Dataharvest 2026 - the European Investigative Journalism Conference

Every investigative journalist has faced the same bottleneck. What would I find if I could check all of them: all the company registrations, all the addresses, all the permits? Until recently, answering that question required weeks of scripting. In this session, we introduce a faster way: directing an AI coding agent to build investigative data pipelines on demand. Participants will direct an agent to pull data from a public source, clean it, and turn it into an interactive visualization, all without writing code manually. The approach is applicable to a range of investigative beats, from financial crime and corruption to environmental accountability and lobbying networks.

To follow along, participants should have a basic understanding of web technologies, but no programming experience is needed. After attending this session, participants will be able to direct an AI coding agent to build a data pipeline, from raw data to interactive visualization, and apply this methodology to their own investigative questions. Participants should have a laptop with a modern web browser. We will provide API keys and access credentials during the session. Detailed setup instructions will be shared via a GitHub repository before the workshop.

In this session, participants will take an archive of podcast episodes and other documents, and set up some cloud infrastructure to analyse the files using open source transcription, text extraction and generative AI tooling. The aim is to equip attendees with the skills to rapidly perform bulk operations on large troves of data by leveraging cloud platforms. By the end of the workshop participants will be have a pipeline that can answer questions like 'which podcast episodes have instances of greenwashing in them'. At The Guardian, we have used these techniques in two recent investigations. When investigating the Free Birth Society we needed to perform analysis on hundreds of hours of audio files. When the Epstein files were released we had to try and extract meaning out of millions of unstructured text documents. By making use of simple cloud tools (queues and instances) we were able to process hundreds of files in parallel whilst retaining control of the data.

Participants should have some experience of using the command line. All cloud accounts will be provided. After attending this session, participants will be able to use the cloud to quickly analyse large numbers of documents and media files. Participants using Windows could save some time by setting up WSL https://learn.microsoft.com/en-us/windows/wsl/install

Learn approaches to tooling and infrastructure that allow every member of your newsroom to participate in your AI experiments, along with how to test and track both improvements and disappointments along the way!

In this workshop, we'll look at: Python libraries that can turn tiny snippets of code or prompts into shareable web apps (Gradio, Streamlit), platforms that allow non-technical users to build evaluations and experiment on their own (Braintrust, n8n), and approaches to models and tooling that provide long-term value and flexibility when selecting services and providers (Pydantic, OpenRouter).

Whether you're looking to use AI for investigative work or to ease the copy-editing burden, increasing participation across the newsroom can help discover limitations and inspiration, along with easing anxieties over automation. To get the most out of this session, participants should have a working knowledge of Python.

After attending this session, participants will have a suite of approaches to bring non-technical members of their newsroom into their AI processes. Participants should have Jupyter installed or a Google account to work in the cloud.