UK-Based WholeSum Raises $335K To Fix AI’s Trust Problem In Text Analytics

The funding comes as demand grows from enterprises in high-trust sectors such as healthcare, finance, and defence, where reliable and explainable AI insights are essential.

Many organisations struggle to analyse large volumes of unstructured data. While they increasingly use large language models (LLMs), these tools often produce inconsistent or unverified results, making them unsuitable for regulated industries.

WholeSum was founded by Emily Kucharski and Dr Adam Kucharski after they experienced these challenges firsthand while working with large qualitative datasets. They identified a key gap in the market: the need for AI tools that can scale analysis while remaining scientifically accurate, transparent, and defensible.

Read Also - Lisbon-based Bondstone Launches Venture Capital Arm And €50 Million DeepTech Fund

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.

“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.”

About WholeSum

WholeSum is an analytics startup that helps organisations analyse large volumes of text data and turn it into reliable insights. It enables teams to process surveys, reviews, and other unstructured data at scale. The platform delivers statistically robust, auditable results, avoiding the limitations of slow manual analysis or unreliable AI summaries.