Insights
A Primer on Generative Engine Optimisation
In an earlier companion piece we shared that agentic commerce has arrived, that buyers and their agents are increasingly beginning their journeys inside AI assistants, and that the answer an assistant returns is becoming the new shelf.
Agentic Commerce Comes of Age: What It Is and Why it Matters
For the better part of three decades, the governing metaphor of digital commerce has been the shopfront. A business builds a website, draws visitors to it through search and advertising, and does what it can to convert their attention into a purchase once they arrive.
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.
The Open Model Moment: Why Every Enterprise Needs a Multi-Model Strategy
The open model shift is accelerating, and three moves in the last fortnight show exactly where this is heading. Stop paying someone else's margin to think for you and adopt a multi-model strategy.
10 Ways to Build Future-Proofed AI Workflows
Several Azure OpenAI model versions will soon be retired - here’s 10 ways to build future-proofed LLM workflows to prevent migration risks when models are deprecated.
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 Small Language Models Are the Key to Agent Independence
Open source small language models offer organisations a strategic path to building AI agents that avoid vendor lock-in, enable explainability for regulated industries and provide operational independence from the three dominant LLM providers.
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.
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.
