Session replay issue detection workspace

For

Product and engineering teams running web or mobile apps with session replay data

Solves

User experience issues and bugs sit unseen in session replays because manual review does not scale and findings never reach the team workflow.

Delivers

Reviewer-approved issue reports linked to replay evidence

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce manual replay review while routing evidence-backed issues to the people who fix them.

1. Capture and play back user sessions.
2. Detect errors, broken flows and pain points automatically.
3. Start analysis without event tagging or instrumentation.
4. Score and categorize issue severity.
5. Attach replay snippets, steps and metadata to issues.
6. Summarize recurring patterns in weekly reports.
7. Query session data in plain language.
8. Generate AI summaries of behavior and opportunities.
9. Produce experience reports that link to replays.
10. Deliver instant analysis for immediate adjustment.
11. Collect events, errors and replays through an SDK.
12. Run background agents that email customers, create issues and open pull requests.
13. Check agent action outcomes and feed improvements back.
14. Send updates through Slack, SMS and WhatsApp.
15. Combine replays, heatmaps, surveys and feedback.
16. Observe live interactions and emotional responses.
17. Keep the SDK lightweight and avoid PII and cookies.
18. Launch targeted in-platform research studies.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned reviewer-approved issue report linked to replay evidence with source references and unresolved questions.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Captured sessions, SDK events, errors and user feedback

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect captured sessions

4. 3

   SDK events

5. 4

   Errors and user feedback

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved issue reports linked to replay evidence

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, session versions, team comments, approval states, usage allowances, review limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: capture and play back user sessions; detect errors, broken flows and pain points automatically. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will product and engineering teams running web or mobile apps with session replay data use it to solve "user experience issues and bugs sit unseen in session replays because manual review does not scale and findings never reach the team workflow"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Confirmed issues per review hour and time from detection to routed fix.
4. Measure, then decide. Track confirmed issues per review hour and time from detection to routed fix; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved issue reports linked to replay evidence. Retain the explicit scope boundary: One supported SDK version and one app surface; final severity and fix decisions remain with the product team.

What the build depends on. Session upload and preview, asynchronous detection jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist product QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One supported SDK version and one app surface; final severity and fix decisions remain with the product team.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined app surface. Offer a monthly production allowance after repeat demand. Quote complex multi-app or enterprise integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved issue report linked to replay evidence. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce manual replay review while routing evidence-backed issues to the people who fix them. Demonstrate a concrete reviewer-approved issue report linked to replay evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and engineering teams running web or mobile apps with session replay data professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved issue report linked to replay evidence from a small authorized input set, with a transparent calculation of confirmed issues per review hour and time from detection to routed fix and no promised savings.

The first 30 days

1. Week 1: interview five product and engineering teams running web or mobile apps with session replay data and inspect a recent example of user experience issues and bugs sitting unseen in session replays.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure confirmed issues per review hour and time from detection to routed fix, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Confirmed issues per review hour and time from detection to routed fix. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Confirmed issues per review hour and time from detection to routed fix; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved issue reports linked to replay evidence. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved issue patterns, severity rules and review examples, together with reliable delivery for a narrow product niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product and engineering teams running web or mobile apps with session replay data. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Prism AI, Lucent, Stey, Stey.ai, Human Behavior and Sprig 2.0 AI Product Experience Platform, plus manual replay review and generic analytics tools. Compare this product with the buyer's present method on confirmed issues per review hour and time from detection to routed fix. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Session storage, replay processing, model calls, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved issue reports linked to replay evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.