Your context changes. The need for confidence stays.
Quality Engineering shaped around your product, your team and the next decision you need to make.
You built the product. Now find out whether it is ready.
A working demo is a starting point. Your first customers will bring real data, unexpected inputs and workflows you have not tried.
QA SONIC independently tests critical workflows, edge cases, integrations and release risks, then tells you what matters most before launch.
An independent quality assessment, prioritized fixes and clear release observations.
Get a Product Quality AssessmentKeep quality moving as release frequency increases.
Every new feature adds regression exposure. Repeating the same manual checks can slow an otherwise productive engineering team.
Add risk-driven regression, automation, release testing and specialist Quality Engineering without slowing the delivery process.
Repeatable coverage, earlier quality feedback and evidence that supports each release.
Improve Your Release ProcessBuilt quickly. Ready for real users?
AI can help build software quickly. It cannot guarantee the result is ready for real users.
We test AI-assisted applications independently across functionality, integrations, data behavior, edge cases, usability and regression risk.
Clarity on critical workflows, coverage gaps and the issues to address before launch.
Test an AI-Built ProductYour delivery. Our Quality Engineering.
Client projects need reliable QA, but building a permanent internal department may not fit your current pipeline.
Use QA SONIC as a white-label or embedded Quality Engineering partner for client projects, automation and release testing.
QA delivery under an agreed operating model, with consistent reporting and flexible capacity.
Discuss White-Label QARevenue-critical workflows deserve specialist testing.
A failed payment, duplicate charge or broken refund can become a customer and operations problem.
We validate checkout, billing, subscriptions, refunds, gateway behavior, webhooks and failure scenarios across the transaction lifecycle.
Transaction coverage, gateway-specific evidence and prioritized findings around revenue risk.
Review a Payment FlowEvaluate AI behavior beyond the demo.
A handful of successful prompts cannot establish relevance, consistency or reliability across a changing AI feature.
Create repeatable evaluation sets, assess relevance, hallucination, consistency and regression, then add human review where automated confidence is insufficient.
Repeatable evaluation data, human-reviewed findings and visibility into AI regression risk.
Discuss AI Quality EvaluationQuality should move with development.
Your engineering team should not have to slow down just to become confident in what it ships.