Technical SEO, reporting, and web development case studies.

Overview

KingdomBlue needed a stronger digital foundation across public-facing pages and product journeys. My work focused on making the site easier to discover, faster to use, and clearer to convert.

Challenge

The site had to support multiple services and product journeys while improving search visibility at the same time. SEO, UX, speed, and conversion paths had to be handled as one connected problem.

My Role

Developer / Designer, with day-to-day responsibility across technical SEO, landing pages, UI components, and conversion-supporting digital assets.

What I Did

Ran SEO reviews, improved page structure and content alignment, built landing pages and reusable UI pieces, worked on speed and Core Web Vitals, and added interactive functionality through APIs and WebSockets. I also applied AIEO thinking when shaping content for both traditional and AI-assisted search environments.

Tools Used

Technical Decisions

I prioritised crawlability, page speed, and landing page structure before layering on visual polish. Reusable front-end components kept the experience more consistent and made future updates easier to manage.

Outcome / Impact

The result was a stronger technical base for search visibility, cleaner user journeys, and a site experience better aligned with enquiry and conversion goals. It also connected day-to-day SEO work more closely to the actual build.

Why This Matters

This is the kind of work Technical SEO and Digital Performance roles require: diagnosing visibility problems, improving the front end, and tying optimisation back to business actions.

Overview

The KingdomBlue Life Report Generator is a Python and Flask application built to turn Search Console data into usable monthly and custom reports. It replaced one-off reporting with a repeatable workflow for analysis and stakeholder updates.

Challenge

Search performance data existed, but pulling it into a consistent reporting process was slow and fragmented. The system needed to fetch reliable data, preserve history, and generate outputs the team could actually use.

My Role

I designed and built the reporting workflow, from Search Console integration and KPI logic to the dashboard, data storage, and export flow.

What I Did

Built the Flask app, connected the Google Search Console API, created KPI and trend views, added custom date ranges, stored historical metrics in SQLite, and generated downloadable Excel reports with optional scheduled email delivery.

Tools Used

Technical Decisions

Flask kept the system lightweight and fast to iterate on. I used SQLite for local historical analysis, separated the report engine from the web layer, and streamed Excel files in memory so reports could be delivered without depending on server-side file storage.

Outcome / Impact

The team got a reusable reporting tool instead of manual spreadsheet work. Search visibility trends, KPI movement, and period comparisons became easier to review, share, and act on.

Why This Matters

It shows the reporting side of digital performance: not just finding issues, but building the systems that help teams monitor, explain, and improve performance over time.

Overview

AURA was a product built on a Next.js frontend, FastAPI backend, Postgres, and Redis. It brought together search, signal detail pages, dashboards, watchlists, alerts, portfolio tools, and PDF reports so investors could move from market scanning to interpretation in one place.

Challenge

The product had to surface live market data, JSE-specific symbol enrichment, AI-style narrative outputs, and explainability layers without turning the interface into a wall of numbers. The real problem was not only access to data, but structuring it so users could understand what mattered first.

My Role

I focused on the product and interface layer: dashboard hierarchy, signal-detail clarity, search and discovery flows, report readability, and investor-facing presentation across data-heavy views.

What I Did

Mapped summary-to-detail journeys, shaped dashboard and terminal layouts, structured search and screener results, and helped frame investor-facing explanation blocks around Alpha Score, Trade Setup, Flow Signal, market narrative, and report outputs.

Tools Used

Technical Decisions

The product was split between a Next.js web app and FastAPI API so search, dashboards, reports, and signal engines could evolve independently. Redis caching reduced repeated market-data latency, JSE symbol mapping improved local search resolution, and the interface separated scan views from deep analysis to keep decision-making faster.

Outcome / Impact

The result was a clearer investment product with stronger discovery, cleaner signal interpretation, and report-ready views that could support repeated analysis rather than one-off screenshots. It positioned AURA as a platform experience, not only a collection of charts.

Why This Matters

This matters for Technical SEO and Digital Performance roles because it shows I can work beyond marketing pages. I can structure data-heavy products, think in systems, handle API-driven workflows, and turn complex information into something people can use.