WholeSum, a data analytics startup, has raised £980,000 in pre-seed funding from Love Ventures, Beamline, and strategic angels. The funding brings its total pre-seed funding to £980,000 following an initial £730,000 raise led by Twin Path Ventures announced earlier this year. It will be used for R&D, expanding scientific and engineering teams, and scaling enterprise deployments in sectors where methodological rigour is critical.
Organisations are increasingly finding that existing AI tools fail to deliver reliable, auditable insight from large volumes of text data, particularly in regulated environments such as healthcare, financial services, and defence. While most organisational data is unstructured, teams struggle to analyse it at scale and often encounter hallucinations, inconsistencies, and outputs that cannot be reproduced or defended. WholeSum addresses this with a hybrid AI and statistical inference platform that converts free-text data into uncertainty-aware, reproducible, and auditable insight. Designed as an API-first infrastructure layer, it integrates into existing analytics workflows and enables organisations to extract signals and underlying drivers from text data.
Since its initial raise, it has seen traction across enterprise organisations in high-trust sectors, including early work with universities, financial institutions and pharmaceutical companies. This work has demonstrated that early signals are often found in unstructured text data rather than lagged quantitative metrics.
Founded by Emily Kucharski and Dr Adam Kucharski, WholeSum combines expertise in commercial insight generation with research in statistical inference and machine learning. The business was created following the founders’ experience analysing large-scale qualitative datasets in a previous venture, where they identified a lack of scalable and scientifically defensible tools for extracting insight from qualitative data.
From talking to dozens of large organisations making high-stakes decisions, we’ve seen a clear pattern: teams are experimenting with AI for text analysis, but quickly hit a wall when outputs can’t be trusted or reproduced. This funding allows us to move faster in building infrastructure for robust analysis at scale.
Generic LLMs can’t deliver the consistent, reliable signals that high-trust industries need from unstructured data. Emily and Adam are uniquely positioned to solve this, and we're delighted to be backing them as they scale across Pharmaceuticals, Financial Services and beyond.
