AI-assisted QA and software testing that run inside delivery
Netbase provides software testing and QA services for product and commerce teams to release often without breaking what customers use. QA runs inside delivery, not after it: acceptance criteria, automated regression with AI-generated test drafts, performance budgets and release readiness checks. Page speed shows the result: USticker's page load fell 21%, and PrintLeo's improved 35%.
Book a solution review View relevant workReviewed by David (CEO) · Updated 17 Sep 2026
Page load time fell 21% at USticker
Page load improved 35% at PrintLeo
Bounce rate fell 40% at PrintLeo
Completed designs rose 32% in six months
Tired of releases that break checkout? Book a solution review.
What quality engineering and testing includes
Testing at the end of a project finds problems when they are most expensive to fix. Quality engineering moves the work to the start: every user story gets acceptance criteria before code is written, every change runs through automated regression, and every page has a performance budget it must meet. The service belongs to our Quality & Security family.
Included
- Test strategy and acceptance criteria per feature
- Functional, integration and API testing
- Automated regression suites that run on every release
- Performance budgets, page-speed testing and load checks
- Cross-browser and mobile checks
- Vulnerability scanning in the delivery pipeline
- Release readiness reports and user acceptance testing support
Not included
- Certification audits of your product
- Legal or regulatory compliance sign-off
- Crowd-testing panels and physical device labs
- Penetration testing as a standalone service (see application security)
When to use it, and when another route fits
Quality engineering fits any product that changes often and earns money every day. It is less useful for software that is finished and rarely touched.
Good fit
- Releases regularly break checkout, search or login
- Pages are slow and nobody measures by how much
- Your developers test their own work with no second pair of eyes
- A product with integrations that fail silently
Another route fits better
- A one-off site that will not change after launch
- You need a formal audit certificate for your product
- You only need extra testers on a fixed script: a dedicated team fits
- The product is being replaced soon: plan the migration first
Every other route is listed in the services directory.
Outcomes and buyer jobs
Teams hire quality engineering to:
A regression suite proves the old features still work before the new ones go live.
Performance budgets turn speed from a one-off fix into a release rule.
Acceptance criteria written with the business stop misunderstandings before code.
A release readiness report shows what was tested, what passed and what risk remains.
Capability modules and deliverables
Risk-based test plan, test levels and the definition of done
Testable criteria for every story, agreed with product owners
An automated suite for critical journeys, run on every release
Page-speed baseline, budgets per page type and measured fixes
API and data checks for payment, shipping, ERP and CRM flows
A report per release with results, open defects and a go or no-go call
Performance deserves its own line. A page that loads slowly loses buyers before they see the product, and speed decays with every new script and image. We measure a baseline, agree a budget for each page type, and fail a release that breaks it, the same way a failing test would.
Delivery process, team, quality and security
Quality work follows our six-step delivery lifecycle:
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Discovery and strategic alignment
Critical journeys, risks and the definition of done.
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Team assembly and architecture planning
QA engineers join at team assembly, not at the end.
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Agile execution with outcome-based milestones
Every milestone closes with test results.
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Modular and productized components
Proven modules are reused where they remove risk and time.
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Training, rollout and optimization
Releases go live after readiness checks, then speed and quality are measured.
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Ongoing support and co-building
Suites and performance budgets grow with the product.
Teams range from 3 to 30 people and always include QA alongside business analysts, project managers, solution architects, developers and UI/UX. QA works in the same Agile sprints, remote-first from Hanoi in English, with test results visible in your Jira or GitHub. Governance runs through weekly reviews, KPI dashboards and a dedicated account manager and project manager.
Security testing sits inside the same pipeline. Netbase applies secure code review and version control, TLS in transit and AES at rest, role-based access control, MFA for admin dashboards, vulnerability scanning and penetration testing, and disaster recovery planning; contributors work under NDA. Netbase JSC holds ISO 27001 certification and a SOC 2 Type II attestation. Both apply to Netbase itself; they do not extend to your product or its hosting.
How AI changes testing, for your product and in our practice
AI makes test coverage cheaper to build and keep, and AI features themselves need a new kind of testing. QA engineers stay accountable: a generated test enters the suite only after review, and a release decision is never automated.
AI in our QA practice:
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Test case generation
Draft test cases and edge cases from user stories and acceptance criteria.
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Regression test drafts for changed code
Suggested automated tests where a change touches critical journeys.
