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
Why Your Organisation Needs Agent Lifecycle Management
Explore why organisations should adopt full lifecycle management for AI agents for monitoring, governing, versioning and maintaining in business systems.
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.
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.
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.
AI Agents and the Three Lines of Defence: A Banking Inspired Approach
This blog provides an AI governance framework for highly-regulated industries, using the banking industry as inspiration.
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.
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.
Key Safety Features for Creating AI-Enabled Products with Amazon Bedrock
Explore Amazon Bedrock's essential safety features for responsible AI deployment. Learn how guardrails like content filters, denied topics, and contextual grounding checks mitigate risks in AI-enabled products. Discover how these features prevent incidents like chatbot jailbreaking and misinformation, ensuring compliance and protecting brand reputation. Ideal for technology decision-makers seeking to innovate with AI while prioritising safety and ethics in an era of increasing AI capabilities and public scrutiny.
AI-Powered Software Development: Reimagining Innovation in the Enterprise and Beyond
Explore how AI-powered software development is revolutionising innovation in this insightful blog. Discover how AI is democratising software creation, lowering barriers to entry, and enabling rapid prototyping, which accelerates the innovation cycle. Learn how enterprises can leverage AI to foster agile transformation, cross-functional collaboration, and continuous innovation. The blog also delves into the broader economic impact, including the rise of citizen developers, new business models, and global economic equalisation. Uncover the challenges and considerations as AI reshapes the software development landscape and the future of entrepreneurship.
Harnessing AI for Business Success: Tackling Data Quality with Data Mesh and Data Fabric Principles
Unlock the transformative potential of AI for your business by tackling data quality challenges head-on. In our latest blog, we delve into how data mesh and data fabric principles can enhance data governance, ensuring your AI models are built on reliable, high-quality data. Learn how building value-aligned data products and implementing automated testing frameworks can drive significant business value. Discover the importance of strong data culture, effective metadata management, and innovative techniques like Change Data Capture (CDC) in maintaining data integrity. Embrace the future of AI with robust data strategies that foster trust and drive sustainable growth. Read now to explore the critical steps for leveraging AI to its fullest potential.