Turn messy text into trustworthy insights
Statistically robust analysis of open-ended surveys, reviews, social comments and more. Full auditability, AI hallucination protection, and scalable.
Statistically robust analysis of open-ended surveys, reviews, social comments and more. Full auditability, AI hallucination protection, and scalable.
LLMs make things up and miscount, even with prompt fine-tuning. Our hybrid pipelines - which combine the best of AI, symbolic reasoning and statistical models - protect from this.
Uncover themes with traceability and confidence scores. In benchmarking, we outperform leading reasoning models on accuracy of theme allocation, and match manual analysis accuracy.
Unlike LLMs, WholeSum's performance doesn't drop as data volumes increase. And for large datasets, you can integrate WholeSum into your existing platforms via our API.
Our analysis supports many verticals and industries, from software platforms to service providers, and education to employee engagement.
Quantified themes
Curated quotes
Personas
Trends
Predictions
We want to build a world where data capture and research are led by what matters, not what can be analysed.
Our cofounder is in the top 0.1% of cited researchers worldwide for his work on statistical modelling, inference and machine learning.
We've led data and research projects with top organisations, including BBC, Visa, SpaceX and Meta.
WholeSum improves key business decisions by delivering better insights.
"The analyses you provided of the open ends, particularly the themes and summaries with key verbatim, were powerful. They 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, especially given the volume of data."
Informing decision-making criteria around student selection by uncovering high-performance predictors and trends from qualitative survey data.
“WholeSum turned 63,000 words of founder experiences into detailed, human summaries for us, for submission to UK parliament. I needed the speed of AI but without the risk of errors, and WholeSum very much delivered!"
“Your analysis helped us 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 made analysing free-text survey responses and interview transcripts from doctors quick and easy, saving me at least half a day of manual work. The insights were clear, actionable, and far more reliable than typical LLM outputs. Highly recommend!"
Measuring employee experiences and quantifying desired changes to improve key HR metrics such as retention and job satisfaction.
"The report we got from your analysis of 25x lengthy interview transcripts was great. It also validated and matched the main themes that came out of your survey analysis. Pulling out quotes helped us animate the report and made it much more personal."
Our pipelines were built while crowdsourcing the world’s largest database of parenting experiences, turning thousands of words into digestible and engaging summaries each week.
Words analysed
Insights
Weekly respondents
WholeSum offers flexible pricing to match your scale and goals.
Starting from just
Trustworthy analysis of emerging themes in a set of responses.
Structured data tables
Rich, detailed summaries
Accuracy checks
Secure storage
Starting from
Customise your analysis and add more advanced statistical outputs. Everything in Core plus add-ons like:
Cross-question analysis
Predictions
Clusters & trends
Study design input
API Beta
Contact us to join the waitlist and become a pilot partner to try it out for free:
Full API access
Integration support
Exclusive design partner discounts
Priority support
Yes. Data processed by large language models is transmitted securely using encrypted connections in transit and at rest. No data is retained by LLM providers after processing.
AI tools generally rely solely on prompt engineering and model fine-tuning, which risks hallucinations and errors. WholeSum instead takes a quantitative approach, using LLMs alongside complementary technologies to deliver accuracy at scale.
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. We can produce structured matrices to power your broader analysis, and we're working on predictable API endpoints that can be incorporated into dashboards, for example.
Whether it's deeper insights, cost efficiency or time saved, we'll help you give your valuable qualitative data the analysis it deserves.
Try it now