Surveys for AI Products

Use cases for AI Products

Capture model onboarding

Ask immediately after signup and tag the answer by plan so the first review starts from a concrete moment. It keeps the decision tied to model onboarding / prompt quality.

Diagnose prompt quality

Separate prompt quality from output accuracy so the next action is not based on a combined complaint. Reviewers can compare the model onboarding / prompt quality slice without rebuilding context.

Improve output accuracy

Link the comment to lifecycle stage so the owner sees the path that produced it. The team sees whether model onboarding / prompt quality moved after the fix.

Route trust controls

Rotate renewal risk into the survey for one cycle when the team needs a deeper diagnostic. It turns model onboarding / prompt quality into a concrete operating note.

Validate technical clarity

Capture the blocker before users and buyers leave the support ticket step. The evidence remains anchored in model onboarding / prompt quality.

Rotate renewal risk

Send urgent trust controls notes to the owner of renewal review with the original comment attached. That separates model onboarding / prompt quality from background noise.

SurveyNinja features for AI Products

Output accuracy verbatim themes

Keep the strongest output accuracy quotes beside their score so product and support team can separate evidence from opinion. This keeps the model onboarding / prompt quality evidence separate.

Prompt quality follow-up trail

Record who owns each prompt quality issue and whether the next support ticket response changed. Use it as the model onboarding / prompt quality checkpoint.

Model onboarding timing view

Compare model onboarding by signup timing so late feedback does not distort the first signal. It protects the model onboarding / prompt quality signal from being averaged away.

Renewal risk evidence retention

Retain enough renewal risk context for audit and learning while removing details the reviewer does not need. The next review can start from the model onboarding / prompt quality context.

Technical clarity trend lens

Compare technical clarity before and after a change, then read the movement by feature rather than by total score alone. That gives the model onboarding / prompt quality owner a narrower brief.

Trust controls escalation rules

Flag urgent trust controls wording and send it to the owner of renewal review with plan still attached. The model onboarding / prompt quality pattern stays readable.

Signals to watch in AI Products feedback

Compare output accuracy comments by workspace role before rewriting the whole workflow. Use it as the model onboarding / prompt quality checkpoint.

Use plan and workspace role to decide whether the issue is local, segment-specific, or systemic. It protects the model onboarding / prompt quality signal from being averaged away.

Use the same technical clarity wording for two waves to learn whether the change held. The next review can start from the model onboarding / prompt quality context.

Capture the blocker before users and buyers leave the support ticket step. That gives the model onboarding / prompt quality owner a narrower brief.

How to collect AI Products feedback

The survey is a link: send it by e-mail, QR code or website widget, or in the SMS and messenger messages your own tools already send. It takes 1–2 minutes to answer

Signup follow-up

Send after signup so model onboarding feedback is captured before the detail fades. Use the result to prioritize the model onboarding / prompt quality lane.

Link in support replies

Add the survey link to support replies so users can rate output accuracy and prompt quality right after the conversation. The comment stays tied to the case and to model onboarding.

In-flow widget

Ask inside activation when the team needs a lightweight read on prompt quality. This gives model onboarding / prompt quality a clear before-and-after read.

Activation QR

Place the QR where users and buyers finish activation and still know what shaped the score. The review can isolate model onboarding / prompt quality before broader changes.

Mobile recovery pulse

Send a short mobile prompt when technical clarity or renewal risk deserves a fast check. It keeps model onboarding / prompt quality close to the moment that caused it.

Where to place surveys for AI Products

Signup

After Signup

What detail changed model onboarding most?

Signup follow-up

Activation

During Activation

Where did prompt quality create friction?

Activation QR

Support ticket

Before Support ticket closes

What would make output accuracy easier next time?

Embedded support ticket prompt

Renewal review

When Renewal review starts

Which part of trust controls needs follow-up?

Private review link

How to act on AI Products survey metrics

Model onboarding

plan

Review Model onboarding by plan before changing the full workflow. Keep the model onboarding / prompt quality slice separate.

Prompt quality

feature

Assign Prompt quality to the owner closest to the moment and compare the next wave through model onboarding / prompt quality.

Output accuracy

workspace role

Use verbatim Output accuracy answers to choose the next experiment for workspace role; keep model onboarding / prompt quality attached.

Trust controls

lifecycle stage

Escalate only Trust controls comments with clear risk language, then validate model onboarding / prompt quality in the following pulse.

Frequently Asked Questions

Which signal should AI Products review first?

Start with model onboarding if the team needs a baseline, or prompt quality if the latest complaints already point to a specific workflow. That gives the model onboarding / prompt quality owner a narrower brief.

When is the best moment to ask?

Ask around signup or support ticket, while users and buyers can still explain the detail behind the score. The model onboarding / prompt quality pattern stays readable.

How many questions should the survey include?

Keep it short: one main rating, two diagnostics for prompt quality and output accuracy, one open comment, and the segment fields your review uses. It keeps the decision tied to model onboarding / prompt quality.

Can SurveyNinja compare answers by segment?

Yes. Keep plan, feature, workspace role, and lifecycle stage on each response so broad trends do not hide local issues. Reviewers can compare the model onboarding / prompt quality slice without rebuilding context.

How should sensitive feedback be protected?

Restrict access when responses mention workspace data, user roles, and account context; reviewers still see the segment and action history they need. The team sees whether model onboarding / prompt quality moved after the fix.

Which channel should start the program?

Use signup follow-up for coverage, then add a QR or embedded form when feedback must stay attached to activation. It turns model onboarding / prompt quality into a concrete operating note.

What should the team track besides the score?

Track model onboarding, output accuracy, recurring comment themes, and whether assigned owners close the loop. The evidence remains anchored in model onboarding / prompt quality.

How do comments become action items?

SurveyNinja keeps the rating, comment, channel, and workspace role together so the next owner does not rebuild context. That separates model onboarding / prompt quality from background noise.

Can this run as an ongoing program?

Yes. Keep the core rating stable, rotate a diagnostic such as renewal risk, and compare movement by plan over time. Use the result to prioritize the model onboarding / prompt quality lane.

How do we avoid generic survey copy?

Remove any question that would not change prompt quality, trust controls, or the way product and support team protects adoption. The comment stays connected to model onboarding / prompt quality.

Plans for AI Products feedback programs

Choose the plan that matches your response volume and reporting needs. Full pricing

Free

$0/mo No time limit and no credit card required

Free — forever

Up to 50 surveys
100 responses/mo
30 questions per survey
Logic, export, API and MCP

Start free

Basic

$15/mo per month, billed annually

For small teams and regular feedback collection

100 surveys per account
1,000 responses per month
50 questions per survey
1 GB storage, files up to 100 MB
Image, video & audio in questions
Custom themes and your logo
Matrix, file upload and research question types
Response filters

Choose plan

Standard

$37/mo per month, billed annually

For marketing, HR and product research

500 surveys per account
5,000 responses per month
100 questions per survey
3 GB storage, files up to 300 MB
Response limit and deadline
Survey and question timers
No SurveyNinja branding
Incomplete responses

Choose plan

14 days free

Premium

$69/mo per month, billed annually

For large teams and advanced automation

∞ surveys per account
20,000 responses per month
∞ questions per survey
6 GB storage, files up to 500 MB
Custom roles for the team
Custom domain
AI report on responses
Promo codes

Start trial

Enterprise

On request

Custom plan for large companies

Unlimited resources
Up to 1,000,000 responses/month
Flexible task-based limits
Priority support
Number of seats
Monthly response limits
Disk space
Personal manager