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.

Book a consult See our work

Runs in minutes

Or it is not run.

Flake tracked

And fixed, not retried.

Load tested

Before launch day.

Engagement

Embedded · monthly, per engineer

Flaky tests fixed at the cause. A suite that runs in minutes. Load tested before launch day. Example figures.

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

Automated test generationEval harnessesGuardrailsPlaywrightpytestJestk6CI pipelines

Where we do this mostFintech & payments →Healthcare & life sciences →

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.

Claude CodeMCP serversAgent orchestrationGuardrailsAutomated reviewJira

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.

Computer visionDataset strategyEvaluationRetraining loopi18n

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.

Recommendation systemsImage searchEvaluation gatesModel monitoring

Client withheld · details on the callAsk about this →

How it runs.

The same loop as every engagement

Week 0

Read the suite

What exists, what is skipped, what is flaky, and how long a green build takes today.

Week 1

A gate on every pull request

The suite runs in CI in minutes; a red build blocks the merge, and everyone can see why.

Week 3

End to end on the journeys that matter

The five flows that would make the phone ring, covered on real browsers and devices.

Week 6

Load test and handover

The launch load, run before the launch; your team owns the suite and the flake board.

Fortnightly

The review

Build time, flake rate, incidents, before and after. Then the next gap.

Questions we get about this.

Playwright, pytest, Jest and k6 for most stacks, plus generated tests and guardrails from our agentic pipeline where the codebase suits it. The tool follows the stack; we do not make you adopt ours.

A suite that runs in minutes speeds the team up; one that takes an hour gets skipped. The first thing we measure is build time, and it goes down before coverage goes up.

Yes. Evaluation harnesses are tests: a labelled set, a number, a gate in CI. We have shipped them for computer vision, recommendation and retrieval features.

The other seven.

All eight end in something running

01Custom software→02Team augmentation→03Data engineering & analytics→04AI & machine learning→05Cloud & DevOps→06Legacy modernisation→07Mobile→08QA & test automationhere

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.

Book a consult See our work

Or email info@hopsig.com — reply within one working day.

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 →

QA & test automation servicesSuites that makedeploys boring.

What you get.

From the record.

Agentic software-delivery framework on Claude Code

Detection from user-submitted photographs

Personalisation and image-search engine

How it runs.

Read the suite

A gate on every pull request

End to end on the journeys that matter

Load test and handover

The review

Questions we get about this.

What tools?+

Will tests slow the team down?+

Can you test the AI features too?+

The other seven.

Show us the suite everyone skips. We will make it trusted.

What you get.

From the record.

Agentic software-delivery framework on Claude Code

Detection from user-submitted photographs

Personalisation and image-search engine

How it runs.

Read the suite

A gate on every pull request

End to end on the journeys that matter

Load test and handover

The review

Questions we get about this.

What tools?+

Will tests slow the team down?+

Can you test the AI features too?+

The other seven.

QA & test automation servicesSuites that make
deploys boring.