UI automation carries the load
Slow, flaky end-to-end suites catch bugs last and break on every UI change. QA time goes to maintenance, not quality.
everylayer · the Proof Gate for AI-written code
Build at AI speed. Prove every layer.
AI writes the code now. Whether you can trust it is decided by your tests, and most test suites were losing even before AI made it worse.
everylayer is our agent-powered test pyramid platform, and the Proof Gate for the code your agents write. It scores test health across all seven layers of your frontend and backend repos, generates the missing tests as draft pull requests in your repo, and keeps every AI-written change proven before it ships: rapid iterations and scale, with proof. It is the same evidence discipline we run on the work itself.
The problem
AI slop is deferred debugging, with interest.
Generated code compiles, demos well and reads clean. None of that proves it handles the edge case, the concurrency, or the contract with the service next door. Unproven merges surface in staging and production, the two most expensive places to find them.
Your team writes more of it every week, across a shifting mix of coding agents and models (Artificial Analysis now benchmarks the combinations, harness and model both). everylayer is the layer above all of them: whatever writes the code, we prove it before it merges.
Coverage % lies
High line coverage says nothing about whether tests actually catch bugs. Green dashboards, escaping defects.
Frontend and backend break each other
Without contract tests, every schema change is an integration surprise waiting in a shared staging environment.
Manual cycles gate every release
Days of hand-run regression before every ship, and AI-speed development makes the validation backlog grow faster.
90%
of developers now use AI at work, and DORA 2025 finds that adoption raises delivery instability unless strong automated testing and fast feedback are in place.
Google DORA, 2025
8x
more duplicated code blocks in 2024 as AI adoption rose, while refactoring fell by half. Cloned code carries 15 to 50% more defects.
GitClear, 2025
95%
of enterprise GenAI pilots deliver no measurable P&L, blamed on integration and missing controls, not model quality.
MIT, State of AI in Business 2025
~2x
more often, AI reaches production when bought or partnered (67%) than built in-house (33%).
MIT NANDA, 2025
The model
Seven layers. One health score.
Every layer earns a 0 to 100 health score from open-source tooling, and the shape of your suite is scored against the ideal pyramid. Those roll up into one composite score across four dimensions, weights configurable and the trend reported weekly, so it becomes the quality KPI your team steers by.
E2E UICritical user journeys onlyjourney coverage
IntegrationReal services and databases togetherAPI coverage %
MutationDo tests catch injected bugs?mutation score %
Frontend contractConsumer expectations of the APIcontract coverage %
Backend contractProvider honours its API contractsendpoint coverage %
ComponentModules with dependencies mockedcomponent coverage %
UnitLogic in isolationline & branch coverage %
Pyramid health Coverage % per layer, mutation score and contract coverage, plus the concentration and gap indexes the agent maintains.
Delivery health Feedback time per layer, selection accuracy against the nightly full run, flake rate and quarantine.
NFR health Core Web Vitals budget compliance, endpoint SLO fit, open WCAG violations and security findings by severity.
Production health Journey pass rate, live SLO compliance, real-user field vitals and error-budget burn.
100% open-source toolchain: best-in-class open-source tools at every layer, functional and non-functional, picked per stack. Nothing proprietary ever lands in your codebase. The full toolchain is below.
Coverage
Web, mobile, services, and the AI inside them.
If it has a repo and a way to assert behaviour, it fits the pyramid.
Web apps
React, Next and Vue frontends; Node, Spring and FastAPI backends. Launch adapters cover JS/TS, Java/Spring and Python, unit to E2E with Playwright on top.
Mobile apps
React Native, Flutter, Swift and Kotlin. The layers hold; only the tooling changes at the top (Maestro, Appium, Detox). Adapter next on the roadmap.
APIs & services
REST, GraphQL and gRPC, sync or async. Event-driven services get message contracts too: Pact message pacts for Kafka, RabbitMQ and SQS, plus Avro/Protobuf schema-compatibility gates. The pyramid covers every conversation your services have.
