Systemic fragmentation.
No single source of truth existed for high-tier asset metadata. The market was visibly waiting for one, but no one had built it.
Wiki
A content-first knowledge platform that turned the data fragmentation of a $5B+ market into a global traffic engine.
Europe
5.35M
Monthly visitors (80% ecosystem referral)
909.82K
Monthly organic search visitors
24,000+
Unique asset entries
< 100ms
Global API latency
Client name withheld under NDA. Metrics verified through joint post-launch review.
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Summary
The CS2 digital asset economy is a market with capitalisation north of $5 billion — and historically, the information layer underneath it was fragmented across half a dozen sources, none of them definitive, most of them slow or visually inconsistent. A market leader operating inside this economy reached a point where its legacy wiki was actively constraining the business: a rigid admin interface, an overloaded UI distracting users from the content itself, no consistent visual standard. We were brought in to architect an autonomous, high-performance knowledge platform — what the client called the "single source of truth" — that could serve millions of users at sub-100ms latency and route organic traffic back to the core ecosystem. The result is a headless, content-first platform now serving 5.35 million monthly visitors and generating close to a million in monthly organic search traffic on its own.
Request
The brief was specific: a structured, scalable wiki-catalogue of 24,000+ unique assets, on an autonomous engine, with its own domain, designed for organic search traffic that would feed back into the core ecosystem. Three implicit constraints made the project harder than that headline suggests:
The new platform had to be substantially more performant than the legacy one — sub-100ms globally, not just regionally
It had to operate on 10–15% of the cloud resources of comparable platforms in the ecosystem — performance and cost discipline at the same time
The content management workflow had to scale to a team of editors and translators across 29+ locales, without the admin bottlenecks that had constrained the previous version
Challenge
Five constraints shaped the architecture.
UI distraction.
The legacy interface had drifted toward monetisation-driven layouts that pulled attention away from the content. Bounce rates reflected it.
Administrative rigidity.
The legacy admin panel had been built for a smaller catalogue and a smaller team. Scaling it to 24,000+ entries and dozens of editors was not a feature request — it was a rewrite.
Visual inconsistency.
Default game asset APIs returned low-resolution images, often defaced by user stickers. For a platform claiming to be the visual authority on these assets, that was a non-starter.
Infrastructure efficiency.
The new system had to achieve peak global performance while consuming only 10–15% of the cloud resources of the primary platform. Performance optimisation and cost discipline as the same problem.
Feature scope
Core wiki catalog
Main pages, category management, and high-fidelity asset detail pages
Advanced discovery engine
Custom-built search with typo tolerance and fuzzy matching
Headless content management
Modular CMS with role-based access for Superadmins, Editors, and Translators
Visual asset pipeline
Semi-automated image processing for 2K/4K sticker-free assets
Ecosystem integration
SSO with the core platform and SEO architecture from the foundation
Tech stack
Application:
Next.js + TypeScript (frontend), Nest.js (backend), Node.js
Styling:
Tailwind 4.0
Infrastructure:
AWS (EC2, S3), PostgreSQL 17.5 (RDS), Valkey 8.0.1 (ElastiCache), Terraform, GitLab CI/CD
QA:
Manual QA on isolated staging and production smokes, Postman and Swagger for API contract tests, JMeter for stress and load testing, Playwright + JS for automation of stable functionality, Grafana for logs and monitoring
Key Features
Custom asset image pipeline
Official game APIs return low-resolution images, frequently with user-applied stickers obscuring the asset itself. We built a custom extraction pipeline that bypasses these limitations: it intercepts external asset responses, automatically filters for sticker-free items, and converts them to optimised WebP at 2K/4K resolution.
The result is visual consistency across the catalogue and significantly smaller file sizes than the alternative — both authority and speed from the same pipeline.
