A neurodivergent solutions architect built an AI system using Amazon Quick that automates email triage, prioritization, and task management to compensate for executive function gaps created by AuDHD. Rather than asking users to maintain organizational systems, the AI observes, classifies, and acts autonomously while the user provides only initial input. For neurodivergent professionals—roughly 15–20 percent of the UK adult population—this model addresses a gap in productivity tooling that typically assumes neurotypical cognitive patterns.
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A solutions architect with AuDHD (co-occurring autism and ADHD) built an AI-powered workflow system running on Amazon Quick desktop application that automates email triage, task prioritization, and follow-up management. The system classifies emails against rules, presents prioritized briefings, tracks elapsed time on tasks, and maintains full conversation context—requiring only the push of a button to start each morning.
Why it matters
Approximately 15–20 percent of the UK adult population is neurodivergent, yet most AI productivity tools assume neurotypical brains. For neurodivergent professionals, email and task management consume disproportionate cognitive energy. This system compensates for specific gaps—time blindness, decision paralysis, context-switching difficulty—that make conventional productivity tools fail. The author notes that for many neurodivergent people, AI can mean the difference between functioning and not functioning, whereas neurotypical users typically deploy AI to go from good to great.
What to watch
The system uses Amazon Bedrock for inference and a custom Model Context Protocol (MCP) server built with Kiro (an AI-powered IDE) that connects to Outlook, calendar, and Asana. Rules are stored as configurable markdown files, so refinements take effect immediately without redeployment. The design prioritizes near-zero cognitive cost: users authenticate once per session and the system handles everything else automatically.
The article presents AI not as an enhancement tool for already-functional workflows, but as a foundational accessibility mechanism for neurodivergent professionals. The author's core insight—that traditional productivity tools fail because they require sustained organizational effort that neurodivergent brains cannot maintain—reframes why most Asana, Notion, and Todoist implementations end in abandonment. The system compensates by inverting the cognitive demand: instead of the user maintaining the tool, the tool maintains itself, requiring only an initial trigger each morning.
The technical architecture reflects this principle. By embedding triage rules and priority logic as configurable markdown files read fresh each session, the system allows rule refinement without redeployment—critical for a user whose needs may shift. Amazon Bedrock's inference layer means the system can improve as foundation models advance without workflow disruption. The Quick desktop application's persistent conversation memory directly addresses the ADHD symptom of context-loss during task-switching while simultaneously serving the autism-side need for complete context before re-engagement.
The author frames this as a difference in use case: neurotypical users deploy AI to optimize from good to great, while neurodivergent users may deploy it to reach baseline functioning. With approximately 15–20 percent of the UK adult population neurodivergent, and most productivity tooling designed implicitly for neurotypical cognitive patterns, this signals a gap in how software accessibility is currently understood and built.
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