AI features & agents
LLM outputs scored with open-source, self-hostable eval suites (DeepEval, Ragas, promptfoo) plus Langfuse for online evals: golden datasets, prompt regression, and tool-call trajectory checks for agents. Scored beside the pyramid, like delivery health.
Beyond functional
Correct is not enough. Prove it is fast, safe and usable.
The seven layers prove behaviour. These three gates cut across all of them and run shift-left on the same pull request: cheap checks on every change, heavier scans nightly. All open-source, all mapped to the code that changed.
Performance gate
Client and server, shift-left. Lighthouse CI budgets and Core Web Vitals on changed routes; k6, JMeter or Locust smoke thresholds on touched APIs. A bundle bloat or a p95 regression turns the gate amber in the pull request, not in production.
Security gate
OWASP-aligned and 100% open-source. OWASP ZAP baseline DAST on preview envs, CodeQL and Semgrep SAST, Trivy for dependency and container CVEs, Gitleaks for secrets, mapped to OWASP ASVS controls on the changed code.
Accessibility gate
WCAG 2.2 on UI changes. axe-core inside component and journey tests, Lighthouse and Pa11y on changed pages, plus a keyboard and screen-reader checklist for what automation cannot prove. A new serious violation blocks the merge.
The toolchain
Every layer, one open-source toolchain.
Best-in-class open-source tools at every layer, functional through non-functional, picked per stack and run on your CI. Nothing proprietary ever lands in your codebase.
Functional · the seven layers
UnitJest, Vitest, JUnit 5, pytest, go test
ComponentTesting Library, Vitest, Spring slice tests, pytest
Backend contractPact (provider), Specmatic, Spring Cloud Contract
Frontend contractPact (consumer), Specmatic, MSW
IntegrationTestcontainers, WireMock, REST Assured, Schemathesis
MutationStryker (JS/TS), PIT (Java), mutmut / cosmic-ray (Python)
E2E & UIPlaywright, Cypress; mobile: Maestro, Appium, Detox
Performance · shift-left, client and server
Client & Core Web VitalsLighthouse CI, web-vitals, Unlighthouse, sitespeed.io, WebPageTest
Load & stressk6, JMeter, Gatling, Locust, Artillery
Security · OWASP-aligned, 100% open-source
SAST · codeSemgrep, CodeQL, Bandit (Python), gosec (Go)
DAST · running appOWASP ZAP, Nuclei
Dependencies & SCATrivy, Grype, OWASP Dependency-Check, osv-scanner
SecretsGitleaks, TruffleHog
Containers & IaCTrivy, Checkov, tfsec
Accessibility · WCAG 2.2
Automated checksaxe-core, Pa11y, Lighthouse a11y
AI & LLM evals · scored beside the pyramid
Output & agent trajectoryDeepEval, Ragas, promptfoo, Langfuse (online evals)
The impact agent
It starts with a map, not a run.
The impact agent keeps a live map of your codebase: per-test coverage traces, the call graph and git churn, rebuilt on every merge, linking each line of code to the tests that guard it. Every pull request then runs only what the change reaches, direct hits plus one dependency hop, while the nightly full suite stays the safety net that proves the selection was right.
01Pull request openedFour files changed, a normal diff.
02Impact map consultedThe diff is matched to the tests whose traces touch it: direct hits plus one dependency hop.
0368 of 1,400 tests runAbout eight minutes in parallel, not the full-suite hour.
04Verdict postedGreen or not, with any gap flags attached, before the merge.
The same map surfaces the two things most suites are blind to:
Concentration
Where the suite is overweight. Clusters of tests re-prove the same behaviour, and logic gets proven through slow end-to-end journeys when a unit or contract test would prove it cheaper. In one suite, 412 of 900 tests re-proved just 38 behaviours, burning 26 minutes of every run on duplicates.