WebP reduces file size by 98%
WebP
PNG
176KB
8.6MB
Multi-source data aggregation
For 24,000+ assets in a volatile market, no single data source is reliable enough on its own — anomalies, downtime, and stale data all show up frequently in any one feed. We engineered a backend aggregation engine that combines three distinct stream types in real time: a primary pricing feed, a volume and fallback feed, and a metadata-and-translations feed. The engine normalises across them, surfaces outliers, and ensures the platform stays correct during market fluctuations. The user sees one consistent answer; the architecture handles the disagreement underneath.
Global multilingual search
The platform serves an international audience, so search had to work equally well in 29+ locales — including languages with Unicode, accents, and non-Latin characters. The search engine was architected for sub-100ms responses on multilingual queries with performance parity across regions. Editors in different languages aren't waiting on a US-centric query path.
Game files processing service
A separate Node.js service downloads game files directly and parses data from binary formats. This minimises the delay between a new asset appearing in the game and its appearance on the platform. The same pipeline handles conversion of 3D in-game models into 2D images suitable for the catalogue — letting the platform present new content before any of the third-party feeds have caught up.
Timeline
Weeks 1–4
Discovery and prototyping
UI/UX design, architectural blueprinting, ADRs for every consequential trade-off.
Weeks 5–12
MVP build
Backend engine, headless CMS, SSO integration.
Weeks 13–20
Global scaling
Multilingual search deployment and 4K media migration.
Results
Summary
The platform now operates as an autonomous traffic engine for the core ecosystem rather than a maintenance burden inside it.
Results in Numbers
5.35M
total monthly visits — 80% ecosystem referral
909.82K
monthly organic search visitors — generated entirely by the new platform
< 100ms
global API latency for international audiences
24,000+
unique asset entries maintained at scale
08:03
average session duration — measurable shift in user engagement
29+
languages supported through the multilingual pipeline
48
SEO authority score of 48 — built from a standing start within the engagement window
10–15%
cloud-resource footprint relative to comparable platforms in the ecosystem
Next steps
The project has moved into a continuous evolution phase. Current focus areas include:
Feature expansion — new interactive tools to deepen the browsing experience
Audience growth — extending SEO coverage to emerging global markets
UX refinement — iterative updates to the content delivery system to keep the platform the fastest and most informative resource in the ecosystem
F. A. Q.
Why headless for a content platform at this scale?
Headless architecture — in this case Next.js on the frontend, Nest.js on the backend — decouples content management from delivery, which is what lets the platform serve sub-100ms responses to millions of users while editors work in a separate, optimised admin interface. The trade-off is operational complexity (two systems to run, not one), and we don't recommend headless by default. For content-first platforms where read performance and editor velocity both matter, the trade-off is worth it.
How long does building a platform like this take?
The deployment cycle here was 20 weeks: 4 weeks of discovery and prototyping, 8 weeks of MVP engineering, 8 weeks of global scaling. That timeline reflects a defined scope — 24,000+ entries, 29+ locales, sub-100ms global latency. Projects with materially different scope or constraints land in different windows.
How does automated media processing affect SEO?
In two ways. First, asset quality directly affects user signals — bounce rate, dwell time, perceived authority — which Google has used as ranking inputs for years. High-fidelity, sticker-free 2K/4K assets read as authoritative. Second, optimised WebP delivery reduces page weight, which improves Core Web Vitals scores. The combination of both is what generated the organic traffic curve.
Can a wiki catalogue be cleanly integrated with an existing product ecosystem?
Yes, if it's designed for it from the start. The pattern that worked here was a separate domain — so the wiki could rank independently in search — combined with SSO and ecosystem integration on the user side. This let the platform operate as an autonomous traffic source while staying part of the core product experience for logged-in users.
How do you maintain data integrity across 24,000+ entries in a volatile market?
By treating no single data source as authoritative. The backend engine aggregates and normalises feeds across three stream types — pricing, volume, metadata — in real time, and surfaces anomalies rather than passing them through. When market data disagrees across sources, the platform has explicit logic for resolution rather than silently inheriting one feed's noise.
Can you share the client name?
The client name is withheld under NDA. Get in touch if you'd like to discuss the engagement in more detail under a mutual NDA — we can walk through the architecture and operational decisions at a level not appropriate for public material.
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