Anthos offers a decision intelligence platform that builds causal models to forecast intervention impacts beyond historical data. By translating expert knowledge and multimodal data into causation-aware formats, it enables organizations to automate knowledge extraction and make more confident decisions in complex environments.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional machine learning models struggle to predict the outcomes of interventions that fall outside of their historical training data. This limitation hinders organizations' ability to navigate uncertainty and make confident, high-impact decisions in complex environments.
Solution
Anthos provides a decision intelligence platform that builds causal models to forecast intervention impacts beyond historical data. The platform leverages agentic programs and natural language processing to translate expert knowledge and multimodal data into structured, causation-aware formats. This process enables organizations to automate knowledge extraction and gain strategic advantage through more confident decision-making. By uncovering underlying mental models and integrating diverse data sources, Anthos creates robust causal models that predict system responses to novel scenarios. These models are then embedded into operational workflows, facilitating automated scenario analysis and faster learning.
Target Audience
Organizations across various sectors seeking to improve decision-making in complex and uncertain environments, particularly those involved in risk management, project development, and operational optimization.
Features
- Causal modeling engine for predicting intervention outcomes beyond historical data.
- Agentic programs for knowledge extraction and reasoning.
- Natural Language Processing (NLP) for translating expert knowledge and unstructured data.
- Data integration capabilities to connect and enrich diverse data sources.
- Structured, causation-aware data formatting for model input.
- Automated scenario analysis for evaluating potential interventions.
- Iterative model refinement through collaborative feedback loops and real-world testing.
- Workflow embedding for direct integration of decision support into operational processes.