Selectric typewriter and approval stamp — AI drafts cases, testers keep sign-off
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AI-Enabled SQA for software teams

Put AI into quality without giving up human sign-off. Cases and evidence in your tools. Living automation. Shift-left. Computer-use when the UI can be driven.

THE PROBLEM

AI has increased the velocity of development. Without accelerating QA, quality becomes the bottleneck, or the risk.

QA teams are already stretched thin. Development is shipping faster with AI. If quality work stays in standalone documents and spreadsheets, held together by last-minute heroics, QA cannot keep pace with the release path.

Auditors and stakeholders care that cases link to requirements, that evidence exists, and that someone signed off. They do not care whether you used AI to get there. So we put AI into the QA workflow your team already runs: cases and evidence land in the tools you use (or in custom dashboards we build), living automation suites rerun on every release, and computer-use runs drive the browser and capture the proof. Your people still own judgment.

RECENT WORK

AI in the QA workflow, measured in production.

COMPLETED
Major open-source foundation
An AI test-generation utility built into the QA workflow the team already ran. Cases drafted from specs in the team's own style, reviewed and approved by testers.
TEST CREATION TIME
-95%
COVERAGE
2x
HEADCOUNT ADDED
0
SIGN-OFF
Testers
ACTIVE
Digital health company
Cases and evidence land in Jira and Zephyr. A living Playwright regression suite reruns every release. Computer-use runs drive the browser and write step / expected / actual tables and screenshots back to the ticket.
SYSTEM OF RECORD
Jira / Zephyr
AUTOMATION
Playwright
EVIDENCE
Computer-use runs
OUTCOME
What took days now takes minutes of review
THE WORKFLOW

How a test case gets made.

The AI does not live in a chat window. It runs inside the workflow, and every step lands in your tools.

1
Input.
A requirement, spec, user story, or acceptance criteria, plus how your team already writes a case.
2
Draft.
AI drafts step-by-step cases in your team's format, linked to the requirement they cover.
3
Land.
Cases are created as drafts in your system of record (Jira, Zephyr, TestRail, Azure DevOps, qTest, or whatever you track work in). Traceability links are preserved.
4
Automate.
Where the case can be automated, AI generates or updates the script in your stack, and flags flakes and coverage gaps on every run.
5
Evidence.
Where the UI can be driven, a computer-use run executes the case and writes screenshots and a step / expected / actual table back to the ticket.
6
Sign-off.
A tester reviews, adds depth, accepts or rejects. Nothing enters the regression suite without a human approving it. The audit trail shows who.
SAMPLE ARTIFACT

A generated case, as it lands in the ticket.

ILLUSTRATIVE
TC-2417  Patient can reschedule an upcoming appointment
TRACES TO: REQ-311 (Appointment self-service)
PRECONDITIONS: Authenticated patient; one upcoming appointment exists

STEP  ACTION                                  EXPECTED                              ACTUAL
01    Open Appointments                       Upcoming list shows 1 appointment     ✓
02    Select appointment > Reschedule         Slot picker opens, current slot shown ✓
03    Choose an available slot, confirm       Confirmation with new date/time       ✓
04    Return to Appointments                  List shows updated slot only          ✓

EVIDENCE: 4 screenshots attached · run 2026-08-24 09:14 · playwright
STATUS: Draft → Reviewed by J. Tester → Approved

Drafted by AI from REQ-311. Executed by a computer-use run. Reviewed and approved by a tester. Every field lives in the ticket, not in a document.

STRAIGHT ANSWERS

What QA leads ask us first.

Does it hallucinate test cases?
Left alone, yes. That is why cases are drafted from your specs and your existing case style, land as drafts, and nothing enters the regression suite without a tester accepting it. The AI takes the first pass. Your people take the decision.
Where does our data go?
Wherever your policy says it can. We are model-agnostic. For regulated products or PHI, the same workflow runs on AWS Bedrock, Azure OpenAI, or local models inside your boundary. Specs and evidence never have to leave your environment.
Will this pass an audit?
The audit trail gets stronger, not weaker. Requirement to case to evidence to approver, all in the system of record with timestamps. Auditors see a chain of custody. They do not care that AI drafted the case; they care that someone signed off, and now that is recorded on every one.
Our automation is already flaky.
That is the usual starting point. Living automation means flakes get found and named on every run instead of discovered in the last hardening sprint. Stale tests get maintained by the AI, not abandoned.
What happens to our testers?
They stop writing packets. Drafting, evidence capture, traceability, and maintenance of stale tests move to the AI. Testers keep product knowledge, risk calls, exploratory depth, and sign-off. At the foundation, coverage doubled with no change in headcount.
YOUR STACK

We work in your stack. You own everything we build.

WE WORK WITH YOUR STACK
  • [·]Playwright
  • [·]Cypress
  • [·]Selenium
  • [·]Jest
  • [·]Vitest
  • [·]pytest
  • [·]JUnit
  • [·]TestNG
  • [·]Mocha
  • [·]Jasmine
  • [·]Robot Framework
  • [·]Appium

We do not bring a proprietary testing platform. Suites, scripts, prompts, and integrations are written in your tools and your repositories, and they are yours when we leave. No license to renew, no vendor to depend on.

FIT

Who this is for. And who it isn't.

A fit if:
  • Development is shipping faster than QA can keep up.
  • You have a test management tool and an automation stack, and the suite has gone stale.
  • Your product is regulated and evidence matters: healthcare, financial services, anything audited.
  • You want testers doing judgment work, not packet work.
Not a fit if:
  • There are no requirements, specs, or stories to draft cases from.
  • You are looking to remove human review and sign-off.
  • You want a chatbot for testers rather than AI inside the workflow.
PROVE IT FIRST

Send one sanitized spec. We'll send back cases in your format.

The first step costs nothing. Send one sanitized requirement, spec, or user story from a product you actually ship. We return drafted test cases in your team's format, plus one automated flow, so you can judge the work product before any conversation about scope. If it fits, the next steps are Blueprint (scope, architecture, sign-off model) and Build.

STEP 01
Prove. Free, on your spec
STEP 02
Blueprint. Scope and sign-off model
STEP 03
Build. In your tools, your repos
STEP 04
Enable. Testers own it

The person who built the foundation's test-generation utility scopes and runs every AI-enabled SQA engagement. Operator, not advisor.

RELATED SERVICES

This page is AI inside software QA.

If you are building AI systems and need to test those systems (evaluation harnesses, red-teaming, regression testing for non-deterministic outputs), see QA & Testing for AI Systems.

For the full QA practice behind this work (strategy, automation frameworks, performance testing, training), see Enterprise QA & Test Automation.

BOOK A TECHNICAL CONVERSATION

Tell us where quality breaks in your release path. We will scope an AI-enabled SQA path that fits your tools and your sign-off model.

contact@proticom.com
844.PROTICOM
proticom.ai
»   REAL AI · PRODUCTION GRADE · NO HYPE