MCIOB Develops Own AI Software for SMEs Using AI

Why It Matters

Construction AI democratizes enterprise‑grade construction management, giving cost‑conscious SMEs access to tools previously reserved for large firms. It also proves that AI‑driven code generation can produce complex, production‑ready software without traditional development teams.

Key Takeaways

• Construction AI built with Claude Code, no prior coding experience
• Platform includes 60+ AI tools across 22 construction modules
• SaaS app runs 700k lines, 186 tables, 596 APIs
• Pricing $127/month per seat, affordable for SME contractors
• Secured 20 customers in two months; showcasing at Digital Construction Week

Pulse Analysis

The launch of Construction AI underscores how generative‑AI code assistants are reshaping software development for niche markets. Steve McKenna, a seasoned contractor with no formal programming background, leveraged Anthropic’s Claude Code to architect a multi‑tenant SaaS platform in under two months. The resulting system comprises more than 700,000 lines of code, 186 secured database tables and 596 API endpoints, yet it was assembled through iterative prompts rather than traditional engineering cycles. This case illustrates that sophisticated, production‑grade applications can now be built by domain experts without hiring full‑stack teams.

For the construction sector, the product fills a glaring gap between heavyweight platforms such as Procore and Aconex and the budget constraints of small‑to‑medium contractors. Priced at roughly $127 per user per month, Construction AI offers 60+ AI‑enhanced tools across 22 modules—including tender analysis, cost control and programme tracking—at a fraction of enterprise licences. Within two months the service attracted 20 paying firms and secured a showcase slot at Digital Construction Week, signalling rapid market validation and a growing appetite for AI‑driven efficiency among SMEs.

Looking ahead, the platform’s modular architecture and embedded knowledge base position it for rapid feature expansion, especially in tendering and estimating workflows. As more SMEs adopt AI‑centric tools, vendors that can deliver industry‑specific data models and maintain regulatory compliance will gain a competitive edge. McKenna’s approach also raises broader questions about the sustainability of AI‑generated code, support models, and security standards. Nonetheless, Construction AI demonstrates that AI‑assisted development can democratize access to enterprise‑level technology, potentially reshaping the construction software landscape over the next few years.