AI Solutions Engineering

AI that fits the way a team actually works.

I work from the business problem outward. The goal is not to add AI for its own sake; it is to create a dependable system that reduces repeated work, gives people better visibility, and can be maintained after delivery.

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How I help

From need to usable system

Find the operational bottleneck before choosing a tool.
Translate a process into a clear workflow, build plan, and handoff.
Build and improve automations, internal tools, and applied AI systems.
Document the work so teams can operate and evolve it after rollout.

Selected work

Applied systems and delivery evidence

Public case studies are sanitized: they explain the problem, approach, and result without exposing client code or data.

Applied AI · Internal tool

AI content pipeline

A 12-step internal content-production system that turned a keyword brief into a review-ready document.

549 articles across 882 jobs; measured failure rate reduced from 25–40% to 4–8%.

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Systems delivery · Programme design

Content syndication programme

A new programme designed to make distribution work visible, repeatable, and easier to improve.

A public case study focused on what held up and what the original metrics did not prove.

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Automation design · Operations

Automation systems

A portfolio of operational workflows designed around a clear need, trigger, action, and measurable outcome.

80+ automations shipped across operational workflows.

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AI agent · Reporting workflow

Reporting agent

A reporting agent built to remove a recurring manual reporting process end to end.

One built agent; planned onboarding and offboarding workflows are intentionally not presented as shipped.

Read the public case study

View the GitHub portfolio