Clariva QA embeds in your engineering cycle to build the quality and governance infrastructure fast-moving startups need — from test coverage to AI risk, priced for teams who don't have a QA hire yet.
We don't hand you a report and disappear. We embed in your engineering cycle — starting with strategy, building automation and assurance, and staying on to make sure it holds.
A structured assessment of your quality maturity and your AI exposure — coverage gaps, automation readiness, and where AI is making decisions in your product that a regulator, auditor, or customer could challenge.
We design and build your test automation framework, integrated into your CI/CD pipeline — and where your product ships AI features, we build adversarial test suites alongside it, in the same pipeline.
Ongoing embedded oversight — monthly pipeline health, failure triage, and a recurring pass on AI behavior so drift, new bias exposure, or new AI features never ship un-audited.
Senior QA leadership — six years in production-scale quality engineering — priced for teams who don't have a QA hire yet, not for teams who already have a budget line for one.
A focused, one-week look at where your product is most likely to break — critical paths, untested edge cases, and (if you're shipping AI features) where model behavior hasn't been stress-tested. You get a prioritized report you can hand straight to your engineers. No retainer, no follow-up call required.
Regulators aren't waiting for AI features to mature before they ask how you tested them. If your product makes decisions — pricing, lending, fraud flags, support responses — someone will eventually ask you to prove it was audited. We'd rather that someone was us, three months ago.
Most QA firms hand you a report and leave. Most AI-audit firms have never shipped a regression suite. We stay embedded across both — attending sprint reviews, holding the full picture of your quality and governance posture.
English, French, Spanish, and Arabic — your team communicates in the language most natural to them, without losing technical precision, including on AI governance frameworks that read differently in each region.
Deep familiarity with regulated software environments — where AI risk and compliance risk are no longer separate conversations.
We come from test engineering, not from an AI-ethics whitepaper. We red-team your AI the way we've always regression-tested your code: rigorously, and built into your pipeline.
Four markets, each in its native language, business culture, and regulatory reality.
Fintech and SaaS companies in California, Texas, and across the US — deep familiarity with US engineering culture and NIST AI RMF expectations.
France, Belgium, Switzerland — full EU AI Act and GDPR-adjacent regulatory fluency, natively in French.
UAE, Saudi Arabia, Qatar — a rapidly growing tech and Fintech ecosystem, served natively in Arabic and English.
Mexico, Colombia, and the broader Spanish-speaking market — supporting engineering and compliance teams natively.
If yours isn't here, book the audit call and ask directly.
If you have paying users or a launch date on the calendar, it's usually the right time — the Risk Audit is a lower-commitment way to find out first, with no obligation to continue.
Yes. Plans are month-to-month with no contracts or minimum terms. Scale a seat up, down, or pause it entirely before your next billing date — no penalty, no negotiation required.
No — any startup shipping fast benefits from fractional QA. Fintech and companies with AI-driven features get extra value, since regulatory and AI-risk exposure is built into every plan from Growth up.
Standard international contractor setup — a signed statement of work, invoicing in USD, and payment via wire or Wise. You'll complete a simple W-8BEN form; no US tax withholding applies since the work is performed remotely.
That's a good problem. Most teams use a fractional seat exactly until they can justify a full-time QA hire — we'll tell you honestly when that point is closer than you think, and can help scope the role.
30 minutes, no pitch — just an honest look at where your quality and AI governance stand today, and what's next.