Insights
Cutting Code AFK. How Ralph Wiggum can do the work for you
So it finally happened, I was pulled into a meeting and told to replace myself with AI.” Read Elena’s full breakdown on how to use the Ralph Wiggum Loop to speed up development, including learnings and a step-by-step guide.
Metrics for AI projects - what your team feels versus what the board wants to see
Your team can feel the time an AI tool saves, while your board needs to see where that time actually went. Here’s what to measure, and what an invoice pipeline taught me about the difference.
The Knowledge Graph Tool Inside Our AI Accelerator
Why we paired Neo4j, knowledge graphs and the Model Context Protocol inside our AI Accelerator, and how the combination turns months of integration work into days.
Why a Multi-Model Strategy Is Now Mission Critical
When Anthropic withdrew Claude Fable 5 globally just three days after launch, following a US export control order, it proved that enterprise AI concentration risk is no longer just commercial but geopolitical. This article makes the case for a resilient multi-model strategy spread across providers and jurisdictions for enterprise AI.
From Figma to Functioning Frontend in Four Weeks
Discover how the WeBuild-AI team moved from Figma to functioning frontend in four weeks, including the full scope of multi-functioning tools, user testing and key learnings.
Build Foundational Enterprise AI Using Spec-Driven Development and Architecture Decision Records
From agile and waterfall methodologies to AI-native software development lifecycles - how to standardise spec-driven approaches to build AI-ready enterprises.
10 Steps to Scaling AI Coding Assistants in Your Dev Team
Using AI coding assistants can dramatically increase speed of development, but there’s still bottlenecks that will inhibit delivery. Read our 10 steps to avoid a backlog of unreviewed work that’s never shipped.
Five Workflow Patterns to Multiply Your Development Capacity with AI Coding Assistants
Multiply your software development capacity with AI coding assistants - here’s 5 workflows to get you started
The three infrastructure decisions that determine AI delivery speed in 2026
A step-by-step guide of three infrastructure decisions to speed up your AI delivery
Why your SDLC is slowing down AI delivery (and what to do about it)
Four changes that will adapt your SDLC to enable AI delivery, reduce bottlenecks and increase experimentation
Aligning Spec-Driven Development and Context Engineering For 2026
Are spec-driven development (SDD) and context engineering competing, or complementary - and how do we see that partnership working for 2026?
The SDLC in the Age of Agents: When Everything Happens at Once
How has the software development lifecycle changed in the age of AI agents? Read on to find out
The Context Switching Tax: Here's How To Avoid The Tax Using AI
Enterprise businesses often have fragmented systems with disparate information, and rely on highly skilled engineers to be information repositories. No more - read on to learn about how Model Context Protocol enables context switching at scale.
AI for innovation: creating a culture of experimentation
Our customers commonly struggle with the culture behind innovation - not just allowing, but encouraging, their brightest minds to explore and invent. Read on for our AI-native recommendations.
The WeBuild-AI Perspective on Context Engineering
Context Engineering promises dramatically better AI outcomes, yet the reality involves substantial trade-offs in token economics, latency, and MCP infrastructure investment that determine what's actually feasible at enterprise scale.
