Hootfolio offers a no‑code Causal AI SaaS that automatically discovers causal relationships from uploaded CSV datasets and visualizes them as directed‑acyclic graphs. The platform provides quantitative edge weights and counterfactual simulation tools, enabling analysts and managers in marketing, product, HR, and policy to test intervention scenarios and estimate ROI without specialized statistical expertise.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations rely on correlation‑based analytics, which often leads to misguided decisions because the true causal drivers of outcomes remain hidden. Identifying actionable root causes across dozens or hundreds of variables typically requires specialist statistical expertise and extensive manual effort. Consequently, teams struggle to prioritize interventions, justify actions to stakeholders, and measure the real impact of implemented measures.
Solution
hootfolio delivers a no‑code Causal AI platform called **causal analysis** that automatically infers causal relationships from structured data and presents them as directed‑acyclic graphs (DAGs). Users upload CSV files and, within a few clicks, obtain a visual causal model that quantifies the strength of each cause‑effect link. The underlying algorithm—originating from NEC’s patented research—scales to mixed‑type datasets with over 100 variables, delivering results in a fraction of the time required by traditional methods. The platform also supports counterfactual simulation, allowing decision makers to test “what‑if” scenarios and estimate the expected lift of specific actions before implementation. All outputs are exportable for inclusion in reports, presentations, or downstream analytics pipelines, enabling faster, evidence‑based decision making without the need for a dedicated data science team.
Target Audience
Primary users are data‑driven decision makers in marketing, product, HR, and strategy functions—such as analysts, managers, and consultants—who need to uncover root causes and evaluate intervention impact without deep statistical expertise. The platform also serves government agencies and policy teams that require transparent, evidence‑based analysis for program design.
Features
- Drag‑and‑drop CSV upload with fully automated causal graph generation in under a minute for datasets containing 10‑500 variables.
- Interactive DAG visualization where edge thickness and numeric scores represent estimated causal effect magnitude, editable layout for seamless integration into slide decks.
- Support for continuous, categorical, and ordinal data types, with built‑in handling of missing values and mixed‑mode variables.
- High‑performance inference engine based on NEC’s proprietary causal discovery algorithm, achieving >90 % accuracy on benchmark datasets and up to 50× faster than conventional approaches.
- Counterfactual simulation module that predicts outcome changes under hypothetical interventions, providing quantitative ROI estimates for proposed actions.
- Ability to inject domain knowledge (e.g., known constraints or expert hypotheses) to refine the causal model and improve precision.
- SaaS delivery with role‑based access control, audit logging, and secure data transmission compliant with industry privacy standards.
- Optional professional services: workshops, guided implementation, and ongoing project support to embed causal reasoning into organizational workflows.