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Self-healing locators and flaky test analysis
Broken selectors and unstable tests flagged with a likely cause.
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Defect triage
Duplicate bug reports grouped and failures clustered by root cause.
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Synthetic test data
Realistic, non-personal data for payment, shipping and account flows.
Testing the AI features you ship:
- Evaluation sets. Expected answers and scoring for assistants, search and recommendations.
- Safety and security checks. Prompt injection, unsafe output and data leakage tested before release.
- Drift checks after launch. Quality re-measured when a model, prompt or data source changes.
AI in this service
Where AI already runs in delivered work, and where it is offered as a growth capability.
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In delivered work
Recommendation engine
Delivered for 4over4's online printing store.
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Available capability
Evaluation and testing for AI features
Machine learning, NLP and generative AI that Netbase offers; not yet tied to a published quality engineering case.
Engagement models and commercial variables
Most Netbase projects are delivered on fixed-price contracts agreed after discovery; milestone-based, monthly team retainer and KPI-linked terms are also offered. QA is usually part of a project-based build or a dedicated development team, priced with the rest of the work on milestones or a monthly retainer. A live product can also add quality engineering on its own, as on-demand support that builds the regression suite and performance budgets around an existing codebase. We do not publish rate cards.
What moves effort: the number of critical journeys, integrations and devices to cover, release frequency, how much of the existing code has tests, and the quality of test data.
Technology as an implementation choice
We choose test and automation tools to fit your stack and team, so the suite keeps running after we hand it over. Most performance work happens in the front end: image loading, page weight, scripts and rendering on our frontend and mobile stack. Tests live in version control next to the code they protect.
Technologies we build with
Industry applications
Printing and packaging. Print commerce is a hard test case: online design tools, live previews, print-ready files and checkout must all work together on every device. When we deliver a web-to-print platform, those journeys are the first ones under regression tests and performance budgets.
Online retail. Any store where a broken checkout costs money by the minute gains from regression on critical journeys and budgets on page speed.
Printing and packaging: online ordering, AI artwork checks and production handoff
Netbase helps print and packaging businesses move ordering, artwork approval and production handoff online, with AI where files go wrong, so customers configure, design, proof and pay in one flow and the press floor receives clean jobs. Six published print-commerce case studies and 50+ custom web-to-print platforms, across apparel, packaging, signage, promotional merchandise and B2B portals, back this page.
Learn More
Proof: faster, steadier releases
USticker. The sticker printer needed a richer online design tool and a checkout that was faster and secure. Netbase added real-time design customization, integrated a secure payment gateway and optimized image loading. Page load fell 21%, completed designs rose 32% in six months, retention rose 25% and online-order revenue rose 15%; customers who used the design tool converted 40% better. Read the USticker case.
PrintLeo. The printing and advertising company had a slow, unstable website with a high bounce rate. Netbase delivered an end-to-end e-commerce solution with performance and stability work. Page load improved 35%, bounce rate fell 40%, engagement rose 25%, online sales revenue grew 20% and order processing time fell 15%. Read the PrintLeo case.
More results are in our work.
Netbase JSC built the feature and integrated a secure payment gateway.
Keep Reading
Netbase JSC delivered an end-to-end e-commerce solution with integrated design tools and performance work.
Keep ReadingBuyer FAQ
At team assembly. Acceptance criteria are written before development, and tests grow with every milestone.
Yes. We start with a risk review and automate the critical journeys first.
You do. Test code created for you is part of the custom work you own.
Yes. Page-speed baselines, budgets per page type and load checks before busy periods.
No. ISO 27001 and SOC 2 Type II apply to Netbase JSC itself, not to your product or its hosting.
No. AI drafts tests and triages failures faster, but engineers decide what to test, review every generated test and make the release call.
A suite nobody maintains. We keep tests in version control and review them each release.
Related solutions
Web-to-print platform with AI design assistance, from online design to a print-ready order
A web-to-print platform is an online ordering, design and prepress workflow that helps print businesses sell custom products: customers configure, design and approve their order online, AI can suggest layouts and catch artwork problems, and production receives a print-ready file. Netbase has delivered 50+ custom web-to-print platforms across apparel, packaging, signage, promotional merchandise and corporate B2B portals.
Learn More
Service owner and next step
This service is owned by David, Netbase's Chairman, founder and CEO, and maintained by the Netbase Editorial Team. Our guide to software delivery quality governance covers the wider delivery controls. Want releases you can trust? Book a solution review, or view relevant work first.
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