Aftercare
Aftercare · Productivity · Marketing
Aftercare is an AI survey platform for conversational surveys. It asks follow-up questions based on what people write, flags junk responses and codes open-ended answers automatically.
About Aftercare
What Is Aftercare
Aftercare is a survey tool built around a simple complaint: fixed questionnaires get fixed answers. A respondent writes "the onboarding was fine" and the form moves on. Aftercare reads that line, notices it says nothing useful, and asks what specifically felt fine or didn't.
That behavior runs on large language models plus the company's own scoring logic. Each open response gets rated for completeness, relevance, consistency and nonsense, and those scores decide whether another question is worth asking. You can also set a hard cap on follow-ups so interviews don't drag.
The company launched out of Y Combinator's Winter 2024 batch. Founders are Aidan Lee, Justin May and Anand Nanduri, with research teams at Nextdoor, Datadog, MongoDB and Barclays listed as users.
Two ways to buy it. The full platform covers building, publishing and analyzing a survey in one place. The APIs drop the same follow-up, data quality and coding engines into Qualtrics, Forsta, Alchemer, Voxco, Intellisurvey or a homegrown tool.
The main limit is fit, not quality. AI follow-ups earn their keep on open-ended questions where people can ramble, explain or contradict themselves. A five-question rating scale survey gains little. Pricing isn't published either, so you have to talk to sales before you know the bill.
Getting Started
- Create an account on the Aftercare site, then start a survey from a blank canvas, a template or a one-line description of what you want to learn.
- Choose which questions get AI follow-ups and set the rules: stop when a response scores high enough, or stop after a fixed number of probes.
- Add branching, skip logic and question blocks in the workflow builder, then preview the path a respondent will see.
- Share the survey link, or connect an existing survey stack through the API if your team already runs fielding elsewhere.
- Watch responses come in with themes and categories attached, then export the coded data for your own analysis.
Product Information
A quick look at Aftercare's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Product and UX researchers who run qualitative studies and want detail without booking more interviews.
- Customer experience teams running post-purchase surveys, where the reason behind a score matters more than the score.
- Insights teams at mid-size companies without a dedicated analyst to hand-code thousands of open responses.
- Survey vendors and agencies that field studies for clients and need clean data before it reaches the report.
Tasks
- Running post-purchase surveys that dig into why someone picked a product, switched away or gave up at checkout.
- Testing concepts and prototypes, where follow-ups pull out the reasoning behind a first reaction.
- Cleaning open-ended survey data before analysis, filtering bots, duplicates and copy-pasted answers.
- Coding open text into themes so a 3,000-response study becomes a table you can read in an hour.
- Adding depth to NPS or CSAT programs that already have a score but no explanation.
Scenarios
- A churn study where one follow-up question turns "too expensive" into a specific missing feature.
- Event and conference feedback, where attendees type three words and the AI keeps pulling for the rest.
- Quarterly brand tracking that needs open-ended survey analysis on a fixed deadline.
- A research team that already fields in Qualtrics and wants AI follow-ups without migrating platforms.
Key features
AI Follow-Up Questions
Every open response gets read and scored, then Aftercare decides whether to probe. A vague answer triggers a request for an example, a partial answer triggers a request for the missing piece. The probing stops once a response clears your completeness threshold, which keeps interviews from running long.
Response Scoring You Control
The platform rates each answer for completeness, relevance, consistency and nonsense, and exposes those four numbers. You can use them to tune how aggressive follow-ups get, or to flag respondents whose answers don't hold together. Bots and low-effort entries can be dropped mid-survey instead of at the analysis stage.
Workflow Builder with Branching
A drag-and-drop canvas holds questions, follow-up blocks and branching logic in one view. You see the whole respondent journey before launch, which matters when a survey has twelve paths. Templates cover NPS, CSAT, exit, event, market research and employee engagement studies.
AI Response Categorization
Aftercare summarizes key points and sorts open answers into categories as they arrive. The result is a live theme table rather than a spreadsheet of raw quotes, and you can rename or merge categories when the automatic labels miss your framing.
AI Survey Generator
Describe the study in a sentence and the AI survey generator drafts a full questionnaire with question types and follow-up logic attached. It's the fastest way in, and it's free to try. Editing afterward is expected, since a generated draft rarely matches your exact wording.
AI Data Quality API
One endpoint handles survey data quality: it screens open text for off-topic answers, low-effort replies, nonsense, duplicates and LLM-generated responses. It also reads keystroke data from the browser to spot typing patterns that don't look human. Teams plug it into panels and fielding partners to reject bad respondents before they get paid.
AI Coding for Open-Ended Data
Upload a spreadsheet or call the API and Aftercare builds a code frame, then sorts each response into it. Analysts can edit categories and re-run the sort, which beats rebuilding a manual codebook every quarter. That kind of survey coding used to take a junior analyst a week. Here it runs while fieldwork is still open, and it's the piece that makes open-ended survey analysis affordable at volume.
Integrations with Existing Survey Stacks
Aftercare connects to Qualtrics, Forsta, Alchemer, Voxco and Intellisurvey, so follow-ups run inside the tool your team already uses. Custom integrations go through the same API. Nothing about the setup requires moving a live program to a new platform.
Pros and cons
Pros
- Follow-up questions pull specifics out of short answers, which is where most survey data loses value.
- Quality scoring works during fielding, so bad respondents get removed before they cost you a completed response fee.
- Coding and categorization arrive with the responses, cutting the gap between fieldwork and a readable report.
- API access means teams can keep Qualtrics or Forsta and still get the AI layer.
- A free account covers building and sharing a survey, which makes a trial cheap.
Cons
- No public price list. Budgeting starts with a sales call, and API costs scale with response volume.
- The value depends on open-ended questions. Closed rating scales see little benefit and still need the same setup.
- AI-generated follow-ups occasionally miss context in technical or niche audiences, so someone has to review early responses.
- Heavy customization of code frames and category labels takes practice, and the learning curve shows up in the first study.
Frequently asked questions
It runs surveys where the AI asks follow-up questions based on each answer, then scores, categorizes and codes the responses. You get longer, more specific answers and less manual sorting afterward.
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