Trusted by research leaders and data science teams
Three simple steps to unlock your insights
Import and validate your Excel or csv file in seconds. Pick which text fields you want to analyse.
Our statistical pipeline processes your data using large language models and machine learning to uncover, interpret and quantify themes.
Receive themes, sentiment analysis, and curated quotes with full confidence scores. Filter visualisations by segment.
See what our customers are saying
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's founders bring over 20 years' combined experience across market research and academic research, with world-leading expertise in data science and statistical inference.
For too long, organisations have settled for glorified word clouds, crude sentiment tracking or unreliable AI summaries. We're on a mission to change that.
We want to build a world where audience insights are led by what matters, not what can be easily analysed.
Check these out for quick facts about WholeSum
WholeSum’s hybrid AI approach consistently outperforms leading reasoning models such as GPT-5 and Gemini 2.5 Pro on theme 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 enterprise language model APIs using data encryption at rest and in transit, with no training performed on the 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. You can download structured matrices to cross-analyse with wider data and we're working on API endpoints that can be incorporated into dashboards, for example.
We currently support CSV, XLS and XLSX on our self-serve platform.
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