i3QA™ testing services — manual, automation and AI-led — and the newer discipline underneath them: proving what a system does when it no longer does the same thing twice.
Enterprises are not stuck in pilot because their models are bad. They are stuck because nobody can certify the behaviour well enough to put it in front of a customer or a regulator.
Conventional test engineering assumes a system is deterministic: same input, same output, pass or fail. That assumption quietly stopped holding the moment a language model entered the request path. The tooling most organisations own has no answer for it.
This practice does both halves. The established half is twenty years of functional, regression, performance and security testing for banking-grade and telecom-grade platforms. The newer half is assurance for non-deterministic systems: evaluation harnesses with a held-out set and a scored rubric, guardrail and jailbreak testing, regression bands rather than exact-match assertions, drift detection against a live baseline, and audit trails an examiner can follow.
Functional and regression testing, exploratory testing, UAT support and release certification. Manual where judgement is required, automated everywhere it is not, and a clear-eyed view of which is which.
Modular, scalable automation suites built to survive the application changing. Selenium, CodeCept, Postman and Newman for API collections, Tricentis Tosca for model-based testing on large enterprise estates. We have introduced contemporary automation to legacy applications that had never had any.
Endpoint protection against payload tampering and data injection, role-based access control validation, session and authorisation testing, and common web vulnerabilities including click-jacking. Burp Suite for attack simulation, with intercepted and manipulated payloads rather than a scanner report.
Preparing a platform for the certification it has to pass — PCI‑DSS most often, with data scrubbing, audit trails, timestamp integrity and the evidence pack that goes with them. We work to the assessor’s checklist, not to a generic hardening guide.
Load and soak testing against a defined budget, plus the pipeline work that shortens a release cycle: what gets tested on every commit, what gets tested nightly, and what still needs a human before it ships.
Evaluation harnesses with versioned datasets and scored rubrics. Guardrail and adversarial-prompt testing. Regression under non-determinism, expressed as tolerance bands with an agreed failure threshold. Drift detection against a live baseline. Cost-per-outcome tracking, because a system that gets slower and more expensive has regressed even when every assertion passes. Delivered alongside TurfAI where we built the system, and independently where you built it.
The TurfAI practiceTest generation, case maintenance and triage are all getting faster because of the same tools everyone else has. Sold by the hour, this practice gets repriced by the market within eighteen months.
So we do not sell it by the hour where we can avoid it. We price against release confidence: what has to be provably true before a build ships, and what it costs to keep that true as the system changes. When automation makes a step cheaper, that shows up in your bill rather than in our margin.
If you are being quoted a rate card for AI testing without a definition of what “pass” means for a non-deterministic system, that is the question worth asking — of us as much as of anyone else.
API security, RBAC validation and PCI‑DSS readiness across payments, KYC and social modules.
Read the case study →
Release-cycle reduction on a legacy financial application platform, without loosening the controls.
Read the case study →Bring us the answer to that question and we will build the harness that proves it — whether or not we wrote the system.
