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Automate Refiner feedback summarization with AI

Swiftask connects your Refiner data to AI to condense thousands of customer feedbacks into strategic, actionable summaries.

Result:

Stop reading reviews manually. Identify key trends instantly.

Manual feedback analysis stalls your roadmap

Collecting feedback with Refiner is essential, but analyzing it is a major bottleneck. Your product team spends hours sorting spreadsheets, tagging comments, and searching for patterns, wasting precious time that could be spent on actual development.

Main negative impacts:

Information overload: The volume of customer feedback exceeds human analytical capacity, leading to critical insights being missed.
Selection bias: By reading only a fraction of reviews, you risk prioritizing features based on isolated opinions rather than deep-seated trends.
Slow product response: The delay between receiving feedback and making strategic decisions slows down your product roadmap agility.

Swiftask automates the extraction and summarization of your Refiner data. The AI categorizes feedback, detects pain points, and delivers clear reports for your product meetings.

BEFORE / AFTER

What changes with Swiftask

Traditional feedback management

The team exports Refiner data, spends hours cleaning files, tries to group topics by keyword, and ends up producing an outdated report.

Swiftask + Refiner workflow

Each new feedback is processed in real-time. Swiftask synthesizes trends and sends a weekly alert with identified priorities.

Setting up your AI summary in 4 steps

STEP 1 : Connect your Refiner sources

Link your Refiner account to Swiftask via API to enable automated retrieval of new responses.

STEP 2 : Define your analysis axes

Configure the themes you are interested in: bugs, feature requests, UX satisfaction, or pricing.

STEP 3 : Set your summary frequency

Choose to receive your summaries daily, weekly, or as soon as a critical volume is reached.

STEP 4 : Centralize your insights

Automate sending reports to your project management tool (Jira, Notion, Slack) directly from Swiftask.

What your AI agent can analyze

The agent examines sentiment, user context, and the recurrence of issues mentioned in your Refiner surveys.

Target connector: The agent performs the right actions in refiner based on event context.
Automated actions: Automatic categorization of verbatims. Detection of sudden anomalies. Generation of executive summaries for management. Exporting insights to your ticketing tools.
Native governance: Swiftask ensures every summary is based on real data, with full transparency on the sources.

Each action is contextualized and executed automatically at the right time.

Each Swiftask agent uses a dedicated identity (e.g. agent-refiner@swiftask.ai ). You keep full visibility on every action and every sent message.

Key takeaway: The agent automates repetitive decisions and leaves high-value actions to your teams.

Benefits for your Product team

1. Data-driven prioritization

Make informed decisions based on a global view, not just subjective impressions.

2. Operational time savings

Eliminate manual sorting tasks and focus on implementing solutions.

3. Increased reactivity

Detect a bug or customer frustration as soon as it emerges in feedback.

4. Cross-functional alignment

Share clear summaries with Marketing and Support teams for total consistency.

5. Effortless scalability

Whether you receive 10 or 10,000 feedbacks, the workload remains the same thanks to automation.

Data privacy and compliance

Swiftask applies enterprise-grade security standards for your refiner automations.

Native anonymization: Swiftask can mask personal data (PII) before analysis by the AI.
Secure storage: Your feedback travels through encrypted channels and is never used to train public models.
Access control: Precisely manage who can view summary reports within your organization.
GDPR compliance: The architecture is designed to meet European data protection requirements.

To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.

RESULTS

Performance of your automated analysis

Metric	Before	After
Weekly analysis time	8 hours (manual)	15 minutes (validation)
Feedback coverage	Partial sampling	100% analyzed
Trend detection delay	Several days	Real-time
Categorization accuracy	Variable (human)	Consistent (AI)

Take action with refiner

Stop reading reviews manually. Identify key trends instantly.

Uncover hidden product trends in your Refiner feedback

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