AI-powered testing support that raises your developers' testing skills
No dedicated QA? Developers also doing the testing? arQua:Intellity uses AI to lift your developers' testing ability — so the whole team can raise quality together, and stand on its own within a year.
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Does any of this sound familiar?
Your team has no dedicated QA — developers handle testing too. But are you uneasy about its quality and efficiency?
- Developers never get the chance to build test design and implementation skills
- Testing tends to be ad-hoc — "let's just run it and see"
- The impact of new features and fixes is unclear, so test scope stays vague
- Every feature adds testing effort, and you can't keep up
- You want to invest time in testing, but development takes priority and it's pushed back
arQua:Intellity uses AI to raise your developers' testing ability.
What is arQua:Intellity?
How AI changes testing
1. AI in test automation Test design: generate test-case proposals from specs. Impact analysis: set the right scope from the changes made. Test code: generate code from natural language.
2. AI in exploratory testing Charter creation: propose the purpose and focus areas of the exploration. Risk analysis: identify priority areas. Session logs: auto-generate reports from exploration results.
3. Test data generation Automatically generate variations of test data, including edge cases.
4. Failure analysis & refactoring Analyse error logs to help pinpoint causes, and suggest improvements to test code quality.
Built for organisations like these
The ideal fit
- Team makeup: developers only (no dedicated QA)
- Testing: developers also own testing
- Current pain: lack of testing skills, difficulty grasping impact, vague test scope
- Attitude to AI: keen to adopt it actively
- The goal: developers raise their testing ability and the whole team lifts quality
How the service is structured
Phase 1 — Foundation
1–2 months AI environment setup
Phase 2 — Expansion
3–6 months Test expansion & exploratory practice
Phase 3 — Self-Sufficiency
3–6 months Team-led operation
Goal: the team becomes self-sufficient in up to one year — developing and testing in partnership with AI, growing together.
Phase 1: Foundation
Phase 2: Expansion
Phase 3: Self-Sufficiency
Why choose arQua:Intellity?
vs. conventional test-automation support Their challenges: tool introduction only ・ automated tests only ・ external dependency continues.
vs. doing it in-house yourselves The challenges: lack of know-how ・ time lost to trial and error ・ hard to see results.
Our strengths ✓ Pioneers in applying AI ✓ Exploratory-testing expertise ✓ Impact analysis ✓ Self-sufficient in a year
Expected outcomes
- Better testing skills — developers learn testing skills while using AI.
- Systematic test design — move from experience-driven to systematically designed, with AI support.
- The right test scope — use AI to analyse impact and set an appropriate scope.
- Structured exploratory testing — from ad-hoc "just try it" to charter-based testing.
- Higher quality across the team — within a year, the whole team can raise quality.