AI app for product development · no coding needed
Session replay issue detection workspace
Reduce manual replay review while routing evidence-backed issues to the people who fix them.
Made for: Product and engineering teams running web or mobile apps with session replay data
What it does for you
The problem
User experience issues and bugs sit unseen in session replays because manual review does not scale and findings never reach the team workflow.
What it gives you
Reviewer-approved issue reports linked to replay evidence
What you give it
Captured sessionsSDK eventserrorsuser feedback
Build your own version of Prism AI, Lucent and more
One app with what these 6 AI tools do, yours to keep and change: Prism AI, Lucent, Stey, Stey.ai, Human Behavior, Sprig 2.0 AI Product Experience Platform.
Everything these tools do, in one app
- Session replay capture Records and plays back user sessions so teams can see exactly how people interacted with the product.Found in Prism AI, Lucent, Stey and 3 more
- Automatic issue detection Uses AI to automatically find errors, broken flows, and user pain points in session replays without manual review.Found in Prism AI, Lucent, Stey and 2 more
- No manual setup Works without requiring event tagging or complex instrumentation to start analyzing sessions.Found in Prism AI
- Severity scoring Rates and categorizes issues so teams can prioritize the most important problems first.Found in Lucent
- Reproduction context delivery Automatically sends video snippets, steps, and metadata into team workflows to speed up debugging.Found in Lucent
- Weekly reports Provides regular summaries that highlight recurring patterns and user friction across sessions.Found in Lucent
- Natural language search Lets users query session data in plain language to quickly find specific actions or behaviors.Found in Stey.ai
- AI-generated summaries Automatically summarizes user behavior and highlights issues or optimization opportunities.Found in Stey.ai
- User experience reports Generates reports that outline recent experience problems and allow further investigation via session replays.Found in Stey, Stey.ai
- Instant analysis Provides quick, AI-powered insights that enable immediate adjustments and optimizations.Found in Stey
- SDK capture Collects events, errors, and session replays through an SDK integrated into the product.Found in Human Behavior
- Background agents Runs automated agents that take actions like emailing customers, creating issues, and opening pull requests based on replay findings.Found in Human Behavior
- Agent-driven results checking Automatically checks the outcomes of actions taken and feeds improvements back into the product without human monitoring.Found in Human Behavior
- Slack/SMS reporting Delivers updates and reports through messaging platforms like Slack, SMS, and WhatsApp instead of a dashboard.Found in Human Behavior
- Multi-modal data collection Combines replays, heatmaps, surveys, and user feedback to provide a holistic view of user experience.Found in Sprig 2.0 AI Product Experience Platform
- Real-time user observation Captures live interactions and emotional responses to understand user motivations behind behavior.Found in Sprig 2.0 AI Product Experience Platform
- Lightweight SDK Minimizes impact on app or website performance while ensuring data privacy by avoiding PII and browser cookies.Found in Sprig 2.0 AI Product Experience Platform
- Customizable research studies Enables teams to launch targeted studies by asking questions within the platform to streamline user research.Found in Sprig 2.0 AI Product Experience Platform
How it works, step by step
- Capture and play back user sessions
- Detect errors, broken flows and pain points automatically
- Start analysis without event tagging or instrumentation
- Score and categorize issue severity
- Attach replay snippets, steps and metadata to issues
- Summarize recurring patterns in weekly reports
- Query session data in plain language
- Generate AI summaries of behavior and opportunities
- Produce experience reports that link to replays
- Deliver instant analysis for immediate adjustment
- Collect events, errors and replays through an SDK
- Run background agents that email customers, create issues and open pull requests
- Check agent action outcomes and feed improvements back
- Send updates through Slack, SMS and WhatsApp
- Combine replays, heatmaps, surveys and feedback
- Observe live interactions and emotional responses
- Keep the SDK lightweight and avoid PII and cookies
- Launch targeted in-platform research studies
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewer-approved issue report linked to replay evidence with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Session replay issue detection workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Session replay issue detection workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data200 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce manual replay review while routing evidence-backed issues to the people who fix them. For product and engineering teams running web or mobile apps with session replay data, convert captured sessions, SDK events, errors and user feedback into reviewer-approved issue reports linked to replay evidence. The benefit is a testable hypothesis, measured through confirmed issues per review hour and time from detection to routed fix; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect captured sessions, SDK events, errors and user feedback, then follow this sequence: 1. Capture and play back user sessions. 2. Detect errors, broken flows and pain points automatically. 3. Score and categorize issue severity. 4. Attach replay snippets, steps and metadata to issues. Resolve uncertain cases with qualified reviewers, approve reviewer-approved issue reports linked to replay evidence, and measure confirmed issues per review hour and time from detection to routed fix against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One supported SDK version and one app surface; final severity and fix decisions remain with the product team. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve user privacy, source attribution, consent accuracy and usage permissions. Product owners approve substantive issue routing and external actions. One supported SDK version and one app surface; final severity and fix decisions remain with the product team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One supported SDK version and one app surface; final severity and fix decisions remain with the product team. Implement one approved input format, a bounded representative case set and the first two task modules: capture and play back user sessions; detect errors, broken flows and pain points automatically. Support the third module with operator review: score and categorize issue severity. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Customer-owned SDK capture, authorized session data and permitted feedback sources. Cloud storage, issue trackers, pull request systems and messaging platforms. Start with file exchange and validate destination specifications before promising direct issue creation. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Session capture and consent settings, Issue review queue, Evidence-backed report and delivery. Use a thumbnail gallery for sessions and issues, a large central replay player with timeline, and a right-hand panel for detected issues, severity, steps and comments. Let users compare flagged moments side by side. Display detected, confirmed, dismissed and routed states. Provide a shareable report link with comments anchored to the relevant replay moment. Make the task-specific outcome reviewer-approved issue reports linked to replay evidence visible beside its evidence, review state and value baseline.
