QA & test automation servicesSuites that make
deploys boring.
Unit, integration and end-to-end coverage that runs in CI in minutes, plus load testing before the launch rather than after the outage.
Runs in minutes
Or it is not run.
Flake tracked
And fixed, not retried.
Load tested
Before launch day.
Engagement
Embedded · monthly, per engineer
What you get.
Unit, integration and end-to-end coverage that runs in CI in minutes
Flaky tests tracked and fixed, not retried until green
Load testing before the launch, not after the outage
The same discipline for AI features: an evaluation harness that gates release
Engagement: Embedded · monthly, per engineer · Ask about your test suite →
Good for
— Releases that break something every time
— A test suite everyone skips
— A launch with no idea what load it takes
We will say no when
— Coverage is wanted as a number rather than as fewer incidents
— Tests are wanted so that nobody has to review
Stack · only what we have shipped to production
From the record.
All case notes →01 · AI · delivery
Data & AI
Agentic software-delivery framework on Claude Code
Problem — An engineering practice wanted to deliver faster without losing review, tests and documentation along the way.
What we did — An end-to-end pipeline on Claude Code: project planning, automated epic and story population with progress tracking, autonomous story implementation, a dedicated solutions-architect agent, guardrail enforcement, automated code review and test generation, living documentation, and human-in-the-loop escalation pushed to mobile when a decision needs a person.
Measured — Rolled out to 10 engineers and now the practice’s default path for new work. Two Android apps scoped at five developers were shipped by one, on the original timeline.
Client withheld · details on the callAsk about this →
02 · Agritech · computer vision
Data & AI
Detection from user-submitted photographs
Problem — Non-experts photographing in poor conditions; a model that had to act on what they sent.
What we did — Owned the labelled dataset strategy, the evaluation criteria that gated release, production monitoring and the retraining loop as field data arrived; capture guidance and failure handling treated as part of the product. Localised for low-literacy users.
Measured — 98% detection accuracy. Government incubator funding secured on the minimum viable build.
Client withheld · details on the callAsk about this →
03 · Retail · AI
Data & AI
Personalisation and image-search engine
Problem — Recommendations and visual search that had to prove their return in the client’s numbers, not in a demo.
What we did — Recommendation and image-search models with evaluation before release, production monitoring, retraining and version rollout owned end to end.
Measured — Up to 261× client ROI. Shipped an image-search model into production.
Client withheld · details on the callAsk about this →
QA & test automation · the call
Show us the suite everyone skips. We will make it trusted.
Bring the problem, not a brief. If it is outside what we do well, we will say which of our contacts to call instead.
Or email info@hopsig.com — reply within one working day.