The FutureHouse Platform, launched in May 2025, is a domain-specific AI environment designed to support various stages of scientific research. It provides researchers with access to four specialised agents — each tailored to a particular task in the knowledge production pipeline: concise information retrieval (Crow), deep literature synthesis (Falcon), precedent detection
Can large language models return accurate results when asked to find real academic texts written by specific philosophers on a specific topic? In this case, the topic was Marxist theory — and the instruction was to list peer-reviewed publications in which Gilles Deleuze or Michel Foucault offer a critique of it.
One of the key promises of recent generative AI models is their ability to process not only text and images but also audio and video content. GPT-4.5, with its apparent support for MP3 and MP4 file uploads, seems like an ideal tool for tasks such as transcription — particularly for
As data visualisation becomes an increasingly integral part of communicating research and insights, the choice of generative AI models capable of handling both code and chart rendering has never been more important. In this post, we explore which models can be reliably used for visual tasks—such as generating Python-based
If your goal is to generate a downloadable .txt file—be it for survey responses, formatted data, or any structured text—not all models can deliver. In this case study, we tested various models to see whether they can create actual .txt files rather than just displaying text or offering
With generative AI tools becoming increasingly common in academic workflows, many researchers are tempted to use them for tasks like journal selection. But can these models really help you decide where to publish? Our quick test in the field of legislative studies suggests: not just yet. We wanted to see
Generative AI models have become indispensable tools across industries, but their effectiveness often hinges on the types of input files they can process. Here, we assess the capabilities of leading GenAI models—including Claude 3.7 Sonnet, GPT-4.5, and Qwen2.5-Max—examining their support for formats like PDF, JSON,
Data pre-processing is a crucial but often tedious stage in research, particularly when standardising complex CSV datasets. This blog demonstrates how GenAI can systematically structure geographic location data directly from an input CSV file without coding. Our primary objective was to ensure consistent and accurate categorisation, preparing the data for
Time series analysis is a powerful tool for researchers across disciplines, yet its reliance on programming can pose a challenge for those without coding expertise. This blog demonstrates how AI-driven prompts can enable researchers to perform simple time series analyses without writing a single line of code, making data insights
As part of testing the synthetic data generation capabilities of GenAI models, we assigned a seemingly simple task to some of the most popular language models: to generate 50 unique customer feedback entries about a smartwatch, including specified fields (name, age, city, feedback, and date of purchase) and a varied
Named Entity Recognition (NER) is a key technique in natural language processing that involves identifying and classifying specific entities in a text, such as people, organisations, locations or dates. The following prompt offers a structured and nuanced approach to testing NER capabilities using a rich biographical narrative. The prompt instructs
