Offering · Mortgage and insurance
Web, mobile, API and data testing for lenders, servicers and insurers, with the Mortgage Domain Pack built in. AI assists in writing and maintaining tests, which your engineers review; execution is deterministic and runs in your own pipeline — no model in the runtime loop, and no result you cannot reproduce.
The category problem
The current wave of AI testing tools puts a model inside the execution path — an agent reads the screen at runtime and decides what to click. It demonstrates well, but in a regulated enterprise it does not produce defensible evidence.
The same test passes on Tuesday and fails on Wednesday against unchanged code. Every red result becomes triage work for a person, who has to work out whether it is a real defect or variation in the model. The suite generates work instead of confidence.
No examiner accepts "the model decided." Deterministic execution isn't a technical preference — it is what makes the evidence admissible. Same commit and same data produce the same result, and any run can be re-created on demand.
Where we draw the line
That single line is the whole design. AI earns its place writing and repairing the automation, where a human reviews everything it produces. It never touches the run, because the run is the evidence.
AI is used here
Turning written test cases into executable code, keeping that code alive as the application changes, and pointing out where coverage has drifted from the plan. Everything it writes arrives as a pull request for your engineers to approve.
AI is never here
Runs are plain automation binaries, not agents. Failures reproduce from the committed script. The evidence is a build artifact rather than a transcript of what a model thought it saw.
The mortgage domain pack
This is the difference between mortgage test automation and test automation that happens to be pointed at a lender. A generic framework hands you a runner and a blank page. The pack arrives with the domain already in it — fixtures, schemas and suites for MISMO payloads, GSE delivery paths, and credit and pricing decisions.
Fixtures and schemas for the MISMO logical data model and the ULDD and iLAD delivery containers — so a payload is tested as data before anyone tests it as a screen.
Suites that exercise agency delivery the way an investor will, so a data error surfaces in your own pipeline rather than in a rejection weeks later.
Cases for eligibility, pricing and product rules where the assertion is that the decision is correct and reproducible — not merely that the page rendered.
Insurance applications
Insurance applications fail in the same places mortgage applications do — in the rules and the data, not the screens. Scenarios are built with your team against your products, forms and jurisdictions.
Quote, bind, endorsement and renewal flows, with assertions on the premium, the forms attached and the rules applied — by state and by product.
Intake, triage, coverage verification, reserving and payment, with authority limits and referral triggers tested as decisions rather than as page renders.
Telematics and third-party data feeds, carrier and partner APIs, and downstream reporting — contract and data tests so a feed change is caught before it reaches a claim.
Testing the AI inside the application?
Guardrails, retrieval context, versioned regression and production validation for the language models in your product — with the same discipline as the rest of your test estate.
See LLM Assurance →The shape of it
Plans and cases are read from the tool your QA organization already uses, and results are written back against the originating run — so your existing process stays the system of record.
Authored
Your tester writes the case, because the case is a statement about what the system must do.
Generated
The written case is compiled into a script your engineers read as a pull request.
Run
Deterministic execution in your pipeline, producing evidence that reproduces from the commit.
Interoperability
We are not another test management tool, and adopting this does not mean migrating your QA estate. We read plans and cases out of your tool, and write results, evidence and defects back into the same run — so nothing downstream of your team has to change.
Jira with Xray or Zephyr, Tricentis qTest and Tosca, TestRail, Azure Test Plans, OpenText ALM Octane, PractiTest, BrowserStack Test Management — read and written back through their REST APIs. An in-house system works too, if it exposes one.
Playwright drives the browser — Chromium, Firefox and WebKit, with parallel workers, traces, stubbing and visual checks. Appium drives the handset — native iOS and Android plus mobile web. API cases generate as HTTP and contract tests, so one run spans UI, mobile and service layers.
Jenkins, GitHub Actions, Azure DevOps or GitLab CI, inside your own tenancy or data centre. BrowserStack for the real browser, OS and device matrix if you want it — Selenium Grid or your own fleet if you don't. Results and defects sync back to Jira or ServiceNow.
Control & governance
The same posture as everything else we deliver: your application data stays inside your boundary, generation can run against a private or self-hosted model, and every script is version-controlled with a named reviewer behind it.
Which requirements are covered, by which cases, at which status — as of the last run, not the last spreadsheet update.
Any run can be re-created and re-executed on demand, months later, exactly as it ran the first time.
Getting started
Coverage rises without adding senior automation headcount, and every release ships with its own evidence. We start where the pain is measurable — on its own or inside the optional four-week engagement, against a real release.
Questions buyers ask
QA and test automation for mortgage and insurance applications — web, mobile, API and data testing. AI assists in writing and maintaining the tests, which your engineers review; execution is deterministic and runs in your own CI pipeline, so every result can be reproduced from the commit.
Fixtures, schemas and test suites with the mortgage domain already built in — MISMO payloads, ULDD and iLAD delivery containers, GSE delivery paths, and credit, pricing and eligibility rules — so you are not starting from an empty test framework.
No. AI is used to author and maintain tests, and everything it writes arrives as a pull request for your engineers to approve. Runs are standard automation — Playwright for browsers, Appium for mobile, and HTTP and contract tests for APIs — so the evidence is reproducible.
Yes. Plans and cases are read from your test management tool — such as Jira with Xray or Zephyr, TestRail, qTest or Azure Test Plans — and results are written back to the same run. It runs in your CI, such as Jenkins, GitHub Actions, Azure DevOps or GitLab CI.
Yes. Quote, bind, endorsement and renewal flows, claims and FNOL, authority limits and referral triggers, and telematics and partner data feeds are tested as decisions rather than as page renders.
What we'd need to start
Access to your test management tool and CI, one high-regression area, and someone who owns QA. We come back with a costed pilot scoped to a single measurable number.
Scope application testingNeed help fixing what the tests reveal? Add Reslt AI product engineering →
