AI, Machine Learning, Reinforcement Learning, and MLOps Articles

Learn more about AI, machine learning, reinforcement learning, and MLOps with our insight-packed articles. Our AI blog delves into industrial use of AI, the machine learning blog is more technical, the reinforcement learning blog is industrially renowned, and our mlops blog discusses operational ML.

Early Adopter Release of Kodit: MCP server to index external repositories

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Author
Dr. Phil Winder
CEO

A lot of my work today is assisted with AI. From editing blog posts to developing proposals. But the number one use case is AI-assisted coding. Tools like Cursor,l Cline, Roo, Aider, Claude Code, etc. have disrupted software engineering to levels I hadn’t anticipated. But still, it’s not perfect.

This post is about one particular set of problems and a new open source tool I developed to alleviate it.

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AI-Native Transformation: How AI is Driving Organisational Change

Published
Author
Dr. Phil Winder
CEO

As AI-native transformation reshapes the technology landscape, organizations must rethink not just their tools but their entire structures and processes. Simply adopting AI technologies is not enough. True transformation requires organizational change, guided by a deep understanding of both technical feasibility and business value.

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Intro to Vision RAG: Smarter Retrieval for Visual Content in PDFs

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Author
Dr. Phil Winder
CEO

As visual data becomes increasingly central to enterprise content, traditional retrieval-augmented generation (RAG) systems often fall short when faced with richly visual documents like PDFs filled with charts, diagrams, and infographics. Vision RAG is a cutting-edge pipeline that leverages vision models to generate image embeddings, enabling intelligent indexing and retrieval of visual content.

In this session, you’ll explore the state of the art in visual RAG, see a live demo using open-source tools like VLLM and custom Python components, and learn how to integrate this capability into your own GenAI stack. The presentation will also highlight Helix, our secure GenAI platform, showcasing how Vision RAG fits into a scalable, enterprise-ready solution.

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AI in Aviation Case Study: Predicting Taxi Times

Published
Author
Dr. Phil Winder
CEO

Leveraging 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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Scaling GenAI to Production: Strategies for Enterprise-Grade AI Deployment

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Author
Natalia Kuzminykh
Associate Data Science Content Editor

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.

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Enterprise AI Assistants: Combatting Fragmentation

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Author
Dr. Phil Winder
CEO

Enterprise 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.

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Keynote: Data Transparency, AI Use Cases, Data Sovereignty

Published
Author
Dr. Phil Winder
CEO

At 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.

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Large Language Model Fine-Tuning via Context Stacking

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Author

Fine-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.

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