AI-Powered Quality Engineering for Modern Software
Agentic AI reads your requirements and generates tests. Automation runs them at scale. A QA engineer verifies every flagged result before it ships.
A signal moves around the quality engineering loop: Understand → Risk Mapping → Test Strategy → Execute → AI-Assisted Quality Engineering → Human Verification → Report & Improve, and then back to Understand. Every AI-flagged result is checked by a QA engineer at Human Verification before it becomes a Quality Signal.
4536
TEST CASE AUTOMATED THIS QUARTER
97.6%
PRODUCTION DEFECT LEAKAGE PREVENTION
800+
BLOCKER BUGS INTERCEPTED THIS QUARTER
<2%
FALSE POSITIVE/FLAKY TEST RATE
Every AI decision, checked by a person
Agentic AI reads requirements, generates tests, and runs them across your stack — fast, repeatable, and tireless.
None of it ships without a second opinion. Every AI-flagged result passes through a QA engineer before it reaches you.
Every AI-flagged result is checked by a QA engineer before it reaches you.
Every type of testing, AI-accelerated where it counts
Browse by category. Each service pairs a human QA discipline with the AI tooling that speeds it up.
QA shaped around how your industry actually works.
Different products fail in different ways. Our quality engineering approach adapts to the workflows, integrations and risks that matter in your domain.
Start where you are — not where a sales deck says you should be
From a fully embedded team to a scoped engagement — pick the model that matches how ready you actually are.
Dedicated Team
A dedicated team works as an extension of your engineering organization, building familiarity with your product, codebase, and release rhythm over the course of the engagement.
Coverage-as-a-Service
Coverage-as-a-Service focuses on maintaining agreed testing coverage across your product — manual, automated, or both — without the overhead of a fully embedded team.
Scoped Quick-Start Sprint
A Quick-Start Sprint is a time-boxed engagement around a defined scope — useful for validating a release, auditing existing coverage, or getting started before committing to a longer engagement.
Estimate Your QA Engagement
Explore how different QA coverage and engagement models could fit your product — and understand the potential difference between building equivalent QA capacity in-house and working with a flexible QA partner.
- From fixed QA overhead to flexible quality capacity.
- Model the QA capacity you need — without assuming the full cost structure of an in-house team.
IN-HOUSE QA CAPACITY
- Recruiting
- Team overhead
- Fixed capacity
- Tooling
- Management
FLEXIBLE QA ENGAGEMENT
- On-demand capacity
- Flexible coverage
- Automation support
- Specialized expertise
- Scalable engagement
This is a conceptual comparison, not a financial claim.
Your inputs
Engineers currently involved in testing on your team.
For context only — does not currently change the estimate below.
How often you ship a release.
Approximate test cases or user flows you need covered each release.
How much of your current testing is automated.
Which engagement model you're exploring.
Results
No estimate yet
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Trusted across product teams
Ready to see how it runs on your product?
Start with a pilot — a real look at AI-accelerated, human-verified QA on your product, with no long-term commitment.
Social Media QA
Privacy and blocking, feeds, messaging and moderation flows tested across relationship states.
KEY AREAS