Insights
What is Model Context Protocol and Why Should You Care?
Model Context Protocol (MCP) lets AI systems securely interface with enterprise data, breaking silos and embedding context into AI outputs. Read on to find out more.
Our Principles for Building Enterprise Grade Generative AI
The foundational principles WeBuild‑AI used for building our Pathway platform, from AI‑native design to guardrails, ethics and automation as code.
Establishing Gen-AI Muscle Memory in The Enterprise
Learn how enterprises can build GenAI capabilities into daily workflows through continuous practice, experimentation and organisational learning.
The Technical Blueprint for Enterprise Scale Generative AI
Explore the architecture, tools and processes needed to scale generative AI across enterprise environments efficiently and securely.
Five Fundamental Use Cases for Enterprise Generative AI
Discover five high-impact generative AI use cases that are transforming operations, customer experience, and decision-making in the enterprise.
The Evolution of Enterprise Apps in the Generative AI Era
Learn about how enterprise applications are evolving with GenAI to become more intelligent, adaptive and embedded into daily decision-making in business.
Why Your Enterprise Needs a Unified Approach To Generative AI
Discover why a strategic, enterprise-wide AI strategy is essential to deliver real value, with tangible support, security and usability across the business.
Red Teaming Large Language Models: A Critical Security Imperative
“Red teaming”, a military approach to providing structured challenges to plans, policies and assumptions, has some key uses in technology: from exposing vulnerabilities in LLMs to ensuring safe, secure, and ethical deployment at scale. Learn how we use “red teaming” here at WeBuild-AI.
5 Essential Best Practices for LLM Governance: A Framework for Success
Key practices for organising, monitoring and securing large language model systems in enterprise settings.
The UK Pension Revolution: From Industrial Pioneer to AI Innovation
Explore how the UK’s pension sector is embracing AI to modernise operations, improve service and meet evolving regulatory demands.
How We Built An AI Launchpad in Under 20 Days on Amazon Web Services
Co-founder of WeBuild-AI, Mark Simpson, shares how WeBuild‑AI built our “Pathway” launchpad using AWS and generative AI, completing over 200 deployments in 20 days to validate our product architecture.
Measuring Speed and Efficiency in LLMs
Explore key metrics and benchmarks to evaluate large language model performance, from latency to cost and enterprise-wide impact.
Embracing Model Diversity: Why Organisations Should Adopt Multiple Large Language Models
Learn why using multiple LLMs can enhance resilience, performance and innovation across enterprise AI applications.
Generative AI - With Great Power, Comes Even Greater Responsibility
Explore the essential steps for governing generative AI in this blog by Ben Saunders. As generative AI becomes a powerful tool for innovation, it's crucial to establish robust guardrails and controls to prevent unintended consequences. Learn about the potential risks of unrestricted AI use, including ethical and legal implications, and discover how to implement technical controls and governance frameworks to ensure responsible AI deployment. Stay ahead in the digital age by adopting effective governance strategies that balance innovation with accountability.
LLMOps on AWS: Mastering Large Language Model Operations with Amazon Bedrock
Explore how to operationalise LLMs using AWS tools, with best practices for scalability, observability and secure deployment.
