Enterprise AI, Applied in Production.

Principal Consultant, Josh, walks us through how the WeBuild-AI team built and shipped a scalable AI platform in 6 months that generates regulator-ready documents in under 90 minutes.

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.

Head of Platform Engineering Dan shares the detail on his role at WeBuild-AI, his career history and development, including an impressive number of languages and achievements.

For as long as retailers have sold online, the visitor on the other side of the screen has been a person. Someone to be attracted, reassured, nudged and, with luck, converted.

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.

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.

At an executive partner dinner a little while ago, the conversation drifted, as these conversations invariably now do, towards the question of what everyone is really spending on artificial intelligence. One of the firms around the table offered a figure that gave the rest of us pause.

Download WeBuild-AI's whitepaper on AI agents in the energy and electricity sector - types of AI agents, where they are applicable across the grid, how they work, what’s available now and what’s coming soon.

In January 2026, Ofgem published its consultation on a proposed AI Technical Sandbox - a structured, regulatory-led environment where energy sector participants can test and trial AI solutions under oversight. We responded in March 2026.

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.

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.

Discover three core AI use cases for private equity and venture capital firms and what it took to build them - automated competitor analysis, company research and LPA knowledge retrieval.

How to move your AI Agents from POC to production: a step-by-step guide, no matter your department or industry.

MIT research shows that 95% of organisations see zero return on their AI investment and AI is about to stress-test every weakness in your data foundation. Read on for how to build an AI-ready data foundation.

Discover how the WeBuild-AI team moved from Figma to functioning frontend in four weeks, including the full scope of multi-functioning tools, user testing and key learnings.

The question isn’t whether you need an AI Centre of Excellence. The question is how to build one that enables rather than obstructs, that governs without strangling innovation, whilst creating genuine enterprise value.

Discover which three AI workflows can have the most transformational value for PE due diligence teams, based on our industry experience with real customers and in-house AI expertise.

Executives need to overcome multiple obstacles, from managing expectations, to training, to identifying use cases across the business, in order to successfully implement AI. Here’s how to build AI initiatives that succeed.

Today we’re announcing that we’re officially a Great Place To Work 2026/27. As a scale-up AI consulting business, this is a huge milestone and demonstrates our commitment to culture and talent as we grow. Thanks to the entire team for contributing. Interested in joining? Check out our Careers page!

Senior Consultant Kaitlin shares her latest projects, newest challenges and what she’s working on at the moment - deep-diving into industries and building out entire systems from scratch.

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.

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.

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.

From agile and waterfall methodologies to AI-native software development lifecycles - how to standardise spec-driven approaches to build AI-ready enterprises.

This blog explores the 5 biggest transformational AI use cases for private equity - portfolio data unification, market intelligence infrastructure, due diligence automation, relationship-based deal management and automated LP reporting and communications platform.

The four biggest challenges in AI solution deployment can hinder transformation projects to the point where nothing at all is deployed. How can you overcome these? Read the full blog to find out.

How forward-thinking PE firms are deploying production-ready AI in 4-8 weeks to gain competitive advantage without the 18-month implementation timelines of traditional consultancies

A Practical Framework for Modern PE Firms. Artificial intelligence is on the forefront of disrupting every industry globally, and private equity and venture capital is no exception.