WholeSum offers an AI‑enhanced analysis engine that applies statistical inference to large collections of free‑text data, producing auditable counts, proportions, and confidence intervals for each insight. The platform integrates language models with rigorous machine‑learning methods and delivers results via a secure API or on‑premises deployment, enabling enterprise analytics teams to automate quantitative text analysis at scale.
Funding
$1.9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
STPVTWTFounders
Product
Problem
Organizations that collect large volumes of free‑text data—such as field notes, interview transcripts, online reviews, or survey responses—struggle to extract reliable, quantitative insights because traditional NLP tools lack statistical rigor, reproducibility, and scalability.
Solution
WholeSum provides an AI‑enhanced analysis engine that treats each text entry in the context of the entire dataset, applying statistical inference to preserve structure and quantify uncertainty. The platform integrates language models with rigorous machine‑learning methods, delivering traceable counts, proportions, and thematic summaries that can be audited and reproduced across runs. Results are delivered through an enterprise‑ready API or local deployment, enabling seamless integration with CRM systems, analytics pipelines, and regulatory workflows. By automating signal detection and quantification, WholeSum reduces manual researcher effort from days to minutes while maintaining high accuracy and consistency at scale.
Target Audience
Primary customers are insight, analytics, and strategy teams in large enterprises such as pharmaceutical firms, financial institutions, private equity groups, and consulting agencies that need to turn unstructured text into actionable, quantitative intelligence.
Features
- Contextual analysis of each response against the full dataset to surface subtle, high‑impact signals
- Statistical inference framework that produces auditable counts, proportions, and confidence intervals tied to source text
- Enterprise‑grade infrastructure supporting large‑scale datasets with performance that remains stable as volume grows
- Flexible deployment options including secure API access and on‑premises installation with encrypted data handling
- Exportable outputs compatible with CRM platforms, analytics tools, and regulatory submission formats
- Built‑in reproducibility controls that ensure identical results across repeated analyses for benchmarking and longitudinal studies