SEO, GEO and AEO start with an answerable website
Make services clear, keep structured data honest and improve discovery around real buyer questions rather than acronym-led shortcuts.
Insights / Notes from the work
Things we’ve learned.
Worth passing on.
Practical notes on building software, modernising WordPress and putting AI to work. The decisions, details and lessons behind the delivery.
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Release checks should follow real user journeys
Go beyond a successful build with observable checks for enquiries, editing, integrations, public metadata and recovery.
Stop one saved edit from silently overwriting another
Use record versions, conditional writes and a clear conflict interface to preserve work when people edit the same record.
Publish the article and its metadata as one piece of work
Keep article content, dates, excerpts, feeds and social previews consistent with a compact publishing and generated-page review.
Site search should explain what to do next
Use visitor language, useful result descriptions and honest empty states to make website search support real content discovery.
A development retainer needs an operating model
Agree how maintenance, incidents and new work enter the queue, who sets priorities and how the relationship measures useful progress.
Customer portals: permissions before dashboards
Define record ownership and access by task, then test the boundaries across pages, exports and background work.
Design the ACF content model before the page builder
Separate reusable business facts from layout choices, define shared values and plan content-model changes as data migrations.
Build data imports that can be run twice
Use stable identities, change previews and durable run records so corrected files and interrupted imports have predictable recovery paths.
Service schema without invented prices or reviews
Connect your organisation, pages and services using accurate structured data that agrees with the offer visitors can actually read.
Test the booking calendar without a mouse
Review focus, date restrictions, recovery and assistive technology behaviour across the complete booking-date selection journey.
AI document workflows need an unanswered state
Make missing evidence, conflicting sources and decisions requiring human review explicit parts of an AI document workflow.
What to check between local WordPress and the live host
Identify the environment differences that affect a release, use safe local substitutes and verify the remaining assumptions in staging.
Audit WordPress plugins by responsibility, not count
Map the capabilities, dependencies and data behind each plugin before deciding what to keep, consolidate or remove.
Build the redirect map before the website launch
Inventory old URLs, match their purpose to useful destinations and verify the deployed redirects as part of a website redesign.
API rate limits are a product state
Plan bounded retries, work priorities and honest progress messages so an integration remains usable when a provider limits requests.
Before replacing a spreadsheet, understand its exceptions
Model the decisions, ownership and unusual cases behind an operational spreadsheet before turning it into an internal application.
Booking analytics starts with clear event meanings
Distinguish property views, date searches and checkout launches so a booking funnel reports what the website can actually observe.
Technical discovery should end with a decision
Turn a broad development brief into answerable questions, test the riskiest assumption and produce a recommendation the business can act on.
Property search filters that help guests decide
Define meaningful property filters, preserve search choices and make empty results useful without confusing technical failures with no availability.
A successful contact form should mean a received enquiry
Follow the submission beyond the thank-you screen: validation, durable acceptance, notifications and recovery when a downstream service fails.
Start a legacy rebuild with one useful slice
Choose a bounded workflow, capture the behaviour that matters and use the first release to learn before committing to a full replacement.
Your WordPress backup needs a restore rehearsal
Check that a backup can recover a working site in an isolated environment, with clear ownership, safe integrations and meaningful business checks.
Webhooks that survive duplicate deliveries
Design event identity, durable processing and reconciliation so a repeated webhook does not repeat the business action behind it.
A development handover that survives launch
Give your agency a delivery pack it can use: service ownership, repeatable setup, recovery instructions and a rehearsal of the next ordinary change.
Property photography without the mobile wait
Build property galleries that show the stay clearly without delaying the first useful view, from image sizing to the ongoing editorial workflow.
SuperControl availability: cache the search, explain the failure
Separate property content from live availability, design safe cache keys and distinguish an unavailable search from a genuinely empty result.
Native WordPress around SuperControl: live availability without a widget skin
Sync property data, ask SuperControl for live dates, and hand off to checkout with a calendar that belongs to the theme — not an embed.
Keep SuperControl, rebuild the guest experience
Dated templates and bolted-on calendars are not a reason to rip out the PMS. Keep SuperControl. Rebuild the public site around it.
Using AI on a WordPress modernisation, not as the rebuild
Use AI to inventory plugins, map ACF groups and draft a cut list. People still do the rebuild. Generators do not reduce plugin debt.
