Describe the test. Speedwave writes it and runs it.
Natural language to executable test, running in an isolated environment. The coverage agent in Speedwave's 13-agent pipeline identifies what changed in each commit and maps it to the test surface — so you stop running the full suite on every PR.
Three problems that slow every QA cycle in a regulated environment.
Speedwave addresses each without requiring a new test framework or a data migration.
Manual test authoring
Writing test cases after a developer says "done" creates a sequential bottleneck. Describing acceptance criteria in JIRA and then re-writing them as executable tests is duplication of intent. Speedwave generates executable tests from the description.
PII in test environments
Realistic test data often contains real customer records — names, IBANs, PESELs. That is a GDPR liability in your test environment. Sanitised data breaks realistic tests. PII tokenisation preserves realism without the compliance risk.
Full-suite regression on every PR
Running the complete test suite on every pull request is too slow. Skipping it is too risky. The Speedwave coverage agent identifies which tests are actually relevant to what changed in the commit.
What Speedwave gives QA and testing teams.
From test generation to isolated execution to intelligent regression scope — without changing your test framework.
Natural language to test
Describe the acceptance criteria in plain English — or paste in the JIRA ticket. Speedwave generates executable test code in your existing framework. You review and merge. The authoring step becomes an edit step.
Isolated test environments
Each test run executes inside a Speedwave container. Clean state, reproducible results, no cross-contamination between test suites or projects. Eliminates "works on my machine" failures.
Intelligent regression scope
The coverage agent analyses what changed in a commit and maps it to the test coverage surface. Run what matters for this change — not everything, not nothing.
PII-safe test data
PESEL, IBAN, phone numbers, and email addresses in test datasets are tokenised before reaching the model. Tokenisation is stable within a session — assertions still work, but real customer data never leaves your environment.
Audit trail for test decisions
Every test run, result, and coverage decision logged with timestamp. Useful when a regulator asks why a particular code path was — or was not — covered before a production release.
Ready to see it in your BFSI environment?
We will show you test generation and regression scoping running on a real BFSI project in a 30-minute demo.