Deploying AI tools without a strategy
produces expensive experiments.
Discovery, roadmap, implementation, and team enablement for AI-driven workflow automation, document processing, and intelligent agents — with someone who understands both the technology and how engineering and QA teams actually operate.
Most AI implementations fail
at the process layer, not the tech.
The technology for AI workflow automation, document processing, and intelligent agents is genuinely capable. The failure mode isn't the tools — it's deploying them into workflows that aren't ready, without understanding the edge cases, without governance, without a way to measure whether they're actually working.
The companies that get value from AI implementation are the ones that treat it like any other engineering initiative: start with the problem, define what success looks like, build incrementally, and measure the outcomes. Not "we deployed GPT-4 and pointed it at our documents."
This service brings that discipline to AI implementation — without the enterprise consulting overhead. Stuart understands AI tooling from a practitioner perspective and brings the quality engineering mindset to implementation: what can go wrong, how you measure output quality, and how you build something the team can govern without a permanent consultant dependency.
Three areas where AI implementation delivers.
Workflow agents
AI agents that automate repetitive knowledge work — triage, classification, routing, summarisation. Built on your existing tools and data, not a black-box SaaS layer.
Document automation
AI-assisted document processing: extraction, comparison, generation, and review. Particularly relevant in regulated industries — insurance, legal, financial services — where document volume is high and accuracy matters.
QA + AI integration
Using AI to augment QA workflows: test case generation from requirements, risk-based prioritisation, defect classification, and LLM output quality testing. The intersection of Stuart's two specialist domains.
Four phases. Discovery to enablement.
Discovery
Map current workflows, identify the highest-ROI automation opportunities, assess data and tool readiness. Output: prioritised opportunity list with effort/value assessment.
Roadmap
Define the implementation sequence, tooling choices, success metrics, and governance approach. Output: a roadmap document your team can execute against, with or without Stuart.
Implementation
Build the first agreed use case end-to-end. Integrate with existing tools and data. Measure output quality. Iterate until outputs are reliable and the team trusts the results.
Enablement
Document the implementation, run team sessions, establish monitoring and governance. Objective: the team can run, evaluate, and extend the implementation without Stuart.
Not a strategy deck.
Not a vendor recommendation. Not a prototype.
The output is a working implementation — running in your environment, integrated with your tools, producing measurable results — not a presentation about AI possibilities or a proof-of-concept that requires an enterprise team to productionise.
✓ You've evaluated AI tools but don't know how to implement them safely
✓ You're in a regulated industry with document-heavy processes
✓ You want to know what can go wrong before you find out in production
✓ You need AI governance built in from the start
Day rate or fixed-price. Scoped after discovery.
For advisory or time-and-materials engagements. Rate varies by complexity and specialist depth required.
Preferred for defined implementation projects. Fixed scope, fixed price, confirmed before work begins.