The UK startup targets high-trust sectors with reproducible, auditable insights from unstructured data.
UK-based analytics startup WholeSum has brought its total Pre-Seed funding to $1.3 million, with an additional $335,000 new investment from Love Ventures, Beamline, and strategic angels, following its initial $965k raise led by Twin Path Ventures announced earlier this year.
The round comes amid growing demand from enterprises in high-trust sectors, where organisations are increasingly finding that existing AI tools fail to deliver reliable, auditable insight from large volumes of text data.
While most organisational data is unstructured, teams continue to struggle to analyse it at scale. In practice, many have turned to LLMs, only to encounter hallucinations, inconsistencies, and outputs that cannot be reproduced or defended – particularly in regulated environments such as healthcare, financial services, and defence.
Founded by Emily Kucharski and Dr Adam Kucharski, WholeSum was born out of the founders’ frustration with existing AI tools while analysing large-scale qualitative datasets in a previous venture. The experience highlighted a systemic problem: organisations want to extract meaningful insight from qualitative data, but lack tools that are both scalable and scientifically defensible.
“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,” said Emily Kucharski, cofounder and CEO of WholeSum.
“This funding allows us to move faster in building infrastructure for robust analysis at scale.”
WholeSum addresses this gap 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 directly into existing analytics workflows, enabling organisations to extract nuanced signals and underlying drivers with the same rigour as numerical data.
Since its initial raise, WholeSum has seen strong traction across enterprise organisations in high-trust sectors. Early work with universities, financial institutions and pharmaceutical companies has demonstrated that the most valuable early signals are often buried in unstructured text data rather than in lagged quantitative metrics.
“Generic LLMs can’t deliver the consistent, reliable signals that high-trust industries need from unstructured data,” said Bill Corfield, Principal at Love Ventures.
“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.” The company is now rolling out pilots and enterprise integration for increasingly complex, large-scale datasets.
The additional funding will be used for R&D, expanding the company’s world-class scientific and engineering teams, and scale enterprise deployments in sectors where methodological rigour is critical.