AI-Augmented QA
for teams that ship

We combine QA strategy, automation expertise, and governed AI workflows so your product team can move faster without losing control of quality.

QA that finds the real risk.

We help product and engineering teams turn quality
from a bottleneck into a managed delivery system.

Discuss Your QA Gaps

AI does not replace good QA judgment. It amplifies it.

AI Scenario Mining

Find the edge cases your team misses
when releases move too fast.

Human-Governed Test Design

Get AI speed without letting AI decide
what your team should trust.

AI Regression Prioritization

Run the tests most likely to catch
the release-breaking change first.

AI QA Workflow Installation

Turn scattered AI prompts into a repeatable
QA operating rhythm.

Smarter AI-assisted delivery system

A practical way to design, automate, and govern QA with AI.

  • 04 connected layers
  • Human-governed
  • One release signal
  1. AI scenario intelligence

    Surface edge cases, negative paths, missed acceptance checks, and regression candidates from requirements, stories, tickets, defects, and product flows.

  2. Human-governed test assets

    Use AI to draft scenarios, checks, and automation ideas faster, then have senior QA review what is accurate, useful, and release-worthy.

  3. AI-assisted regression focus

    Prioritize checks based on critical flows, code changes, defect history, business impact, and automation signal so the highest-risk areas get tested first.

  4. Release signal governance

    Turn AI-assisted output into trusted QA routines: review gates, ownership rules, readiness checks, defect enrichment, reporting, and go/no-go visibility.

QA operating model

AI-Augmented QA that creates release signal, not test noise.

A focused delivery layer for using AI inside QA without handing quality decisions to AI. We help your team generate better scenarios, prioritize smarter regression, enrich defects faster, and keep every release-relevant output under senior QA review.

14-Day AI-Augmented QA Pilot Pass October, 2026 1 pass left Book a Fit Call for AI-Augmented QA
AI-Augmented QA Model

More release signal. Without more QA headcount.

Traditional QA runs out of leverage. AI-Augmented QA helps a smaller team cover more surface area, faster, with engineers still owning judgment.

Diagram showing a human-governed AI QA workflow: the team scopes product intent, code and system changes, and defect history; AI maps affected journeys, failure modes, and candidate checks; QA verifies sources, challenges assumptions, and owns the test order; the result is a source-linked impact map, risk order, and test direction.
About the service

AI-Augmented QA that multiplies expert QA judgment.

This service is built for teams that need faster QA output without lowering the trust bar. AI accelerates the work. Senior QA governs what is real, useful, and release-worthy.

01

Prioritize the flows where quality failure has real business cost.

02

Use AI to accelerate scenario and test thinking, not to replace judgment.

03

Convert QA output into clear, repeatable release decisions.

We begin by mapping where your product can fail in ways users actually feel: critical journeys, recent defect patterns, high-change areas, and integration-heavy paths.

Then we focus AI-assisted effort on those hotspots so your team gets useful coverage faster, not a giant backlog of low-value tests.

Risk-first planning outputs
  • Critical user-flow risk map
  • Priority scenario backlog
  • Release-impact coverage targets

AI-Augmented QA

Critical Flow Mapping

Release Risk Map

Automation Assets

AI Release Readiness

Human-Governed AI

No Vendor Lock-In

Quality Operating System

Before you bring us in

The objections smart teams should ask first.

You want more release confidence without hiring a bigger QA team, buying tool theater, or creating a process engineers hate. Here is how we keep the work useful, practical, and owned by your team.

No magic tricksProof before processBuilt for engineersSignal in weeksYour stack stays yours

Not with the way we use it. AI helps draft ideas, edge cases, and candidate checks, but every workflow keeps human review, product context, and maintainability rules in place. We optimize for useful signal, not a bigger pile of scripts.

Horia Adamov reviewing software release evidence at a workstation
Case study

Enterprise IaC platform · Identity protected

From manual-heavy regression to a dependable release signal.

An enterprise IaC platform needed to keep pace with rapid product expansion. A three-person AQA Masters team established QA ownership, built reusable UI and API automation, and connected the maintained suite to clearer CI evidence.

3.2×
core automation growth
~700
documented QA scenarios
100%
maintained suite enabled for parallel CI
of the agreed workflow regression suite automated
Read the case study
Ready to strengthen your QA?

Book a call and find the fastest path to better releases.

Tell us where testing feels slow, risky, or unclear. We’ll help you identify the first QA improvements worth making for your product.

NDA before access Least-privilege scope Every asset stays yours No long-term lock-in
Horia Adamov, QA Architect
Your call host

Horia Adamov

QA Architect