Disciplined analysis of unstructured material

No. We work exclusively with material that you have already collected and that you trust. We do not search the internet, scrape websites, or assemble datasets from external sources. This distinction matters because the integrity of an analysis depends entirely on the integrity of the source material. You know where your material comes from, what it represents, and what its limitations are. We apply analytical rigour to that material, but we do not make choices about what material to include - that remains your decision.

This is where most of our work actually happens. While the AI can process thousands of documents rapidly, that speed is meaningless if the analysis is poorly defined. When you run a coding procedure thousands of times, even small misunderstandings in definitions or criteria compound into significant errors in the final results. A category that is slightly ambiguous will be applied inconsistently across the dataset. A definition that does not quite match your intentions will produce findings that look precise but miss the point. We invest substantial time before any large-scale analysis begins to ensure that definitions are precise, criteria are unambiguous, and the analytical framework genuinely reflects what you are trying to understand. We test procedures on samples, review edge cases, and refine until the results are explainable and defensible. The AI provides scale and consistency; the quality comes from the methodological work that precedes it.

The difference is fundamental and technical, not just methodological. When you paste documents into ChatGPT or similar interfaces, you face several problems that make systematic analysis impossible. First, the model's responses are influenced by its previous answers in the same conversation - if it codes the first ten documents a certain way, that interpretation colours how it approaches the next hundred. There is no isolation between analyses. Second, you have no control over whether the model actually processes all your material or selectively attends to parts it considers most relevant. With hundreds of documents, you cannot verify that each one received genuine attention rather than being skimmed or ignored. Third, these interfaces have context limits - at some point, you simply cannot fit more material into the conversation, and the model begins forgetting earlier documents entirely. We work differently. We use the API directly, which allows us to send each document as an isolated analysis task. Every single piece of material is processed individually, with the same instructions applied fresh each time, with no contamination from previous responses. This is not a convenience - it is the only way to ensure that document 847 receives exactly the same analytical treatment as document 1. The methodological framework we develop is applied identically to every item in your dataset, and we can prove that this happened. That architectural difference is what makes the results defensible.

You receive structured findings that show patterns across your material - what themes appear, how frequently, in what contexts, and how they relate to each other. Depending on the project, this may include coded datasets, thematic summaries, visual dashboards showing distributions and trends, and analytical reports that explain what the material reveals. Crucially, the findings are traceable: we can show you which documents or passages led to which conclusions, so you can verify the analysis against your own reading of the material.