Decipher
AI-powered E2E testing platform that auto-generates and maintains tests and catches production bugs.
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What is Decipher?
Decipher is an AI-powered QA and automated testing platform designed for the agentic era. It uses vision language models and real-time agents to watch every customer session, automatically generating end-to-end (E2E) tests that learn your product and maintain themselves as you ship. The platform detects, diagnoses, and helps ship fixes for anything that blocks user happiness—from silent drop-offs to full-blown errors, alerting teams when a bug hits production.
Key features include self-healing tests that automatically update when UI changes occur, eliminating the need to chase selectors and fix flaky tests. Users can generate reliable E2E coverage in minutes by simply describing a test to Claude Code or showing Decipher using their Chrome extension. The platform monitors production issues by correlating on-screen behavior with technical signals like failed requests, console errors, UI dead ends, form loops, and rage clicks, then creates focused alerts with customer impact data and one-click reproduction via exact session moments.
Decipher integrates seamlessly with tools teams already use, including Github, Sentry, Linear, Asana, and AI coding agents like Claude Code and Cursor. It can spin up pre-filled Linear issues with steps, session links, and customer impact, and even hands structured context to AI coding tools to draft pull requests and propose fixes. The platform is enterprise-ready with SOC2 Type II audited security, AES-256 encryption, client-side PII masking, and SAML/SSO support.
Decipher is designed for engineering teams, QA engineers, product teams, and startups who want comprehensive QA and E2E tests without slowing down their development velocity. It's trusted by world-class engineering teams and large enterprises who need to ship fast without bugs. The platform helps teams answer critical questions like how to reproduce bugs, which bugs to fix first, what caused error spikes, and how many people are hitting specific errors.
The platform offers automatic issue collections that group similar issues together, providing zero duplicate hunting, clear impact metrics showing affected users and key accounts, faster action with one-click ticket creation, and less noise as collections auto-merge. Teams can protect revenue paths by prioritizing incidents based on customer impact and critical flows, creating tests in one click to prevent regressions.
Decipher pricing
Pricing model: Freemium
Pro plan: $250/month with 14-day free trial, includes unlimited test generations, 1,250 test runs (+$0.20/run additional), 5,000 monitored user sessions (+$0.05/session additional), Linear and coding agent integration, agent learning and self-healing, 30-day data retention. Enterprise plan: Custom pricing with everything in Pro plus SAML/SSO, dedicated support, and customizable retention. No free tier mentioned beyond the 14-day trial.
Decipher pros
- Auto-updates test steps when UI changes occur
- Instant E2E test generation from prompts or recordings
- Vision-plus-DOM validation ensures tests match intent
- Managed infrastructure eliminates browser/CI management
- Self-healing tests reduce flaky test triage
- Real-time production bug monitoring with alerts
- Session replays include network calls and logs
- AI-assisted resolution drafts pull requests automatically
- Pre-filled Linear tickets with full context
- Automatic issue collections group similar bugs
- Prioritizes incidents by customer impact
- Chrome extension for quick test creation
- Claude Code integration for agentic workflows
- SOC2 Type II audited security
- Client-side PII masking protects sensitive data
- AES-256 encryption at rest and in-transit
- SAML/SSO support for enterprise access
- 30-day data retention on Pro plan
- Smart caching makes test runs faster
- Protects critical revenue paths and flows
Decipher cons
- No native iOS and Android support yet
- Mobile support roadmap for later this year only
- Pro plan at $250/month may be expensive for small teams
- Additional test runs cost $0.20/run beyond 1,250
- Additional monitored sessions cost $0.05/session beyond 5,000
- 30-day data retention may be insufficient for some enterprises
- Enterprise plan requires custom pricing contact
- Limited to web applications currently
- AI-generated tests still need human validation for complex scenarios
- Chrome extension required for visual test creation
Frequently asked questions about Decipher
Why not just have Claude Code generate Playwright?
Playwright has major limitations. AI-generated Playwright tests are often unreliable because they guess at selectors and assertions from code context alone without verifying clicks worked. Decipher uses vision plus DOM to validate each step truly worked. With Playwright, you own infrastructure managing browsers, CI runners, parallelization, environments, and retries yourself, while Decipher runs on managed infrastructure. Playwright tests are static and break with UI changes, but Decipher continuously updates tests as your product evolves. When failures happen, Playwright gives stack traces and screenshots, while Decipher explains reasons in plain language. Visual assertions in Playwright are painful or impossible, but Decipher evaluates UI visually and semantically.
Are the tests that Decipher generates flaky?
Nope! The Decipher agent automatically heals tests as selectors or steps become outdated. You'll be notified about meaningful adjustments, but otherwise you can just worry about shipping without babysitting locators or triaging flake.
Does Decipher work on mobile?
Native iOS and Android support is not available yet but is on their roadmap for later this year. Currently the platform is limited to web applications.
How does production issue monitoring work?
Just install the SDK and the agent watches sessions correlating on-screen behavior with technical signals like failed requests, console errors, UI dead ends, form loops, rage clicks, and odd state transitions. When it detects a true failure, it creates a focused alert with impact showing who and which accounts were affected, one-click repro via the exact session moment, and an optional ticket plus guardrail test to prevent regressions.
What integrations does Decipher support?
Decipher plugs-and-plays with Github, Sentry, and existing test suites. You can easily run tests in CI/CD, sync issues in Linear and Asana, get real-time alerts sent to your platform of choice, and connect to favorite coding agents like Claude Code and Cursor so they can jump right to a fix.
How secure is Decipher for enterprise use?
Decipher is designed from the ground up for largest enterprises with security to custom integrations. All data uses industry-standard AES-256 encryption at rest and in-transit, client-side PII masking before data reaches servers, SOC2 Type II audited security controls and processes, and SAML/SSO support for enterprise access controls.
What is automatic issue collections?
Decipher groups similar issues the agent discovers together into collections providing zero duplicate hunting since repeated bugs and UX snags roll up automatically, clear impact showing affected users key accounts and trends, faster action with one click to open exact session moments or spin up pre-filled Linear tickets, and less noise as collections auto-merge when patterns evolve so signal stays clean.
How fast can I get started with Decipher?
You can try it for free in less than 5 minutes. For production use on a Next.js app, you can create a free account and instrument your application in under 2 minutes following their documentation instructions.
What questions does Decipher help teams answer?
Decipher helps answer how to reproduce bugs, whether tests represent real users, if issues affect key accounts, what caused sudden error spikes, why conversion rates dropped, which bugs should be fixed first, what test cases to add for new features, and how many people are hitting specific errors.
How does smart caching work in Decipher?
Tests run on cached elements and AI only executes steps when behavior changes, making test runs faster without redundant execution of unchanged test steps.