AI QA engineer
Jina is an autonomous AI QA engineer that understands the impact of every code change on your system, learns from all past issues, and actually runs your app on PRs to catch and fix potential bugs.
Backed and built by a team from
Jina first builds a context layer
Jina starts with your code, infrastructure, and issue history. It builds a layered map of how those pieces connect so every decision has context.
Plans blast radius
On each PR, it traces impact paths through that map and picks the parts most likely to break.
Billing access gates and rollout modes
runningThis area controls when customers are allowed to create billable work and how `off`, `shadow`, and `on` modes change behavior. Jina chose it because rollout transitions can turn old shadow traffic into real backlog.
Outcome settlement and retry idempotency
queuedThis area owns the path from usage intake to final settled cost. Jina chose it because retry and idempotency bugs in billing can create duplicate charges, lost rows, or stuck usage.
Investigates with execution
Unlike static code review agents, Jina runs targeted probes and simulations against the system. It writes checks where needed and validates behavior in context.
Billing access gates and rollout modes
Task: Execute a shadow-to-on backlog probe
The changed billing path touches rollout mode and backlog drainage, so I need to test whether shadow-mode usage stays isolated after enforcement flips on.
billing mode writes -> pending backlog drain
seed shadow customer -> dispatch usage -> flip to on -> drain backlog
01const customer = await seedCustomer({ mode: 'shadow' })02await dispatchUsage(customer, { amount: 4200 })03await setBillingMode(customer, 'on')04expect(await drainBacklog(customer)).toBe(0)Let me note this issue: shadow-mode usage becomes billable after rollout flips on.
Reviews and gives a merge score
Finally, it reviews everything it found, removes false positives, and groups real issues. You get a concise report plus a merge score before deciding to merge.
Shadow-mode runs become real Autumn charges after enforcement flips to on
Missing SECRETS_ENCRYPTION_KEY stores provider secrets in plaintext
Runtime usage persistence failures can turn a completed review into a failed run
Out-of-range tenant billing policies can produce incorrect credit charges
OpenRouter cost literals are not accepted or preserved consistently end to end
Review that understands the system, not just the diff.
Understands every PR
Jina reads each change against your code, infrastructure, and issue history before it decides what to inspect.
Plans from blast radius
The context layer shows where a change can spread, so Jina targets the areas most likely to break.
Runs your app on PRs
Jina executes targeted probes and simulations against the relevant parts of your app instead of only reading files.
Finds the issues that matter
It filters low-impact noise, groups related failures, and keeps the report focused on bugs worth acting on.
Explains root cause
Each issue comes with the path that led to it, so engineers can see what changed and why it matters.
Scores merge risk
After review, Jina gives the PR a merge score so teams can make the decision with clear signal.
Enterprise-grade security
Jina is built for teams whose product is the business. Hermetic by default, least-privilege end-to-end, and ready for the controls your security team will ask about.
Hermetic execution
Every probe runs inside an ephemeral, isolated sandbox. Code, traffic, and traces are scoped to the run and torn down on completion.
Your data, your perimeter
Application data never leaves your infrastructure. Bring-your-own-cloud and private VPC deployment are first-class.
Least-privilege by design
Jina holds scoped, revocable credentials. It only touches the surfaces it's been explicitly granted, audited end-to-end.
Compliance-ready
Built to meet SOC 2 and the controls regulated industries expect, with a full audit trail behind every review and fix.
Frequently asked questions
Everything you would want to know before letting an AI QA engineer review every PR.
Jina is an AI QA engineer for pull requests. It maps your code, infrastructure, and issue history, understands the impact of each change, runs your app to investigate risky paths, then returns a concise issue list with a merge score.
It is a map of how your codebase, infrastructure, runtime behavior, and past issues connect. With that context, Jina can reason about blast radius instead of treating every PR like a fresh static review.
When a PR opens, Jina traces impact paths through the context layer. It looks at what changed, what depends on it, where similar changes caused issues before, and which real flows touch the affected surfaces.
Jina executes targeted probes and simulations in isolated environments against staging or preview deployments. The goal is to validate behavior in context, not just comment on code patterns.
The merge score is Jina's final risk signal for a PR after investigation and self-review. It accounts for the blast radius, evidence from probes, issue severity, and confidence in the findings.
Synthetic users are prompt-driven E2E tests you author and maintain. This product works at the PR review layer: it decides where a change is risky, runs targeted checks, filters false positives, and reports the issues that matter.
No. Jina can read production signals through the integrations you grant it, but probes run in isolated sandboxes against staging or preview deployments. You can also deploy Jina entirely inside your own VPC if your security team prefers.
Application data never leaves your infrastructure. Jina holds scoped, revocable credentials and only touches the surfaces you've explicitly granted. Bring-your-own-cloud and private VPC deployments are supported, and every action Jina takes is auditable end-to-end.
Web applications today — any framework, any backend, any cloud. Native mobile and desktop are on the roadmap. If your stack runs in CI, Jina can plug in.