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Guaranteed Expert Consultation Within 1 Hour. Click Here!
Guaranteed Expert Consultation Within 1 Hour. Click Here!
QA assessments, test strategy, automation frameworks, and hands-on testing for US software teams.
Recognized for delivering the best solutions for businesses of all sizes and across industries.
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Quick answer: QA consulting fixes how you test; QA services do the testing. We do both. A QA process assessment takes 2 to 3 weeks and costs $1,000 to $3,920 at our rates, and ends with a prioritized plan covering coverage gaps, automation candidates, environments, and release criteria. From there, teams usually take an automation framework build, a managed QA pod, or embedded QA engineers.
Most teams do not have a testing problem. They have a feedback problem: defects found too late, a regression suite nobody trusts, environments that do not match production, and a release decision made on a feeling rather than on evidence.
NewAgeSysIT has been testing software for more than 30 years, from Princeton, New Jersey. We work as consultants when you need the approach fixed, and as a delivery team when you need the testing done.
The World Quality Report 2025-26, published by Capgemini, Sogeti, and OpenText from a survey of more than 2,000 senior executives, gives useful benchmarks for a conversation with your leadership:
with nearly half still in the planning stage
with 37% in production and 52% in pilots
though a third report gains of only 1% to 9%
on average
Two things follow from that. Most teams are nowhere near the automation coverage they assume they have, and the AI tooling everyone is buying produces real but modest gains, concentrated in teams that fixed their fundamentals first. That is the work this page is about.
There is a second reason testing budgets are rising: more of the code is written by AI. Veracode's 2026 testing found an average security pass rate of 56% for AI-generated code, with roughly 44% of code generation tasks producing a known vulnerability when the prompt said nothing about security. More code, written faster, with the same defect rate per line, means more testing, not less.
Most engagements start with the first and continue into the second or third. We will tell you when an assessment is not needed, which is usually the case when the team already knows the problem and simply needs testing done.
| Comparison | QA consulting | QA and testing services | QA staff augmentation |
|---|---|---|---|
| What you buy | A review of how you test, and a plan | Testing executed by our team | Engineers inside your team |
| Typical output | Findings, test strategy, automation roadmap, release criteria | Test cases, automation suites, defect reports, release sign-off evidence | Capacity under your management |
| Who it suits | Teams whose process is the problem | Teams without QA capacity, or facing a release | Teams that know what to do and need hands |
| Typical length | 2 to 6 weeks | Per release, per project, or monthly | Monthly, ongoing |
Tell us what is escaping to production, how often you release, and what your last three releases cost you in unplanned work. We will tell you whether the fix is process, coverage, automation, or capacity, and what it costs.
Get a quick expert response within 1 hour.
Security testing here means testing coverage, not a full security program. When the question is "are we exposed," that work belongs with our IT security and cybersecurity services team.
Current coverage, tooling, environments, defect flow, release criteria, and team structure, with a prioritized improvement plan
What gets tested at which level, entry and exit criteria, risk-based prioritization, and the testing budget to support it
Tool selection, framework build, CI integration, reporting, and the rules for what gets automated
Test case design, execution, and maintenance across web, mobile, and back-end systems
Contract, data, and error-path testing for the connections that break quietly
Load, stress, soak, and spike testing, with bottleneck analysis and tuning recommendations
Real-device coverage across iOS and Android versions, networks, and screen sizes
WCAG 2.1 Level AA review with issue lists mapped to success criteria
Vulnerability scanning and test coverage for common attack paths, alongside your security program
Reconciliation, completeness, and integrity checks when systems change
Scripts, coordination, defect triage, and go or no-go evidence for business users
Evidence packages for regulated environments, including HIPAA and ISO 27001 contexts
A standing team covering your release cycle, reporting against agreed quality metrics
Automation fails when teams automate everything, and it fails when they automate nothing. The rules we apply:
Automate stable regression paths, smoke tests on every build, API and contract tests, data setup, and anything run more than a few times per release.
Do not automate flows still changing weekly, one-off exploratory scenarios, usability judgments, or anything where the test would cost more to maintain than to run manually.
Retire tests that have not failed in a year and do not cover a risk, because a suite nobody trusts is worse than a smaller one that people believe.
A worked example, using our rates. Suppose a regression pass takes 60 hours of manual effort and you release every two weeks: that is roughly 1,440 hours a year. Automating the stable 70% costs about 300 hours to build, then 8 hours per release to run and maintain, or around 192 hours a year. First-year saving is roughly 950 hours, which at $25 to $49 per hour is $23,750 to $46,550, and the saving grows in year two when the build cost is gone.
Two cautions that matter more than the math. Flaky tests destroy trust faster than missing tests, so we budget for stabilization rather than pretending it will not be needed. And automation does not reduce headcount in most teams; it moves people from running the same checks every release to testing the things only a person can.
AI is genuinely useful in four places: drafting test cases from requirements, generating test data, maintaining selectors when the interface changes, and triaging failures so engineers look at the right ones first.
