Use AI to request customer feedback
Automate the repeat path and keep human handoff clear.
3-day free trial · No charge during trial
What it handles
Works with
Why it matters
The practical reason to use it.
Manually handling feedback requests over SMS is slow, inconsistent, and hard to scale.
How it works
A step-by-step look at the workflow.
Step 1
A visitor starts a conversation over SMS — the agent identifies the intent and begins collecting sentiment, usage context, and improvement signals.
Step 2
The agent checks your knowledge base and Product guidance, Lifecycle events, Knowledge base to determine the right next step.
Step 3
Once enough context is gathered, the agent requests customer feedback without forcing people into a human queue.
Step 4
If the request falls outside the agent's scope, InsertChat escalates to a human via SMS conversations with the full conversation summary attached.
Step 5
You review which feedback requests conversations resolved end-to-end, where escalation happened, and what rules to tighten for better throughput on the next.
Task flow
How the assistant handles repeat work.
Feedback Requests
The agent requests customer feedback over SMS by collecting sentiment, usage context, and improvement signals before it decides what should happen next.
SMS coverage
Deploy the same workflow across SMS conversations when response speed matters more than a full portal experience, so the task starts where.
Self-serve completion
Resolve straightforward requests end-to-end so the team only intervenes when judgment or approval is actually required.
System actions and handoff
Once the conversation is ready, InsertChat can collect usable feedback while the experience is still fresh, and it can escalate to a.
Accuracy controls
How answers stay accurate.
Grounded in your sources
Responses stay tied to the docs, policies, and structured data your team already trusts for feedback requests.
Rules before replies
Use approval logic, routing thresholds, and business rules before the workflow changes status or triggers downstream actions.
Human review when needed
InsertChat hands off the edge cases, exceptions, and judgment calls instead of pretending every conversation should be fully automated.
Visible automation performance
Track which conversations resolved end-to-end, where escalation happened, and what to tighten next for better throughput.
Add next
Useful next automations.
Speed up first value
Use the same workflow to answer setup questions, recommend next steps, and prevent new users from stalling in the first session.
Collect context once
Capture goals, role data, and activation blockers early so every follow-up has better context than a blank support ticket.
Re-engage before churn
Trigger the right guidance or reminder when usage drops, plan limits are hit, or trial milestones arrive.
Close the feedback loop
Turn onboarding feedback, review requests, and renewal signals into structured workflows instead of scattered asks.
What you get
The changes teams should notice first.
- Less manual work on repetitive conversations
- Faster resolution without human bottlenecks
- Consistent execution every time, at any scale
- Clear visibility into what gets automated and what doesn't
What our users say
Businesses use InsertChat to launch branded assistants faster and keep their knowledge in one branded AI assistant.
Finally, one place for all my AI needs. The ability to switch models mid-conversation is game-changing.
Sarah Chen
Product Designer, Figma
We deployed AI support in 20 minutes. Our response time dropped by 80%. Customers love it.
Marcus Weber
Head of Support, Notion
The white-label option let us offer AI services to our clients overnight. Revenue grew 40% in Q1.
Elena Rodriguez
Agency Founder, Digitale Studio
Commonquestions
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InsertChat
Product FAQ
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Use AI to request customer feedback FAQ
Can an AI agent request customer feedback without human approval?
Yes — you configure exactly which feedback requests actions the agent takes autonomously and which require human review. For example, the agent can request customer feedback without forcing people into a human queue on its own, but escalate edge cases based on thresholds you set. Routine feedback requests cases resolve end-to-end while exceptions get flagged for a person to review.
How does the agent know how to request customer feedback correctly?
The agent is grounded in your knowledge base and Product guidance, Lifecycle events, Knowledge base. It collects sentiment, usage context, and improvement signals before deciding the next step, and it can collect usable feedback while the experience is still fresh once enough context is gathered. It never improvises — it follows the sources and logic you configure, then keeps the next owner in the loop when the workflow needs a handoff.
What happens when the agent can't handle a feedback requests request?
InsertChat hands the conversation to a human via SMS conversations with the full context already attached — the user doesn't repeat themselves. You configure when handoff triggers based on confidence thresholds, request complexity, or sentiment, usage context, and improvement signals that falls outside the agent's scope. The result is a cleaner escalation instead of a dead-end chat.
Does feedback requests automation work over SMS?
Yes. The agent requests customer feedback across SMS conversations when response speed matters more than a full portal experience. The same workflow, knowledge base, and escalation rules apply regardless of where the conversation starts, so the task execution stays consistent at any scale and across every channel you enable.
How do teams measure whether feedback requests automation is working?
Teams usually measure resolution time, handoff quality, and how many conversations finish without manual re-entry. If those numbers improve, the workflow is doing real work instead of just deflecting messages. That makes it easier to expand the automation into adjacent steps once the first path is reliable.
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3-day free trial · No charge during trial