From zero organic education presence to a fully interlinked knowledge base with glossary, learning paths, and automated content scaling — in weeks, not months.
TradeAlgo processes 50 billion market events daily across 250+ data sources. They have institutional-grade tools — dark pool scanners, AI-powered alerts, options flow analytics. But they had no content strategy. Every new user came through paid ads. There was no organic moat, no SEO authority, and no way for potential customers to discover the platform through search.
The competitors — Investopedia, crypto.com/university, Tastytrade — had thousands of articles ranking for high-intent trading queries. TradeAlgo was invisible to the exact audience who would pay for their tools: people actively searching for trading education.
I designed a hub-and-spoke topic cluster model across 16 categories — options, crypto, AI trading, futures, day trading, technical analysis, and more. Each cluster has a pillar page with supporting spoke articles that interlink. This is the same model used by HubSpot, Ahrefs, and every serious SEO operation.
Each article is 2,500–4,000+ words with structured H2/H3 hierarchy, comparison tables, FAQ sections, and expert authorship. Every article includes optimized YAML frontmatter (title, meta description, target keywords, Schema.org data) and 729 purpose-built SVG illustrations — no stock photos.
Content is generated using AI-assisted workflows with editorial quality control, then processed through a custom pipeline that handles SEO markup, image generation, glossary linking, and static site generation automatically.
I built a comprehensive trading glossary with dedicated pages for each term, Schema.org DefinedTerm markup, related term cross-links, and algorithmically scored article recommendations. This creates 299 new long-tail ranking opportunities ("what is [term]" searches) while strengthening the entire site's topical authority.
I wrote a custom Node.js script that scans every article for glossary term matches and bakes hyperlinks directly into the markdown source files. First occurrence only, max 25 per article, longer phrases matched first, with smart filtering to skip generic terms. This creates a dense web of contextual internal links that distributes PageRank across the entire site.
The links are baked in at the source level — zero runtime cost, fully visible to developers, and immediately crawlable by search engines through static generation.
To increase session depth and time on site, I created curated learning paths that sequence articles into structured courses. Three paths launched (Dark Pool Mastery, Options Flow Pro, AI-Powered Trading) with dedicated course pages, Schema.org Course markup, and curriculum views showing article progression.
Users following a path visit 8–12 articles per session instead of bouncing after one. This dramatically improves engagement metrics that Google uses for ranking signals.
This wasn't just content strategy — I built the entire frontend. V2 landing page with search, topic filtering, and learning path showcase. Individual article pages with responsive layouts, glossary link styling, and Schema.org markup. Glossary index and term pages. Learning path index and course pages. Education dropdown in the site header. Everything built in Next.js 16 App Router with Tailwind CSS, statically generated for maximum performance.
Full redesign with hero, search, sticky topic filter, learning paths, glossary preview, YouTube embed, community proof, platform preview, FAQ, and conversion CTAs.
Dynamic routes with schema markup, custom SVGs, FAQ sections, related articles, glossary cross-links, and responsive layouts.
A-Z index page with letter nav + category filters. Individual term pages with definitions, related terms, scored article recommendations.
3 curated courses (33 articles) with dedicated path pages, curriculum views, sidebar navigation, and Course schema markup.
Custom Node.js tooling that bakes 6,020 contextual glossary links into article source files with smart filtering and quality controls.
Automated article generation pipeline producing 10-20 articles/day. SVG illustration generator. Glossary link baking script. HTML export tools.
Purpose-built vector graphics for articles — infographics, comparison charts, workflow diagrams. No stock photos. Optimized for web performance.
Dedicated marketing page for the AI briefing product with feature showcase, pricing tiers, sample briefing preview, and conversion flow.
The infrastructure I built isn't static — it's an engine. An automated pipeline generates 10–20 new SEO-optimized articles per day, complete with custom illustrations, glossary linking, and schema markup. At that rate, the library scales from 362 to 1,000+ articles within weeks. Every new article strengthens the entire network through internal linking and topical authority.
Each new article auto-generates glossary links, SVGs, and schema markup through the pipeline
Next.js App Router, React server/client components, TypeScript, Tailwind CSS, Node.js tooling. I write the code, not just the brief.
Schema.org structured data, hub-and-spoke clusters, internal linking automation, canonical URLs, static generation for crawlability.
AI-assisted content pipelines with custom quality controls. Automated article generation, SVG creation, glossary linking — 10-20 articles/day.
729 unique SVG illustrations. Landing pages, marketing pages, and UI components designed to match brand systems. No templates.
Keyword research, competitive analysis, content architecture, editorial guidelines. Strategy that turns into shipped product — not slide decks.
Custom scripts for content processing, link injection, export generation, and pipeline automation. I build tools that make the work scale.
I build SEO content systems, full-stack web applications, and AI-powered growth infrastructure. If you need results backed by real engineering — let's talk.
Anthony Scott — Blueprint Media
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