Emerging threats and opportunities show up in text data like field team notes, internal feedback and call transcripts long before lagged quantitative metrics, organisations struggle to interpret narrative data consistently at scale.
Wholesum operationalises the identification and monitoring emergent issues, before they hit the outcomes that matter most.
Pharma Customer Engagement, Retail Data Science, Global People Insights
Organisations focused on understanding audience intent and behaviour are still largely dependent on quanitative and structured data.
Wholesum turns text into stable new signals, processing content at scale and feeding rich sources of additional insight directly back into your existing tech systems and models.
AdTech and Media Platforms
In high-stakes industries, narrative data sources hold valuable signals for decision-making. But these are hard to interpret carefully at scale, meaning teams fall back on ad-hoc human interpretation.
Wholesum ingests external text-based sources such as news articles and reports at scale, then triangulates signals with internal datasets to support consistent operational assessments from evolving narrative evidence.
National Security, Investment Management, Private Equity
Traditional classification tools are fine-tuned to a narrow, static range of topics or sentiment.
Modern large language model performance is inconsistent and difficult to operationalise reliably across large, evolving datasets.
Wholesum is different. We use comparative statistical inference across entire datasets, which solves dozens of common failure modes in AI and human interpretation, making large-scale analysis stable and repeatable.
Analyses every entry comparatively in the context of the wider dataset, removing anchoring and interpretation bias.
Discovers both widespread and niche patterns — and quantifies how strong, how prevalent, and how certain each signal is.
Consistent, reproducible outputs across analysis runs, stable across time periods, and traceable to its source data.
Scale from thousands to millions of entries while maintaining stable, reproducible analysis.
Wholesum was built to bring statistical rigour to narrative text data at scale.
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.
About usBook a 30-minute call to discuss how Wholesum can help your team extract reproducible, auditable insights from unstructured text at enterprise scale.
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