Building LLM-powered applications is challenging. But the most important challenge is when products are not useful to their users. This presentation is about the different ways you can gather feedback to improve LLM applications. I’ll review the state of the art, offer some practical tips, and share some examples.
Read moreLeveraging predictive analytics and a stand-to-runway modelling approach, Winder.AI and our aviation client improved taxi time predictions to reduced ground delays and improve fuel efficiency.
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The article examines the challenges of moving GenAI from prototypes to production. It highlights issues such as resource constraints, performance monitoring, cost management, and security, and suggests strategies for efficient scaling, robust guardrails, and continuous monitoring to ensure sustainable enterprise-grade deployments.
Read moreEnterprise AI Assistants unify disparate data sources, providing real-time insights and access control. Building domain-specific assistants and orchestrating them (hierarchical or federated) offers scalability, specialized features, and better performance than single-vendor solutions. Ultimately, organizations need a tailored approach that consolidates knowledge, fosters collaboration, and addresses evolving AI integration challenges.
Read moreAt Winder.AI, we’re seeing a shift in how businesses are adopting AI—not just for innovation, but for real, tangible commercial outcomes. I had the privilege of sharing these insights as a keynote speaker at ITAPA in beautiful Bratislava, Slovakia. The audience was looking for an insight into how AI is being used and some of the key challenges that are being faced today. I took the opportunity to share some of my thoughts about the importance of data transparency, some interesting use cases, and future regulation to watch out for.
Read moreFine-tuning Large Language Models (LLMs) can be a resource-intensive and time-consuming process. Businesses often need large datasets and significant computational power to adapt models to their unique requirements. Attentio, co-founded by Julian and Lukas, is changing this landscape with an innovative technique called context stacking. In this video, we explore how this method works, why it is so efficient, and what it means for enterprises looking to embed custom knowledge directly into their AI models.
Read moreIn this reflective podcast, the team at Winder.AI — Dr. Phil Winder, Charles Humble, and Jonathan Hunter — take a deep dive into their year, discussing trends, lessons learned, and their vision for 2025.
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Join Phil Winder and Charles Humble in this insightful interview as he delves into the critical components of an enterprise AI strategy, based upon his excellent article on the subject.
Gain actionable insights on aligning AI with your business goals, building robust data infrastructure, and prioritizing projects based on ROI. Learn about MLOps, AI security, and the role of generative AI and reinforcement learning across industries.
Read moreDiscover how to deploy open-source LLMs using LLM agent frameworks, orchestration frameworks, and LLMOps platforms. Learn about serving frameworks like vLLM and Ollama, and explore LLMOps tools that enhance language model performance in production environments.
Read moreIn this webinar I’m pleased to present a recorded interview with Kai Zenner, Head of Office and Digital Policy Adviser for MEP Axel Voss. Kai is a key figure in the EU AI Act, responsible for authoring parts of the regulation, whilst advising and debating the rest.
In this interview we discuss the impact of the EU AI Act on the AI industry, the implications for AI ethics, and the future of AI in Europe. We talk about the potential challenges for AI companies, especially those that lie outside of the EU. We find that although there are holes in the regulation, it is a step in the right direction for the AI industry. Kai makes his suggestions as to how companies can prepare for and help to improve the regulation.
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