WordPress AI that belongs in production
Most WordPress AI plugins are demos. Production AI on WordPress runs off the public request, behind a human, with a cost cap and an owner.
Hardening WordPress MCP before you give an agent write access
Treat WordPress MCP like WP-CLI: a small role, a small catalogue, staging first, and logs. Prompts are not access control.
What WordPress MCP is actually for
Model Context Protocol is a tool interface for WordPress, not a chatbot plugin. Use it for inspection and local ops, not overnight publishing.
Link and sync ACF fields across pages (without environment sync)
Share one ACF value across selected pages with a source of truth. That is not the same as syncing WordPress between environments.
The ACF trap: dragging a field does not move the data
Renaming or moving an ACF field leaves stored values behind. Treat schema changes as migrations with preview, transaction and rollback.
AI DevTool Stack for Small Teams
A lean, high-impact AI development stack for small teams shipping fast without losing quality.
From Prototype to Production AI Coding Assistant
What changes between demo and production when building an internal AI coding assistant.
Shipping Claude and OpenAI Together
A practical multi-provider architecture for quality fallback, cost control, and vendor resilience.
Secure Tool Use for AI Agents
How to lock down tool execution paths so AI agents remain useful without introducing avoidable risk.
Cost Controls for Multi-Model AI Products
Simple controls for model routing, token budgets, and caching that protect margin as usage scales.
Testing Strategies for AI-Generated Code
A layered testing approach that keeps AI-generated code fast to ship and safe to run.
Observability for AI Coding Agents
The telemetry stack you need to debug agent behaviour, tool calls, and regression risks in production.
Prompt Versioning for Engineering Teams
Treat prompts as production artefacts with versioning, tests, rollback, and ownership.
Hybrid Human-AI Code Review Patterns
A review model that uses AI for speed and humans for judgment, security, and architectural quality.
AI Coding Jobs and Skills in 2026
What companies now expect from AI-assisted engineers, and how teams can hire for production impact.
AI Coding Tools Landscape 2026
How to evaluate AI coding tools by reliability, cost, governance, and developer experience.
Claude Code Workflows That Scale
A practical workflow for using Claude in real engineering teams, from task shaping to safe rollout.
AI Engineering Design Patterns
In the ever-evolving landscape of artificial intelligence, AI engineering design patterns have emerged as essential tools for developers designing rob...
AI Engineering Best Practices
In the rapidly evolving world of artificial intelligence, engineering teams are constantly challenged to keep pace with the latest advancements. Under...
Advanced AI System Patterns
In the rapidly evolving landscape of artificial intelligence, understanding advanced AI system patterns is crucial for developing robust, scalable, an...
Cost-effective Model Selection
Selecting the right AI model is a critical step in developing intelligent systems, but it often presents a significant cost challenge. With the rapid...
Error Recovery Patterns
In an era dominated by intelligent systems, ensuring robustness in AI applications is critical. Error recovery patterns provide a structured approach...
Streaming Responses Optimisation
In the rapidly evolving landscape of AI engineering, optimising streaming responses is becoming crucial. With real-time data processing and delivery a...
Context Window Management
In the realm of artificial intelligence, managing the context window is pivotal for the success of AI agents. Understanding how to efficiently handle...
Vector Database Selection Guide
Choosing the right vector database is crucial for AI-driven applications as we move towards 2025/2026. With advancements in AI technologies, the abili...
Production Debugging Techniques
In the fast-paced world of AI engineering and production systems, efficient debugging techniques are crucial for maintaining robust and reliable appli...
Model Evaluation Frameworks
In the ever-evolving world of artificial intelligence, selecting the right model evaluation framework is crucial for achieving optimal performance. Mo...
Understanding and Deploying Production AI Systems
As the world of artificial intelligence continues to evolve, deploying AI systems in production environments has become a critical skill for engineers...
Rapid AI Prototyping with LangChain, Supabase, and FastAPI
How we go from idea to working AI prototype in days, not weeks — using LangChain, Supabase, and FastAPI to turn experiments into production-grade systems.
Building Production-Ready AI Agents
Essential patterns for deploying AI agents that actually work in production environments.
RAG vs Fine-tuning: When to Use What
A practical guide to choosing between retrieval-augmented generation and model fine-tuning.
Prompt Engineering Best Practices
How to write prompts that actually work in production environments.
AI Cost Optimisation Strategies
How to build AI applications that deliver value without breaking the budget.
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