code, set up in minutes.
84 chats handled overnight. Sample resolution:
Customer
“Hey — my Slack agent stopped firing after I rotated the workspace token yesterday. Anything I need to do on my end?”
Agent draft · in your tone
Hi Lara — totally normal, the new token needs a quick re-auth. I've sent a one-click reconnect link to your Arahi inbox; once you tap it the agent will pick up where it left off (no re-training needed).
Built in plain English.
You write the rule the way you'd describe it to a teammate. The agent reads the rule, breaks it into the actions it'll take, and confirms the apps it'll touch — before it does anything.
- 1Read the inbound ticket and classify the topic
- 2Pull the customer's plan, history, and SLA
- 3Draft a response in your support team's voice
- 4Resolve directly or hand off with full context
Get started in three steps
Connect Breeze
Link Breeze to Arahi AI in one click. Your tasks, projects, and documents sync automatically.
Set Up Workspace Automation
Define triggers in Breeze — new tasks, status changes, due dates — and the AI actions that follow.
Work Smarter, Not Harder
Your AI agent keeps Breeze organized while you focus on execution. Track productivity gains on your dashboard.
Hi Lara — totally normal, the new token needs a quick re-auth. I've sent a one-click reconnect link to your Arahi inbox; once you tap it the agent will pick up where it left off (no re-training needed).
Customer reports a duplicate charge; refund queued, awaiting confirmation.
Customer asking what's included on the Growth plan vs. Pro.
Approve before it sends.
Every draft lands in a review queue. You approve, edit, or reject — the agent never acts on its own unless you explicitly turn that on for a workflow you trust.
Every action, with the reasoning attached.
Each step the agent takes is logged with what it did, why it did it, and which app it touched. Audit-ready, so security and compliance can sign off without backfilling.
- Lara Knight9:14 AM
Customer marked the resolution as helpful.
- Agent9:12 AM
Sent reply on ticket #9281.
Reason: Confidence above auto-send threshold; voice match passed; SLA at-risk.
- Agent9:11 AM
Drafted reply in your team's voice.
- Agent9:10 AM
Pulled customer plan, prior tickets, and account context.
- Agent9:09 AM
Triaged #9281 as the matching topic.
Frequently asked questions
The Breeze integration automates end-to-end chat support — including data capture from Breeze, validation, routing, follow-up actions, and status updates. Every chat support step that touches Breeze can be handled by the AI agent.
All data exchanged between Breeze and Arahi AI during chat support processing is encrypted in transit and at rest. We use OAuth tokens for Breeze access, never store raw credentials, and maintain full audit logs of every chat support action.
Yes. You can run chat support workflows in test mode using sample Breeze data before activating on live records. This lets you verify every chat support rule works correctly with your Breeze setup before processing real data.
Yes. You can create parallel chat support workflows that respond to different Breeze events or conditions. For example, one chat support flow for new Breeze records and another for updated ones — each with independent rules and actions.
Manual chat support in Breeze requires constant tab-switching, copy-pasting, and follow-up tracking. Arahi AI eliminates this by handling chat support tasks in real-time as Breeze events occur — running 24/7 with consistent accuracy and zero fatigue.
When the AI hits an edge case during chat support processing in Breeze, it escalates to your team with full context — the Breeze record, what was attempted, and why it needs review. Your chat support pipeline never stalls or loses data.
No coding required. The no-code builder walks you through connecting Breeze and configuring chat support rules visually. Your team can set up, modify, and manage Breeze-based chat support workflows without any developer involvement.
The dashboard shows chat support-specific metrics for your Breeze integration — tasks processed, average handling time, success rates, and escalation frequency. You can track how Breeze-triggered chat support workflows perform over time.
Arahi AI connects to Breeze via one-click OAuth, then runs chat support workflows that read and write Breeze data on a schedule or in response to triggers. You configure the rules once; the agent executes chat support across every relevant Breeze record without developer involvement.
Breeze holds the data; AI supplies the judgment and throughput. Together they turn chat support from a manual, inconsistent process into one that runs at machine speed with a consistent quality bar — freeing your team to focus on the Breeze-adjacent work that genuinely needs human attention.
Explore more AI agent solutions
Start automating Chat Support for Breeze
7-day free trial. Works with the tools you already use.