QA & Testing Company in New York

Local Law 144 changed what QA means for a lot of New York employers, and the budget follows which world you are operating in. Standing up Playwright or Cypress automation on a mid-size product is straightforward engineering. A trading-system or AEDT QA program at a Wall Street firm or HR-tech vendor is a compliance artefact. A Local Law 144 bias audit project (independent auditor coordination, harness development, statistical analysis, and public posting drafting) is a legal deliverable with code attached. Scope drivers include framework, CI integration, baseline coverage, and team handover. NYC rates sit above most US markets because the Wall Street and HR-tech QA talent pool commands a premium. We give fixed-fee proposals on bounded scopes and milestone-based contracts on multi-year programs.

NYC Local Law 144, effective July 5, 2023 with enforcement starting January 1 in subsequent years, requires that any Automated Employment Decision Tool used to substantially assist or replace human discretion in hiring or promotion decisions for NYC roles undergo an annual bias audit by an independent auditor. Our role is technical: we build the harness that pulls historic decision data, computes selection rates and impact ratios across sex, race and ethnicity, and intersectional categories using the formulas in the final DCWP rule, produces the data tables an independent auditor needs to issue an opinion, and drafts the public-facing summary the law requires be posted on the employer's website. We coordinate with auditors but do not act as one. We have built this harness for multiple AEDT vendors and employers and can move quickly inside the annual deadline.

Yes. We build test rigs that exercise order management, smart order routing, risk checks under FINRA Rule 15c3-5 market access, post-trade reconciliation, and regulatory reporting feeds (CAT, blue sheets, large trader reporting). Latency tests capture wire-speed timing distributions on the order path, correctness tests cover the FIX, OUCH, ITCH, and proprietary protocols in play, and audit-trail tests verify that every decision the system makes is reconstructable for an examiner. Under NYDFS Part 500, we produce evidence on access control, segregation of duties in deployment, and change-management testing that maps to the cybersecurity program requirements. We do not move client trading code; we test it inside their controlled environments, with our staff cleared through the firm's standard onboarding.

The Southern and Eastern Districts of New York lead the US in ADA Title III digital accessibility filings, and a single demand letter typically costs USD $20,000 to $50,000 to settle before any remediation work, with worse outcomes if WCAG conformance is weak. Our accessibility QA program produces a current-state audit against WCAG 2.1 AA (and 2.2 AA where the client's accessibility statement commits to it), an automated pipeline using axe-core inside Playwright or Cypress with Pa11y for sitewide sweeps and Lighthouse for performance and a11y signals, and a manual screen reader QA pass using NVDA, JAWS, and VoiceOver. We produce a VPAT (Voluntary Product Accessibility Template) where procurement demands it. The evidence trail has held up in actual settlement negotiations our clients have walked through.

Yes. Federal Reserve SR 11-7 on model risk management requires effective challenge of models through independent testing, including conceptual soundness, ongoing monitoring, and outcomes analysis. Our QA team operates as the independent test function for client models (credit, market risk, AML, fraud, algorithmic trading) and produces challenger model results, backtesting, benchmarking, and the documentation Model Risk Management functions submit to internal audit and Federal Reserve examiners. We coordinate with internal MRM teams on scope, do not produce the validation opinion ourselves (that remains an independent function), and provide the test engineering that frees senior validators to focus on judgment rather than infrastructure.

Yes. NYC consumer brands (Madison Avenue retail, Hearst and Condé Nast publishing, HBO and Disney consumer products, hospitality and restaurant groups) need cross-browser and cross-device coverage that reflects an audience using Safari on iOS 18, Chrome on Android 14 and 15, and an unusually high share of older Android devices through tourists and outer-borough demographics. We build BrowserStack Live and Automate or Sauce Labs Real Device Cloud matrices that target the device, OS, and browser distribution your analytics actually show, not a generic top-ten list. Visual regression tests use Percy or Chromatic against design system tokens. Mobile-specific flows are covered with Appium 2 and the platform-native frameworks.

PII handling under NY SHIELD, GLBA for financial firms, and federal employment law for HR data does not allow casual use of production data in non-production environments. Our default is synthetic data generation aligned with the production schema and distribution (using Faker, custom generators, or commercial tools like Tonic.ai or Gretel), with masking for any unavoidable production-derived test data, and a documented data flow that auditors can trace. For trading systems we generate realistic FIX message flows from market data replays. For AEDT bias audits we work with anonymised decision data that has been aggregated to the level needed for impact ratio computation. Data classification is reviewed in discovery and revisited at every milestone.

For a mid-size SaaS or consumer product we can have Playwright or Cypress automation running in CI within four to six weeks, with meaningful coverage on the top user journeys by week eight. For a trading-system or AEDT QA program the ramp is longer (twelve to twenty weeks) because regulatory scope review, data classification, and the production-mirror staging environment all need to be in place first. For an emergency engagement (an active ADA Title III demand letter, a Local Law 144 audit window closing in weeks, a Federal Reserve exam looming) we can mobilise a dedicated pod within two weeks, working alongside the client's incumbent QA staff rather than replacing them. We have run this acceleration on real NYC client crises.