I recently migrated a production Python codebase to Go using Claude Code as the primary coding agent. The codebase was Kodit, an MCP server and CLI tool for indexing code repositories. It had a well-structured Python backend with a FastAPI server, SQLAlchemy ORM, Pydantic models, and a search pipeline combining BM25 and vector embeddings.
Read moreBuilding AI-powered applications is exciting, until you need to evaluate whether your model actually works. How do you test an AI system that gives different answers every time? How do you gather enough test data to be confident in your results? This one-hour practical webinar will transform you from uncertain to confident about AI evaluation.
Read moreWinder.AI helped Apartment List modernize its machine learning operations by unifying data pipelines, automating Kubeflow workflows, and introducing enterprise-grade governance. The outcome: consistent training and inference data, faster deployment cycles, and self-service capabilities that enabled Apartment List’s data science team to scale model delivery with confidence.
Read moreWhile 71% of organizations have deployed generative AI in at least one business function, fewer than 5% of AI pilots achieve meaningful revenue impact. This gap between experimentation and production success represents both the greatest challenge and opportunity for technical leaders in 2025.
Read moreTired of static dashboards that never quite answer the real question you want to ask?
AI language models, popularised by ChatGPT, have gained notoriety for being able to understand queries with human-levels of quality. By plugging this bots into your data sources you can finally get all the answers you desire, by simply asking. (I still say please and thank you!)
In this article, I explore what these so-called “AI Agents” are (not the greatest name), how they work their magic, and the fun things you can do once connected to your data. We’ll dive into some example code and architectures and show off some (hopefully) working demos.
Read moreCurious how AI projects really unfold beyond the hype and what risks lawyers must spot before they commit to projects? In this candid fireside chat, AI veteran Dr Phil Winder teams up with Mills & Reeve tech-law partner Paul Knight to swap war stories, legal landmines and practical guardrails from the front lines of implementation. This 40-minute session arms you with the insider insights every in-house team needs before signing their next AI deal.
Read moreA 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.
Read moreUsing digital twin data to build flight traffic simulators and train reinforcement learning AI agents. A leading aerospace business and Winder.AI opened new horizons for dynamic, data-driven scheduling solutions that integrate with our client’s advanced flight planning technology.
Read moreAs 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. Our AI consulting team helps organizations navigate this complex transformation journey.
Read moreAs 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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