It is not useful for deciding whether to release. The World Quality Report data explains why: 89% of organizations are piloting generative AI in quality engineering, but only 15% have scaled it, and the stated barriers are reliability, data privacy, and integration rather than enthusiasm. Average productivity gains sit at 19%, with a third of organizations reporting almost nothing.
Our rule on every engagement: AI drafts, a person reviews, and release decisions stay with named humans who can explain them. Building that review step into the pipeline, rather than bolting it on, is part of our AI integration and adoption services.
We work in your stack rather than moving you to ours. Where a tool choice is genuinely wrong for your situation, we will say so and explain the migration cost before anyone commits.
Quality arguments go in circles without agreed numbers. These are the ones we baseline at the start and report against:
| Metric | What it tells you |
|---|---|
| Defect escape rate | Defects found in production against those found in testing, the single best measure of whether QA is working |
| Severity mix at release | How many severity one and two issues are open at the go or no-go decision |
| Automated coverage of critical paths | Not overall coverage percentage, but whether the journeys that make money are covered |
| Test suite pass and flake rate | A suite with a 10% flake rate is a suite nobody reads |
| Mean time to detect and fix | How long a defect lives between introduction and resolution |
| Regression cycle time | Hours to complete a full regression pass, which is where automation shows up |
| Defect reopen rate | How often "fixed" was not fixed |
| Release frequency and rollback rate | Whether quality work is speeding delivery up or slowing it down |
We report these monthly in a format your leadership can read without translation, and we agree the targets before the work starts rather than celebrating whatever happens.
A note on the numbers often quoted in this market, including on this page's previous version: reductions of "20 to 40% in testing cost" and "zero severe defects in production" are outcomes some engagements reach, not guarantees. We prefer to agree a baseline, commit to a direction, and show the measurement.
NewAgeSysIT is a US company with its own offshore delivery centers, which is how QA work runs at $25 to $49 per hour with US-based project leadership.
| Engagement | Effort | Cost at our rates | Typical duration |
|---|---|---|---|
| QA process assessment Start here | 40 to 80 hours | $1,000 to $3,920 | 2 to 3 weeks |
| Test strategy and plan | 60 to 120 hours | $1,500 to $5,880 | 2 to 4 weeks |
| Test automation framework build | 120 to 320 hours | $3,000 to $15,680 | 4 to 8 weeks |
| Regression suite build, per application | 200 to 600 hours | $5,000 to $29,400 | 6 to 14 weeks |
| Performance testing engagement | 80 to 200 hours | $2,000 to $9,800 | 3 to 6 weeks |
| Accessibility audit to WCAG 2.1 AA | 60 to 140 hours | $1,500 to $6,860 | 2 to 4 weeks |
| Security testing support | 60 to 160 hours | $1,500 to $7,840 | 2 to 4 weeks |
| Release testing cycle, per release | 60 to 160 hours | $1,500 to $7,840 | 1 to 3 weeks |
| Managed QA pod | 160 to 320 hours a month | $4,000 to $15,680 a month | Ongoing |
Most clients start with an assessment or a single release cycle, then move to a pod or embedded engineers once the approach is agreed. Fixed-price quotes follow the assessment, because quoting a regression suite before anyone has seen the application is how both sides end up unhappy.
For a quick figure on a development project rather than a QA engagement, our app cost calculator takes a couple of minutes.
Current coverage, defect history, release cadence, environments, tooling, and the last three releases reviewed for what escaped and why.
Gaps ranked by risk against effort, with the quick wins separated from the structural work.
Test strategy, automation candidates, environment and data requirements, release criteria, and a costed sequence.
Either your team executes the plan with our review, or we take the work, or we embed engineers inside your team. All three are normal.
Patient data handling, integration accuracy, audit trails, and accessibility
Transaction integrity, reconciliation, fraud paths, and regulated reporting
Checkout under load, payment paths, inventory accuracy, peak-season readiness
Multi-tenant isolation, permissions, upgrade safety, and API contracts
Offline behavior, device coverage, and integration with partner systems
Accessibility to WCAG standards, assessment integrity, and term-time load peaks
Real-time flows, location behavior, and two-sided notification paths
Streaming performance, device matrix coverage, and concurrency
Industry decides what "done" means. A checkout bug costs revenue, a clinical integration bug costs safety, and the testing depth should reflect that difference rather than a standard template.
You can see the delivery side of our work in client products such as Car-Up, an auto repair booking platform where booking and payment paths had to work on the first try.
30+ years in software, 1,000+ projects delivered, 4.8 million downloads across client products, and a five-star Clutch rating.
QA engineers who have shipped, working alongside the developers who build, so defects get fixed rather than argued about.
Your stack, not ours. We work in the tools you already run, and we say so when a tool change is genuinely worth the cost.
Metrics before promises. Baselines agreed at the start, reported monthly.
US leadership, hybrid rates. Princeton-based project management with our own delivery centers at $25 to $49 per hour.
A seven-day risk-free trial so you can see how the team works before committing to a scope.
Get expert guidance before you invest in AI software development. Work directly with Giovanni to validate your technology direction, align AI with business goals, and make confident decisions that reduce risk and accelerate outcomes.
