Practical AI systems, open research, and useful build work.
Context Switch is the build-house layer inside Mark Hay: a place for practical systems, public-good research, and product experiments that turn messy information into useful, reviewable outputs.
The hierarchy stays deliberate: Mark Hay is the public identity, Context Switch is the build-house label, and each product or research output keeps its own name and purpose.
A build-house layer by Mark Hay
Current focus
Reducing admin burden with practical AI
Cross-industry evidence-to-action work covering small-business automation patterns, AI governance for non-technical teams, and reusable implementation templates.
Why it exists
A build-house layer for useful systems work
Context Switch gives practical AI, automation, and research outputs a coherent home without turning the whole site into an AI consultancy brochure.
The role is simple: build useful things, document the judgement behind them, and publish reusable methods where sharing creates more value than hiding the work. It supports the broader markhay.net trust layer rather than replacing it.
Proof tracks
Three lanes, one operating thesis
Context Switch groups selected work by what it proves, not by how shiny it looks.
01
Public-good research
Free evidence-to-action packs that turn source-backed research into practical methods, templates, and implementation guidance.
Open track →02
Practical systems
Automation patterns, admin-burden reduction, and workflow tools for teams that need usefulness before theatre.
Open track →03
Selected builds
Focused products, utilities, and experiments that show the product-minded lane without becoming a second portfolio index.
Open track →Operating principles
No fake authority. No novelty theatre.
The work should be useful, reviewable, and honest about its limits.
- Useful before impressive
- Evidence before confident claims
- Human review where decisions matter
- Open outputs where public value is stronger than secrecy
- Product names stay primary; Context Switch stays the build-house layer
- No fake authority, especially in specialist or regulated domains
Selected outputs
A curated proof strip, not a second project catalogue
These examples show the type of work that fits the build-house thesis. The full browse surface remains Projects.
Public research
Evidence-to-action research
A public-good research programme focused on reducing admin burden, small-business automation patterns, and AI governance for non-technical teams.
View output →Live product
UK Shortlists
A live buying-guide product with category architecture, structured shortlist logic, and disciplined publishing automation.
View output →Product concept
AI Support
A practical support layer for Copilot, ChatGPT, Claude and Gemini adoption, prompt patterns, critique, and reusable instruction building.
View output →Tool suite
E-Commerce Utility Calculators
A compact calculator suite that turns messy commercial arithmetic into fast decision support.
View output →Hierarchy
Clear ownership, no brand clutter
01
Mark Hay
Public identity, trust layer, and main point of contact across the wider body of work.
02
Context Switch
Build-house layer for practical AI systems, open research, and selected systems work.
03
Products and research outputs
The actual proof: named tools, case studies, templates, reports, and public outputs.
Start with the public research track
The first programme focuses on reducing admin burden with practical AI, automation patterns, and governance that non-technical teams can actually use.