Key Decisions We Help Optimize
Across Finance, Retail and CPG we help optimize pressing decisions, including but limited to the following key decisions.
These AI Modules Are Powered By
Automated Decision Intelligence
Incorporating AI and Behavioural Economics our Automated Decision Intelligence is a self-learning system designed to optimize enterprise-level decision-making.
What sets it apart from a traditional decisioning engine, in addition to incorporating human behaviour and its drivers, is its ability to adapt and improve its performance over time without manual intervention. This is achieved through "self-learning loops," consisting of machine learning algorithms that continuously analyze the decisions and outcomes achieved and then refine the decision-making process accordingly.
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Key Components of Automated Decision Intelligence
Input key problems and desired outcomes into platform
Decision Intelligence Modules
Continuum’s model management uniquely combines AI and Behavioural Economics models to ensemble data insights into intelligence that can Predict, and Drive Results that fuel enterprise growth
Outputs & Engagement
Interactive UI with simulations, dynamic interpretable results & Generative AI technology that increase trust and de-risk decisions
AI Automation & Learning
Self-improving, self-learning AI analyzes Decision Intelligence outputs and optimizes decisions
Gathers data from multiple sources, including user interactions, databases, or external data feeds such as market trends, social media, etc.
The Behavioural Economics algorithms analyze the data to identify human behavioural patterns, cognitive biases, or anomalies.
AI algorithms make initial decisions based on the data and behavioural insights.
Executes the decision, which might mean showing a specific product recommendation to a user or changing a price variable or marketing spend.
Collects data on the effectiveness of its decisions. Did the consumer make a purchase? Did the ROI from marketing spend was positive or over its baselines?
Fine-tunes both its AI algorithms and its behavioural models using the feedback loop, which involves reweighting the importance of certain variables, adjusting for newly recognized biases, or retraining machine learning models.
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