Gaps
Where nothing is watching. Impacted-but-untested code, orphan endpoints with no contract, and uncovered branches in files that churn every week. Every gap is ranked by churn, complexity and blast radius, so the backlog burns down worst-first.
Example gap flags
PaymentService.refund()Churned 9 times this quarter, zero tests.POST /orders/{id}/cancelNo contract, no integration test.checkout.tsx41% branch coverage, top-3 churn in the repo.The gates ride the same map. The performance, security and accessibility checks above are scoped to only the pages, endpoints and routes the change touches, so all of them fit inside one sub-15-minute pull-request verdict instead of a quarterly audit.
How it works
One platform, three steps.
Health check
Connect read-only. Our agents classify every existing test into the seven layers and report coverage % and health per layer: a full gap report in days, not months.
Complete the gaps
Agents generate the missing tests bottom-up and land them as draft pull requests in your repo: mutation-validated, reviewed by your team, run by your CI.
Co-develop
On every pull request we check that new code ships the right tests at the right layer, so the pyramid stays green while you build at full speed.
Start small, prove value, stay embedded. Each step stands on its own.
Across the lifecycle
Beyond the pull request.
The tests you built and the map that scopes them keep working after merge. everylayer carries the same evidence into release and into production, so the shape stays true where it actually costs you.
Go/no-go from evidence
can-i-deploy asks the contract broker whether this version is compatible with every live consumer and provider. A release-profile k6 load test runs the real traffic shape against staging with latency SLOs asserted, and the smoke journeys run once more against the production build and config. Rollout is canary and blue-green aware: a regression on the canary pauses it automatically.
Production is a test environment
The Playwright journeys that gate releases run against production around the clock, and lightweight k6 probes assert latency and error-rate SLOs on the live APIs. Real-user Core Web Vitals come back from the field (web-vitals plus sitespeed.io), and anomalies are clustered by root cause, so one incident is one finding with one owner, not forty alerts.
The feedback loop
Every production incident becomes a test.
This is what turns everylayer from a gate into a compounding system. A failure in production is not fixed and forgotten: it is traced, closed at the cheapest layer, and guarded, so the same class of failure cannot ship again.
01Production findingA journey fails or an SLO breaches, clustered to one root cause.
02Traced by the mapThe impact map links it to the code path, and shows no test guarded it.
03Gap openedAt the cheapest layer that could have caught it, ranked top of the backlog.
04Guard test writtenAn agent drafts a mutation-validated test as a pull request your team reviews.
05Cannot happen twiceThe class of failure is now caught pre-merge, and the net got tighter.
Usage re-weights risk. The journeys your users actually take earn the strongest guard tests, so testing effort follows real exposure instead of guesswork.
The rhythm
What runs when.
Not everything runs on every change. Fast, impact-scoped checks block the pull request; the heavy suites run on a schedule and guard the trend.
Pre-commitUnit tests on the touched files: seconds, on the developer’s machine.
Every pull requestImpact-scoped everything: affected tests, Lighthouse budgets, k6 smoke, axe-core and a ZAP baseline. A sub-15-minute verdict.
Merge to mainIntegration suite, mutation sampling on the changed modules, and contract verification.
NightlyFull regression, full k6 load profiles, a sitespeed.io crawl, a full accessibility crawl and active DAST.
Releasecan-i-deploy, release-profile load, and smoke journeys on the production config.
Fast gates block the pull request; heavy suites guard the trend. Nothing waits on a 45-minute pipeline.
The architecture
Agents in our cloud. Tests in your repo.
Everything flows through one auditable gate: a GitHub or GitLab App with scoped permissions. Our agents never live in your codebase, your code never leaves your control, and every test we write lands in your repo in frameworks you already know.
Stack-agnostic core. JS/TS (React, Vue, Angular), Java/Spring and Python at launch; .NET, Go, Rust and mobile next. Adding a stack means adapters, not new agents.
