WholeSum, a UK-based qualitative analytics startup, has raised £730,000 in a pre-seed round led by Twin Path Ventures, with participation from SFC and strategic angels via Ventures Together, alongside grant funding from Women TechEU. It provides a hybrid-AI analytics layer that converts large volumes of free-text data into statistically robust, auditable insights. The new funding will be used to accelerate product development, expand the science and engineering teams, and scale early enterprise deployments, with WholeSum now opening API pilot partnerships.
Most organisational data is unstructured, yet teams lack reliable ways to analyse large volumes of text. Critical nuance and signals remain buried in transcripts, open-ended surveys, online conversations and customer feedback, leaving organisations reliant on slow manual coding or brittle LLM summaries that cannot be audited or reproduced. WholeSum addresses this gap by converting large volumes of qualitative data into reproducible, quantified insight that can be integrated directly via API into existing analytics workflows. A dataset of 10,000 responses, typically weeks of work for a trained analyst, can be processed in seconds and then passed into a deeper statistical analysis toolkit.
WholeSum’s work with Imperial College London, Female Founders Rise in collaboration with Barclays, and others is revealing that the most valuable signals are often buried in unstructured audience data rather than in crude tickbox metrics. Until now, it has not been feasible to uncover those signals reliably and at scale. The product is aimed at high-trust sectors such as research, healthcare and financial services, where dependable qualitative evidence is crucial for better decisions and outcomes. WholeSum outperforms leading reasoning models, including GPT-5 and Gemini 3 Pro, delivering up to 100× faster processing with 100× lower theme attribution error on datasets with clear thematic structure, while ensuring reproducible outputs.
Founded by Emily Kucharski and Adam Kucharski, WholeSum combines expertise in commercial and public sector insights with statistical inference and machine learning. Adam’s award-winning research has informed international health policy through SAGE and WHO, and his open-source tools are used globally. Emily brings experience translating audience data into strategy for major consumer brands at leading advertising agencies. The pair began developing WholeSum after encountering hallucinations and numerical inconsistencies while analysing thousands of user experiences in a previous venture, highlighting the lack of scalable and scientifically defensible tools for qualitative data.
Most organisations I’ve spoken to have tried using AI for qualitative analysis – and they’ve been frustrated and disappointed. If you can’t trust the output, you can’t act on it. It’s absurd that qualitative data remains such an untapped goldmine.
Qualitative insights have been trapped behind manual workflows and inconsistent methods for decades. WholeSum brings scientific rigour and automation to a universal problem. This is foundational infrastructure for how qualitative evidence will be generated and trusted in the future.
