Insights
Why (and why not) train a language model from scratch
Learn about why (and why not to) train a language model from scratch - plus, what would be required to implement in practice
Joining the 5% Inner Circle: Moving Beyond the AI Failure Narrative
Discussing what organisations must do to join the small group that succeeds in AI adoption - only 5%, according to MIT research from 2025.
What Metrics Matter for AI Agent Reliability and Performance
What are the key metrics and measurement strategies that organisations should monitor to ensure their AI agents behave reliably, safely, and usefully?
Why Prefect is A Perfect Pick for AI Agent Monitoring
Exploring how Prefect (a workflow orchestration tool) fits naturally into AI agent monitoring and enables tracing, alerting and observability of agent operations.
Protecting Enterprise Data in the MCP Era
Covering the data governance, security and privacy challenges that arise when connecting AI agents to enterprise data via Model Context Protocol (MCP), as well as how to mitigate risks.
How MCP Transform Enterprise Intelligence
How MCP enables AI systems to make insights more actionable, integrated and contextually aware, based on relevant enterprise data.
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.
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.
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.
The DNA of an AI Agent
A detailed look at AI agents and how they work, including components, reasoning, autonomy and how AI agents are shifting the paradigm for software design.
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.
Unlocking AI's Potential: The C-Suite Blueprint for Responsible Innovation
A C‑level framework to adopting AI responsibly, balancing innovation with risk, oversight and scalability to achieve fast and ethically scale solutions.
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.