Automate and optimize decisions across the full spectrum of decision-making.
Reach the best possible outcomes by combining multiple AI techniques and decision engines to deliver truly optimized results.
Enable quicker decision-making cycles by building and deploying AI models quickly.
Continuously improve decision quality through learning from outcomes and adapting to changing business conditions.
Aera's Decision Composition and Execution lets you design and operationalize decision workflows in Process Builder, a no-code/low-code graphical environment. Using an intuitive drag-and-drop interface powered by Aera's patented depth-first paradigm, decision engineers and analysts can connect data, services, engines, and user experiences — or embed AI agents directly into workflows.
Build and digitize complex decision workflows by connecting nodes in a drag-and-drop canvas.
Access a comprehensive library of nodes to perform various functions quickly and build applications, processes, or skills.
Manage the full workflow lifecycle with branching and merging, debugging, version control, process logs, and rollback.
Seamlessly add visual analytics, pages, forms, and voice interactions to your decision processes.
Aera Cortex™ is an AI engine purpose-built for decision intelligence, enabling organizations to operationalize AI in business decisions. You can build custom models or integrate pre-trained ones, connecting to live data, domain-specific algorithms, and optimization techniques to build plans, run scenarios, and automate decisions without deep coding expertise.
Automate feature engineering, model selection, hyperparameter tuning, and model validation to improve performance.
Apply linear and integer programming, heuristics, and trade-off analysis to deliver more accurate and feasible decisions.
Support model selection, hierarchical forecasting, and overrides to generate accurate time-series predictions.
Manage a comprehensive model lifecycle, including validation, performance monitoring, and automated retraining.
Aera's Learning Engines continuously improve decision quality by capturing and analyzing decision outcomes, user responses, and business impacts. The system adapts in real time to changing business conditions, leveraging accumulated decision memory to refine recommendations. Automated confidence scoring and performance tracking ensure the reliability of AI-driven decisions.
Capture decision outcomes, user responses, and business impacts continuously to track adoption rates and recommendation effectiveness.
Generate interpretable confidence levels with explainable drivers, enabling you to understand the reliability and reasoning behind AI-driven recommendations.
Automate deep learning model development and optimization, including neural network architecture selection, training configuration, and hyperparameter tuning.
Aera's Advanced Modeling combines analytical, graph-based, mathematical, AI/ML, scenario, and simulation to generate context-rich, explainable recommendations. These models incorporate business rules, constraints, relationships, agentic reasoning, and predictive signals to ensure accurate, transparent, and context-aware decision-making across the enterprise.
Map relationships and dependencies across entities to understand upstream and downstream decision impact.
Apply analytical techniques and rules to evaluate the most optimal and feasible decision options.
Anticipate future outcomes to improve the accuracy and confidence of decision recommendations.
Plan scenarios, perform what-if analyses, quantify trade-offs, and assess outcomes before execution.
