Insights
No Figma MCP? How We Still Built 350+ Figma-Accurate Screens With Kiro
For security reasons, we couldn’t use the Figma MCP - so, what then? A context engineering and spec-driven development story from a regulated financial services company, and what it taught us about keeping AI-generated frontend code under control.
What We Learned Testing Five Sensitive Data Detection Tools
We tested sensitive data detection tools Amazon Macie, Microsoft Purview and three open source tools on 18,049 documents to see how much sensitive data automated data classification really finds. Read for our recommendations and what a layered approach costs to run.
The missing foundations holding back AI in energy and utilities
AI adoption is accelerating across energy and utilities, yet many initiatives never progress beyond experimentation. Writing in Energy Manager Magazine, Mark Simpson explains why proprietary data, targeted use cases and explainable governance matter more than model capability when it comes to scaling AI in critical infrastructure.
The reality of sovereign AI: How to avoid dependency without building everything yourself
Ben Saunders, Co-Founder of WeBuild-AI, writes for Silicon Valleys Journal on why sovereign AI is about control and optionality, not building every model in-house, and how a multi-model control plane reduces vendor lock-in.
The real barrier to AI in financial services isn't regulation but trust
Ben Saunders, Co-Founder of WeBuild-AI, writes for The European Financial Review on why most financial services firms use AI in some form yet struggle to reach production, and why organisational confidence and accountability matter more than regulation alone.
Why the future of AI in banking depends on trust, not trials
Ben Saunders, Co-Founder of WeBuild-AI, contributes to The Fintech Times on why banks stall after proof of concept, and why scaling AI in banking is an operating-model and trust problem, not only a regulatory one.
The confidence gap holding back AI in financial services
Ben Saunders, Co-Founder of WeBuild-AI, writes for Finance Derivative on why financial institutions struggle to turn AI pilots into measurable outcomes, and why internal confidence, governance, and stakeholder alignment matter as much as compliance.
From disparate spreadsheets to a secure, scalable AI platform in 4 months
Principal Consultant, Josh, walks us through how the WeBuild-AI team built and shipped a scalable AI platform in 4 months that generates regulator-ready documents in under 90 minutes.
Open Source Models Are the Pathway to Sovereign AI
In this post we explore why open source models have crossed the threshold for everyday enterprise use, why Anthropic's decision to open source its Agent Skills framework is a bigger deal than it might first appear, and why the organisations best placed to move quickly with AI are those thinking carefully about where their data lives and which model is appropriate for which task.
AI Sovereignty is Not a Place, It Is a Control Plane
Ask most boards what AI sovereignty means and you will get one of two answers. Either it is a UK region on a hyperscaler, or it is a box in a private data centre. Both answers share the same flaw: they treat sovereignty as a place. Pick the right location, the thinking goes, and the problem is solved.
The 10 Governance Domains Every Enterprise Must Address Before Deploying AI
Most enterprises are deploying AI faster than they can govern it and regulators are taking notice. This article introduces 10 governance domains that form a practical framework for enterprise AI deployment, covering data protection, sovereignty, regulatory compliance, security, content safety and more, backed by peer-reviewed data and with additional guidance aligned to the EU AI Act and UK regulatory landscape.
Data and AI Governance Across 10 Domains: A Technical Framework for UK and European Enterprises
Download WeBuild-AI's technical whitepaper covering the 10 governance domains every enterprise must address before deploying AI, aligned to the EU AI Act and UK regulatory landscape.
Why Context Graphs Will Define AI Success in Regulated Industries
Highly-regulated industries require data relationships, provenance and context, provided through context graphs that supplement RAG, vector databases and MCP.
How to build AI governance that enables delivery instead of blocking it
Build collaborative and flexible AI governance frameworks that enable rapid innovation, production and delivery, and ensure your AI plans are on-time, on-budget and compliant in 2026.
Practical Security Guardrails for Large Language Models
Actionable techniques to ensure secure LLM deployments that balance innovation with function, from using prompt injection protection to ethical use and access controls.
