Built for scale and decisions that matter
WholeSum turns tens of thousands of free-text field team notes each month into structured CRM input, emergent signals, and targeting insights.
WholeSum analysed hundreds of thousands of online reviews to uncover deeper drivers of customer outcomes and competitive advantages.
WholeSum identified and quantified key barriers to growth for female-founded scale-ups, enabling spend to be directed towards higher-impact, highly targeted initiatives.
Associate Professor, Nottingham Business School
"It would have taken the best part of a year, and tied up a lot of skilled researcher time, to get to the outputs WholeSum can do in minutes."
CEO of Female Founders Rise
"WholeSum turned 63,000 words of founder experiences into detailed, human summaries for us, for a submission to parliament. I needed the speed of AI but without the risk of errors, and WholeSum very much delivered."
Director, Link Consumer
"The analysis you provided was powerful. It enabled us to pull together a really compelling story for our client. We honestly believe we would not have gotten to the same place using more traditional methods of analysis, even with more time."
CEO, Praktiki
"Emily and Adam at WholeSum made analysing my free-text survey responses and interview transcripts from doctors quick and easy, saving me half a day of manual work. The insights were clear, actionable, and far more reliable than typical LLM outputs. Highly recommend!"
Communications Director, PANDAS
"Your analysis has helped us ask questions and look at things in a new light, thanks to seeing our data in an easily digestible format. It's uncovered trends we hadn't noticed before."
WholeSum was built to bring statistical rigour to qualitative data.
Our founders have experience across audience insights and behavioural research, along with world-leading expertise in data science and statistical inference.
Our mission is to find the signals that matter most, no matter how complex the data. We believe organisations should collect the data they actually need, not what’s easiest to analyse.
Some details about WholeSum
Each response is analysed in the context of the entire dataset, meaning WholeSum can detect subtle signals that other tools – and even humans – typically miss. We distinguish between widespread and marginal signals while preserving uncertainty, making conclusions more robust in high-stakes decision contexts.
WholeSum consistently outperforms leading reasoning models and embedding-based methods on tasks such as signal discovery and allocation at scale. More importantly, it produces results that remain consistent, traceable and robust when analysis is repeated, enabling like-for-like comparisons between groups and over time.
WholeSum uses a statistical framework that preserves the structure of the dataset throughout the analysis process. Final outputs such as counts, proportions and supporting evidence are always tied directly back to the source data, ensuring numerical consistency and exact traceability to original text.
Yes. WholeSum supports enterprise API and local deployment options, with no training performed on your data. Data is encrypted in transit and at rest, and can be deleted after analysis or retained under your control.
Outputs are designed to be reusable, comparable across datasets and integrated into existing systems, such as CRM platforms and internal analytics workflows. This enables teams to track how signals evolve over time and maintain consistency across analyses.
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