Pairing inferred. Frontend to backend pairing is inferred from routes and specs, confirmed once at onboarding.
Read-only first. Step 1 needs read-only access; pull-request write comes only in step 2, and your review is the gate.
Walk away anytime. Every test we wrote stays in your repo. No proprietary runtime, no lock-in.
How you run it
Install in 60 seconds. Or keep every byte in your VPC.
The GitHub App is thin: it authenticates, receives events, publishes checks and opens draft pull requests. Where the analysis actually runs is your choice, and the choice can change as trust grows.
Cloud
Install the GitHub App, get a read-only Evidence Snapshot back. No API keys, nothing changes in your repo. The fastest path to your first report; best for startups and pilots.
Local runner
The App stays thin; analysis runs in your own CI or VPC with your OpenAI, Anthropic or Azure keys. Your source never leaves your control. Best for serious SaaS and AI teams.
Self-hosted
The whole control plane runs inside your network against an allowlisted or local model. Strongest posture for banks, health and defense; the procurement unblocker.
Quality enforcement
How we kill AI slop.
Assistants generate the code. Our agents generate the evidence, and gate the merge on it.
Mutation-validated tests
Every generated test has to kill injected bugs before we ship it. Coverage theater does not survive mutation testing.
Pyramid budget
A pull request that adds E2E tests for logic that belongs in a unit or contract test gets flagged before it merges. The shape cannot silently invert again.
Contract watchdog
A backend schema change that would break the frontend is flagged in the pull request, not discovered in staging.
Flake SLO
Generated tests must be deterministic, readable, and survive 30 days on main without flaking before a layer counts as green.
Why everylayer
Every tool picks a layer. We score the whole pyramid.
The 2026 landscape is crowded at single layers: AI unit-test generators, AI browser testing, contract platforms. None of them models the pyramid, none validates its own output with mutation testing, and none spans frontend and backend.
layerthe point tools, 2026everylayer
E2E UIMabl, Testim (Tricentis), Functionize, testRigor, QA Wolf, Momentic, many more
✓
IntegrationKeploy (traffic record and replay), Tusk, Postman Postbot
✓
MutationStryker, PIT, mutmut; but mutation-validated AI test generation in PR workflows is rare
✓
Frontend contractPactFlow AI (SmartBear), Specmatic
✓
Backend contractPactFlow AI (SmartBear), Specmatic
✓
ComponentMeticulous (session replay); nobody generates component tests in your repo
✓
UnitQodo, Diffblue (Java), Tusk, generic coding copilots
✓
Plus measure-only dashboards (Codecov, SonarQube): coverage numbers, no test generation, no shape score. The widest rivals span two layers.
Where it fits
Greenfield, brownfield, or your clients’ field.
Coverage from commit one
Stand the pyramid up before habits form: CI gates from day one, no manual validation cycle ever exists, and vibe-coding at full speed with the guardrails already on.
Untangle the inverted pyramid
Health check first, then gaps closed bottom-up in your repo. Manual regression shrinks from days to minutes, and legacy gets a safety net before you modernize it.
A new offer for every engagement
Open doors at existing clients with the read-only gap report, win new test-automation work on outcomes, and deliver with our agents behind your team.
Working together
A low-risk path to a healthy pyramid.
Each phase earns the next. No long-term commitment upfront.
01 · Health check
Read-only access. Full gap report with per-layer scores and coverage %. Valuable on its own, even if we stop here.
02 · Gap completion
You pay for layers brought to green, verified by mutation scores and your own CI, delivered as draft pull requests your team reviews.
03 · Co-development
Pull-request checks, pyramid budget, contract watchdog, weekly health digests. Cancel anytime; every test stays in your repo.
Start with an Evidence Snapshot.
Read-only access, nothing changes in your repo. You get a gap report: what your tests actually prove, where they are blind, and what to fix first. From there, everylayer runs as the Proof Gate on every pull request, and the same discipline can run the work around it.