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From every single moment, from the beginning till the end, they were there for me. They were very systematic and methodical in every single step and
Owner - ISRA
Increase in Monthly Bookings within 6 Months
Reduction in Appointment No-Shows
They delivered everything on time and it was of great quality. They go above and beyond to meet yourrequirements and deliver the product you are looking for.
Founder -LCard App
Increase in User Sign-Ups in First Quarter
Boost in Networking
Conversions
They are very knowledgeable in the sense that they have built so many of these types of applications that they..
Founder -CAR-UP App
Increase in Online Service Bookings
Reduction in Service Scheduling Conflicts
From every single moment, from the beginning till the end, they were there for me. They were very systematic and methodical in every single step and
CEO - WhipFlip
Faster Vehicle Listing to Offer Time
Increase in Lead-to-Sale Conversion Rate
From every single moment, from the beginning till the end, they were there for me. They were very systematic and methodical in every single step and
Owner - ISRA
Increase in Monthly Bookings within 6 Months
Reduction in Appointment No-Shows
They delivered everything on time and it was of great quality. They go above and beyond to meet yourrequirements and deliver the product you are looking for.
Founder -LCard App
Increase in User Sign-Ups in First Quarter
Boost in Networking Conversions
They are very knowledgeable in the sense that they have built so many of these types of applications that they..
Founder -CAR-UP App
Increase in Online Service Bookings
Reduction in Service Scheduling Conflicts
From every single moment, from the beginning till the end, they were there for me. They were very systematic and methodical in every single step and
CEO - WhipFlip
Faster Vehicle Listing to Offer Time
Increase in Lead-to-Sale Conversion Rate
QA consulting is an expert review of how your team tests, followed by a plan to fix what is not working. It covers coverage gaps, test strategy, automation candidates, environments, test data, defect flow, and release criteria. The output is findings and a costed roadmap rather than test execution, though most clients continue into delivery.
Consulting fixes the approach, services execute the testing. If defects keep escaping despite a busy QA team, you need consulting. If you have no QA capacity before a release, you need services. We do both, and we will say which one your situation calls for.
At our rates, a QA process assessment runs $1,000 to $3,920, a test strategy $1,500 to $5,880, an automation framework build $3,000 to $15,680, a regression suite $5,000 to $29,400 per application, and a managed QA pod $4,000 to $15,680 a month. Fixed-price quotes follow the assessment.
Two to three weeks for a single product, including a review of your last three releases, a look at current coverage and tooling, and interviews with the people doing the testing. Larger platforms with multiple teams take longer and are scoped after a call.
There is no universal number, and chasing one wastes money. The World Quality Report's 2025-26 average is 33% of test cases automated. What matters more is whether the journeys that make you money are covered on every build, and whether the suite is stable enough that people act on a failure.
Playwright, Selenium, and Cypress for web, Appium with XCUITest and Espresso for mobile, Postman and REST Assured for APIs, JMeter, k6, and Gatling for performance, and Jira with Xray, Zephyr, or TestRail for management. We work in whatever you already run unless there is a clear reason to change.
Yes. Real-device testing across iOS and Android versions, screen sizes, and network conditions, using device clouds plus a physical device set for the hardware behaviors that clouds handle poorly, such as camera, background location, and offline sync.
Yes, including load, stress, soak, and spike testing with bottleneck analysis. The output is not just a chart: it is the specific queries, services, or configuration limits that break first, with recommended fixes and a retest.
Yes. We test against WCAG 2.1 Level AA and report issues mapped to specific success criteria with code-level fixes. This matters for public sector buyers under the ADA Title II rule and for anyone selling digital products into the EU.
Not yet, and not in the way vendors suggest. AI drafts test cases, generates test data, maintains selectors, and triages failures well. Surveys show 89% of organizations piloting generative AI in quality engineering but only 15% scaling it, with average productivity gains of 19%. Exploratory testing and release decisions stay with people.
Yes, more of it. Veracode's 2026 research found an average security pass rate of 56% for AI-generated code, with about 44% of generation tasks producing a known vulnerability when security was not specified. More code at higher speed with the same defect rate means more scanning, more review, and tighter pipeline gates.
Yes. Embedded engineers join your ceremonies, your board, and your definition of done, under your management. That suits teams that know what to do and need capacity rather than advice.
Defect escape rate, severity mix at release, automated coverage of critical paths, suite pass and flake rate, mean time to detect and fix, regression cycle time, defect reopen rate, and release frequency against rollback rate. We baseline them before the work and report monthly.
No, and anyone who does is selling. What we can commit to is a measured reduction in escaped defects, clear release criteria, evidence behind the go or no-go decision, and a trend you can see monthly. That is a more useful promise than a number nobody can hold us to.
Tell us what is escaping to production, how often you release, and what your last three releases cost you in unplanned work. We will tell you whether the fix is process, coverage, automation, or capacity, and what it costs.
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Request a consultation or call 1-609-331-9194. We respond within one business hour during the working day.
Process
Coverage
Automation
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