SportsBlock: Custom Platform Build & AI Integration
SportsBlock needed a fan engagement platform that could handle real-time sports data, AI-curated content, and social features at scale. Marshall Tech delivered the MVP in 6 weeks with a custom backend and Next.js frontend, then scaled to 50k+ monthly active users, all running on under $500/month in infrastructure.
Time to MVP
5x faster
Build cost
87% reduction
Monthly active users
Scale achieved
Infrastructure cost
90% lower
01 — Challenge
The problem
SportsBlock had a vision for a next-generation fan engagement platform but no technical team. A previous agency engagement had produced wireframes and a $300k quote for an 8-month build. The founders needed a working product in the market fast enough to secure their seed round, with a budget under $50k.
02 — Approach
The decision
Marshall Tech proposed a custom build: a scalable backend handling real-time data feeds, user management, and content storage, paired with Next.js for a fast, SEO-friendly frontend. AI content curation was built as custom endpoints using Claude, processing incoming sports data and generating personalised feed content. The $300k agency quote became a $40k delivered product.
03 — Results
Measured outcomes
| Metric | Before | After | Impact |
|---|---|---|---|
| Time to MVP | 8 months (quoted) | 6 weeks | 5x faster |
| Build cost | $300k (quoted) | $40k | 87% reduction |
| Monthly active users | 0 | 50k+ | Scale achieved |
| Infrastructure cost | Est. $3k–5k/month | <$500/month | 90% lower |
| Content curation | Manual | AI-automated | 10x throughput |
05 — Stack
Technology used
Facing a similar challenge?
Book a 30-minute discovery call to discuss your situation.
Last updated:
Related resources
Move from this proof set into the service mechanics, expert authority, and supporting content.
Expert
Nick Hugh
Nick Hugh, Principal AI Engineer & Fractional CTO at Marshall Tech, Sydney
Updated 10 Aug 2026
Open resourceInsight
MCP Explained: What Model Context Protocol Means for Your Business
Model Context Protocol (MCP) is an open standard that lets AI agents connect to external tools, databases, and APIs through a universal interface. Think of it as USB for AI: one protocol, any tool, any model. MCP eliminates vendor lock-in and enables businesses to build tool integrations once and use them across any AI platform.
Updated 10 Aug 2026
Open resourceInsight
Build vs Buy: A Decision Framework for Business Technology
Build when the capability is your competitive advantage, when no off-the-shelf solution fits your workflow, or when platform lock-in is an unacceptable risk. Buy when the function is commodity (accounting, email, project management), when time-to-value matters more than customisation, or when the vendor's R&D investment exceeds what you'd spend building. Most businesses should build 10–20% of their stack and buy the rest.
Updated 10 Aug 2026
Open resourceInsight
No-Code vs Custom Code: When to Choose Each in 2026
Choose no-code for internal tools, MVPs, and workflows where speed-to-market matters more than customisation. Choose custom code when you need complex business logic, high performance, data control, or deep integrations. Most growing businesses use both: no-code for rapid prototyping and internal tools, custom code for customer-facing products.
Updated 10 Aug 2026
Open resourceInsight
API-First Architecture: Why It Matters for Growing Businesses
API-first architecture means designing your systems around APIs (application programming interfaces) before building user interfaces. This approach enables faster integration, easier AI adoption, better data flow between systems, and the flexibility to swap components without rebuilding everything. It's the foundation for scalable business technology.
Updated 10 Aug 2026
Open resourceService
AI consulting in Sydney
Marshall Tech works as an AI consultant in Sydney for small and growing businesses that want practical AI tied to operations. We assess readiness, select the right use case, build the workflow, and add guardrails so AI systems are reliable, measurable, and usable in production.
Updated 9 Apr 2026
Open resource