Marshall Tech
custom software no codeaiAPIArchitectureAI Readiness

The businesses that will benefit most from AI in the next 2-3 years are the ones with clean API layers today. If your business logic is locked inside a UI, every AI tool needs custom integration. APIs make AI adoption trivial.

On API-first architecture enabling AI

Nick Hugh

Founder, Principal AI Engineer & Fractional CTO, Marshall Tech

Nick Hugh, Principal AI Engineer & Fractional CTO at Marshall Tech, Sydney

Last updated:

Related resources

Move from this quote into the person, proof, and longer explanation behind it.

Expert

Nick Hugh

Nick Hugh, Principal AI Engineer & Fractional CTO at Marshall Tech, Sydney

Updated 10 Aug 2026

Open resource

Insight

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 resource

Insight

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 resource

Case study

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.

Updated 26 Feb 2026

Open resource

Insight

AI Readiness Assessment Checklist for Australian Businesses

An AI readiness assessment evaluates four dimensions: data quality and accessibility, process maturity, team capability, and infrastructure readiness. Most Australian businesses score 40–60% on their first assessment. The gaps aren't blockers. They're the starting point for a practical implementation roadmap.

Updated 10 Aug 2026

Open resource

Insight

Data Hygiene Guide: Getting Your Data AI-Ready

Data hygiene is the practice of ensuring your business data is accurate, consistent, complete, and accessible. It's the prerequisite for AI implementation, reliable automation, and trustworthy reporting. Businesses with poor data hygiene waste 20–30% of employee time on manual data wrangling and get unreliable results from any AI systems or workflow automation tools they deploy.

Updated 10 Aug 2026

Open resource