AI, Process & Software — built to deliver.

Areas of expertise

• →Business process optimization

  Every organisation runs on processes, and friction accumulates in them the same way debt accumulates in code: hand-offs nobody owns, steps kept out of habit, workarounds instead of a system built for how work actually moves. Fixing it starts with mapping the real flow, not the version on the org chart, then redesigning around locality — so a change in one place doesn't require unraveling five others — and around the outcome the customer actually feels. Often that means better tooling, automation, or AI where it fits. What it always means is a team that can improve its own daily work, instead of just enduring it.

  • End-to-end process mapping & discovery
  • Bottleneck, hand-off & busywork analysis
  • Redesign for locality & simplicity
  • Measurable targets and before/after metrics
  • Change rollout that actually sticks

  Success story

  From hours to minutes: 500+ deployments a day

  One engagement took a deployment process that ran in hours down to minutes — not by adding headcount, but by rebuilding the pipeline and the team structure around it. Deployment frequency went from dozens a day to over 500, with quality and assurance improving alongside it rather than being traded away for speed. The shift changed how the organisation worked day to day, not just how it shipped code.

• →Software delivery

  Production software gets designed and shipped end-to-end — backend systems, APIs and the web apps and marketing sites in front of them. The deep expertise sits in the backend: the JVM (Java, Kotlin, Spring) and Go, building systems that hold correct under load and stay owned by your team long after launch. Front-end work gets delivered too, built well with modern tooling, but the guarantee of senior-level craft is on the backend.

  • Backend systems & APIs — JVM (Java, Kotlin, Spring) & Go
  • Web apps & marketing sites, full-stack delivery
  • Architecture & technical strategy
  • Optimized delivery pipelines & clean handover

  Success story

  WealthTrack — a self-hosted finance tracker built to last

  A family finance tracker built to be fully owned, not rented — self-hosted, with years of financial data kept accurate, and never at risk of vendor lock-in. It's built to be genuinely usable end to end: simple enough for someone with no financial background to read at a glance, with visualisations detailed enough to satisfy someone who reads a balance sheet for a living.

• →AI transformation

  Real AI transformation isn't giving employees a chatbot licence and calling it adoption — that's the mediocre version, and it shows up nowhere in the numbers. It starts with mapping AI usage to defined business goals, then restructuring how the company works so AI is part of the system, not an add-on nobody touches after week one. That includes detailed implementation guidance down to the workflow level, and building custom AI tooling for the company — reducing the FTE a workflow needs while increasing delivery velocity and clarity. Everything gets measured against the goals set at the start, not against tool-usage stats.

  • Goal-mapped AI adoption, measured against defined outcomes
  • Company-wide restructuring around AI, not bolt-on tooling
  • Custom AI tooling built for the company
  • Evaluation, guardrails & cost control

  Success story

  One system, org-wide reach, a fraction of one hire's monthly cost

  A production AI system now runs continuously across a large regulated organization — coordinating hundreds of purpose-built agent routes at an inference cost per team that lands well under a tenth of what one additional hire would cost per month. That's what real AI-transformation ROI looks like: capability that scales without scaling headcount.

• →Advisory & consulting

  Founders and leadership get a second opinion from someone who has built and shipped the systems in question, not just advised on them. The read covers what's actually broken, what's a genuine constraint versus an assumed one, and which fix is proportionate to the problem — a hire, a rewrite, or a build-vs-buy call. The output is a decision that can be acted on this quarter, with the reasoning behind it, not a deck that sits in a drawer.

  • Technical due diligence & advisory
  • AI-implementation strategy
  • Architecture & build-vs-buy decisions
  • Prioritised, plain-language roadmaps

  Success story

  Before the build-vs-buy call, not after

  Most advisory engagements start with the same question: is this the right problem, and is the proposed fix proportionate to it? That means going through the architecture, the team and the vendor claims directly — not taking a summary at face value — before recommending a build-vs-buy call or a roadmap. Leadership gets a plan it can act on, backed by reasoning it can check.