NPS, CSAT, and product feedback that actually feeds back.
ChatGPT sends the right survey at the right moment, classifies the response by theme, routes detractors to your CSM with full context, and clusters open-ended feedback for your product team.
247 surveys sent · 3 detractors routed. Sample reply trigger:
How does OpenAI (ChatGPT) work for Feedback Collection automation?
OpenAI (ChatGPT) works for Feedback Collection automation by powering a DeskFerry agent that runs the workflow end-to-end inside your existing tools — no code, no custom build.
- 01
Trigger fires
The agent connects to OpenAI (ChatGPT) alongside the other apps your team already uses, watches for the triggers that matter for Feedback Collection, and takes the next step on its own while keeping a complete audit trail for review.
- 02
Send NPS surveys via @ChatGPT 30 days post-launch
Trigger feedback requests at optimal moments in the customer journey.
- 03
Route detractors to Customer Success in @HubSpot
Teams typically see higher than one-shot survey sends once the agent is in production.
- 04
Tag themes for the product team
You stay in control: every action is logged, confidence thresholds are configurable, and anything ambiguous is queued for a human instead of being silently auto-completed.
- 05
You approve
Anything under your confidence bar waits for a human.
How you tell it what to do
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.
- 1Send NPS surveys via @ChatGPT 30 days post-launch
- 2Route detractors to Customer Success in @HubSpot
- 3Tag themes for the product team
Already wired in
ChatGPT is built into DeskFerry.
No API key, no model setup, no glue code. Connect the apps your team already uses and feedback collection runs on them.
ChatGPT runs inside DeskFerry on our keys.
- HubSpotConnect
Runs on your data, in your apps.
Nothing to deploy. Nothing to maintain.
Actions
What OpenAI (ChatGPT) + DeskFerry can do
Real OpenAI (ChatGPT) actions your AI agent can perform automatically — no manual work required.
Create Assistant
Tool to create a new assistant with specified parameters. use after finalizing model, tools, and instructions.
Create Message
Tool to create a new message in a specific thread. use when adding messages to an existing conversation after confirming the thread id.
Create Thread
Tool to create a new thread. use when initializing a conversation with optional starter messages.
Delete assistant
Tool to delete a specific assistant by its id. use when you need to remove an assistant after confirming its id.
Delete file
Tool to delete a file. use when you need to remove an uploaded file by its id after confirming the target.
List files
Tool to retrieve a list of files. use when you need to view all files uploaded to your organization.
List fine-tunes
Tool to list your organization's fine-tuning jobs. use when you need to review all fine-tune runs.
List models
Tool to list available models. use when you need to discover which models you can call. use after confirming your api key is valid.
List run steps
Tool to retrieve all steps of a specific run. use when you need to inspect each step's details after initiating a run.
Modify thread
Tool to modify an existing thread's metadata. use after obtaining the thread id when you need to update metadata.
Saw your 4/10 — what would have made it a 9?
ChatGPT flagged your NPS reply for follow-up. You said the onboarding video was hard to follow — that's on us, and I'd love 10 minutes to walk you through the parts that bit.
Personalized using LinkedIn activity from the last 30 days.
Human in the loop
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.
Governance
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.
- Marco11:42 AM
Approved the draft to alex@kibblecorp.com.
- Agent11:41 AM
Drafted the email and queued it for review.
Reason: High-confidence personalization but recipient is C-level — escalating per policy.
- Agent11:40 AM
Pulled LinkedIn activity and HubSpot deal context.
- Agent11:40 AM
Triggered: Send NPS surveys via @ChatGPT 30 days post-launch, route detractors to Customer
- Agent11:38 AM
Confirmed sender domain DKIM is healthy.
How it works
Get started in three steps
Step 01
Connect OpenAI (ChatGPT)
Authorize OpenAI (ChatGPT) in your DeskFerry dashboard. The secure connection takes less than 60 seconds.
Step 02
Configure Your AI Agent
Set up triggers, actions, and conditions specific to how your team uses OpenAI (ChatGPT).
Step 03
Deploy & Monitor Results
Your AI agent goes live immediately. Track tasks automated, time saved, and accuracy metrics in real-time.
Start automating Feedback Collection for OpenAI (ChatGPT)
7-day free trial. Works with the tools you already use.
FAQ
Frequently asked questions
When does it decide to send a survey?
Based on your trigger rules — onboarding milestone, support resolution, 30-day usage, renewal anniversary. It also throttles per-customer so no one gets surveyed more than your defined cadence.
How does theme classification work?
The agent reads each open-ended response, tags it against your theme taxonomy, and surfaces new themes when ~5%+ of responses don't fit. You confirm or reject the new themes monthly.
Does it handle the detractor follow-up itself?
It drafts the follow-up in your CSM's voice with a specific reference to what the detractor said. The CSM reviews, edits, and sends. Some teams configure auto-send for known patterns.
What survey tools does it integrate with?
Typeform, SurveyMonkey, Qualtrics, Delighted, and direct email surveys. Results feed back into your CRM, customer success platform, and product analytics.
How is this different from running NPS in Delighted?
Delighted handles distribution and basic dashboards. This adds context-aware send timing, response classification at scale, theme clustering, and personalized detractor follow-up — the work humans were doing on top of Delighted.
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