Trusted by research, analytics and strategy teams
Built for scale and decisions that matter
See what our customers are saying
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 quick facts about WholeSum
WholeSum's hybrid AI approach consistently outperforms leading reasoning models such as GPT-5, Claude Opus and Gemini Pro on theme discovery and allocation benchmarks, while also delivering substantially higher accuracy than embedding-based classification methods.
Because WholeSum uses AI for specific tasks within a larger framework that uses statistical methods and algorithmic natural language, we avoid using language models – and the hallucination risk they create – to generate final numbers and quotes. Instead, we retrieve the ground truth values at the final step, ensuring all numbers add up and quotes match the original source.
Yes, our statistical approach means that you can match themes and confidence scores back to original responses, making it possible to combine qualitative and quantitative insights at scale. Feel free to get in touch to discuss these advanced analysis options.
Yes. Analysis is performed with local algorithms as well as either local models or enterprise language model APIs, depending on your needs. We use data encryption at rest and in transit, with no training performed on your data.
We use a mix of large language models, algorithmic natural language, machine learning and statistical models to provide flexible, rich and reliable outputs and insights.
We design each step so that outputs can be reused in subsequent analysis, and integrated with your systems.
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