Digital Assurance — QA, Testing & Security
Automated testing, performance work and security review across the stack — extended for AI systems, where correctness is a distribution rather than a pass or fail.
- Test automation
- Performance
- Security review
- AI evaluation
AI broke the definition of a passing test
Traditional software is deterministic: the same input gives the same output, and a test asserts on it. A language model returns something different every time, so quality becomes a score across a dataset rather than a boolean. Most QA practices have not caught up, which is why AI features ship with no regression safety net at all.
Capabilities in this practice
Each of these ships as a working system integrated with what you already run — not a slide deck or a proof of concept that stalls at the pilot.
Test Automation
End-to-end suites in Playwright or Cypress that run in CI in minutes, written to survive refactors instead of breaking on every selector change.
API & Contract Testing
Contract tests between services so an upstream change fails a build rather than a customer transaction.
Performance & Load
JMeter and k6 load profiles modelled on real traffic, with the bottleneck identified rather than a report saying it was slow.
Security Review
Dependency and secret scanning, authentication and authorisation review, and the OWASP basics verified rather than assumed.
AI Evaluation Suites
Golden datasets, groundedness and citation scoring, and LLM-as-judge pipelines wired into CI so a prompt change that degrades quality blocks the merge.
AI Safety Testing
Prompt-injection and jailbreak probes, PII leakage checks and refusal-behaviour testing against the boundaries the system is supposed to hold.
A sequence built to de-risk, not to impress
We measure before we optimise and ship in slices, so you can stop, redirect or scale at any step with evidence rather than instinct.
- 1
Risk map
Identify what actually hurts if it breaks; test that first rather than chasing a coverage percentage.
- 2
Baseline
Measure current quality, flake rate and pipeline duration so improvement is demonstrable.
- 3
Automate the critical paths
Cover the flows that carry revenue or risk before broadening.
- 4
Wire into CI
Tests that do not gate a merge get ignored within a month.
- 5
Add AI evals
Where the product uses models, quality scoring joins the same pipeline as the unit tests.
- 6
Keep it fast
Prune flakes and parallelise; a slow suite gets skipped, which is worse than no suite.
Where this gets applied
We build our own products on this stack. When we recommend an approach, it is one we already run in production and pay the bills for.
The things clients ask before signing
You stop asserting on exact strings and start scoring a dataset. A golden set of real inputs with expected properties — is it grounded, does it cite a real source, is the format valid, does it refuse when it should — produces a score per build. A drop below threshold fails the pipeline exactly like a failing unit test.
Yes, and that is usually the better arrangement. We tend to build the automation framework and the AI evaluation layer, then hand both to your team with the practices to maintain them.
No. We do design and code-level review — auth flows, access control, dependency and secret hygiene, injection surfaces. A formal penetration test from an accredited third party is a different exercise, and we will tell you when you need one.
Ready to put this into production?
Tell us the problem you are trying to solve. We will tell you honestly whether AI is the right tool for it, and what it would take to ship.