Proof of work

Challenge

Critical capabilities were locked behind fragmented tooling, technical interfaces, and specialist knowledge. Access was slow, narrow, and dependent on the right people being involved.

What we built

Built an MCP based access layer that exposed infrastructure tools, endpoints, and integrations through natural language, so users could trigger actions and retrieve information without needing to understand the underlying systems.

Why it matters

This reduced friction dramatically, widened access to important internal capabilities, and shortened the distance between intent and execution.

Outcome

Faster access, less coordination overhead, and a much more scalable way to use internal systems.

Challenge

AI validation was too manual, too slow, and too difficult to scale. Teams lacked a fast and reliable way to understand behavioural change and make informed release decisions.

What we built

Created an automated evaluation system that continuously assessed behaviour, detected drift, enforced release gates, and generated decision ready reporting and metrics.

Why it matters

This moved AI quality from manual review into an operational system that could support faster delivery without losing control.

Outcome

Quicker release decisions, stronger visibility into change, and better risk management.

Challenge

Mobile validation relied too heavily on manual execution, which limited coverage, slowed feedback, and made scaling expensive.

What we built

Introduced an automated mobile quality system using Appium and BrowserStack, supported by AI analysis of logs, failures, and execution patterns to reduce the effort required to interpret results.

Why it matters

The gain was not only automated execution. It was the ability to understand large volumes of failure data faster and act on it more effectively.

Outcome

Broader coverage, faster triage, and far less manual effort spent reviewing results.

Challenge

Core commerce journeys needed reliable automated coverage, but the real challenge was maintaining useful signal and avoiding noisy, slow feedback.

What we built

Built comprehensive Playwright automation across key platform flows and supported it with automated analysis and reporting so results became easier to interpret and act on.

Why it matters

This shifted the work from isolated scripted checks into a more usable quality system that supported real delivery pressure.

Outcome

Better confidence in critical journeys, faster feedback, and clearer signals for the team.

Challenge

Regression analysis was slow, manual, and difficult to trust at scale. As result volume increased, interpretation became the real bottleneck.

What we built

Created an automated system for tracking regressions, analysing patterns, surfacing trends, and generating reports that highlighted meaningful issues instead of raw noise.

Why it matters

This made it easier to understand change over time, focus on what actually mattered, and make decisions faster.

Outcome

Faster understanding of failures, stronger trend visibility, and better prioritisation.