Frequently Asked Questions
Everything About Causal AI
Everything you need to know about causal AI, the Causify platform, and how we help enterprises make better decisions.
General & Causal AI
Causal AI goes beyond pattern recognition to identify true cause-and-effect relationships in data. While traditional AI asks "What is likely to happen?", causal AI asks "Why does it happen?" and "What would happen if we intervened?" This enables more robust, explainable, and actionable predictions.
Predictive AI finds correlations in historical data to forecast outcomes. Causal AI identifies the underlying causal mechanisms that drive those outcomes. This means causal AI predictions remain accurate even when conditions change, and they tell you what to do about it, not just what will happen.
Correlations can be spurious, two variables may move together without one causing the other. Acting on spurious correlations leads to wasted resources and missed opportunities. Causal AI separates true drivers from coincidental patterns, ensuring your decisions are based on real cause-and-effect.
Causify uses causal inference and causal discovery rather than purely correlational machine learning. Every prediction includes a causal explanation showing WHY the model made that prediction. Our models are robust to distribution shifts and enable counterfactual scenario testing.
Causify serves energy (grid optimization, renewable curtailment), industrial operations (predictive maintenance), capital markets (portfolio optimization), and consumer packaged goods (demand sensing). If your industry requires high-stakes decisions where understanding causation matters, Causify can help.
Every Causify prediction includes the full causal chain that led to the output. This is not post-hoc feature importance, it is a structural explanation of the causal pathways. Decision-makers see exactly which factors caused the prediction and can verify the reasoning.
A causal graph is a visual and mathematical representation of cause-and-effect relationships between variables. Nodes represent variables, and directed arrows represent causal relationships. Causify automatically discovers causal graphs from your data using algorithms like PC, FCI, GES, and NOTEARS, combined with domain knowledge constraints.
Counterfactual analysis answers "what if" questions, such as "What would have happened if we had taken a different action?" This is critical for evaluating past decisions, planning interventions, and testing strategies before deploying them. Only causal AI can perform true counterfactual reasoning.
Causal relationships are more stable than correlations when underlying conditions change. While correlational models break during supply shocks, policy changes, or market regime shifts, causal models identify the stable mechanisms that persist across regimes, producing predictions that adapt rather than degrade.
The Ladder of Causation, formalized by Turing Award winner Judea Pearl, describes three levels of causal reasoning: association (what happened), intervention (what will happen if we do X), and counterfactual (what would have happened). Traditional AI operates at level 1. Causify operates at all three levels.
Platform & Technical
Causify connects to your existing data sources including IoT sensors, ERP systems, SCADA, market data feeds, weather APIs, and more. The platform normalizes and aligns heterogeneous data automatically. Typical integrations take days, not months.
Causify supports time-series sensor data, transactional databases, REST APIs, flat files (CSV, Parquet), cloud data warehouses (Snowflake, BigQuery, Redshift, Databricks), streaming data (Kafka, Kinesis), and third-party data providers. Custom connectors can be built for proprietary sources.
Typical implementations take 4-8 weeks from data connection to production deployment, depending on data readiness and use case complexity. We offer a structured proof-of-concept phase to demonstrate value before full deployment.
Yes. Causify holds SOC 2 Type II certification, covering security, availability, and confidentiality controls. This means our security practices have been independently audited and verified by a third-party auditor.
Causify provides a 99.9% uptime SLA for production deployments. Our infrastructure is designed for mission-critical reliability with geographic redundancy, automated failover, and continuous monitoring.
Yes. Causify supports both batch and real-time streaming data. Our inference engine delivers sub-10ms latency for real-time predictions, suitable for operational use cases that require immediate action.
Yes. Causify supports deployment in customer VPC (AWS, Azure, GCP), Causify-managed cloud, or hybrid configurations. On-premise deployment ensures your data never leaves your infrastructure, meeting the most stringent data sovereignty requirements.
Causal discovery typically requires more data than simple correlation analysis. However, Causify algorithms are designed to work efficiently with moderately sized datasets, and domain knowledge integration allows the platform to incorporate expert constraints that reduce data requirements. During the proof-of-concept, we assess your data sufficiency.
Causal drift detection monitors whether the causal relationships in your production data still match the relationships the model was trained on. Unlike traditional model monitoring that only watches prediction accuracy, causal drift detection catches structural changes in your system before predictions degrade.
Causify encrypts data at rest (AES-256) and in transit (TLS 1.3). We support role-based access control, comprehensive audit logging, GDPR and CCPA compliant data handling, and on-premise deployment for organizations with strict data sovereignty requirements.
Solutions
Sentinel builds causal models of equipment behavior from sensor data, maintenance logs, and operational parameters. When the model detects a causal pathway activating toward failure, it generates an alert with the specific causal chain and recommended intervention, not just a threshold alert.
Curtailment is the deliberate reduction of renewable energy output, costing the US industry $3.1 billion annually. Grid builds causal models of curtailment drivers, including weather, demand, and transmission constraints, and recommends optimal dispatch strategies to maximize renewable utilization.
Horizon identifies the causal drivers of demand rather than just extrapolating past trends. By understanding what actually causes demand changes, including promotions, weather, pricing, and competitor actions, Horizon produces forecasts that remain accurate even when conditions change.
Traditional portfolio optimization relies on historical correlations that break down during market regime changes. Optima identifies causal relationships between assets and macro factors, producing portfolios that are robust across market conditions and deliver up to 25% better Sharpe ratios.
Horizon CPG connects to your sales channels and builds a causal model of sell-through drivers. Instead of reacting to declining inventory, it predicts stockouts by identifying the causal factors driving demand, enabling proactive reordering before stockouts occur.
Sentinel typically provides 14 to 21 days advance warning for equipment failures, depending on the failure mode and sensor data availability. Some gradual degradation patterns can be detected even earlier, while acute failures may have shorter warning windows.
Yes. Grid works with all renewable generation types including solar, wind, and hybrid facilities. It models the causal relationships between weather patterns, generation output, grid constraints, and market prices for each generation type separately.
Yes. Horizon uses causal analogs to forecast demand for new products. By identifying products with similar causal drivers (price sensitivity, seasonality patterns, target demographics), Horizon generates accurate forecasts even without historical sales data for the specific product.
Optima is designed for regime change resilience. During the 2022 simultaneous stock and bond drawdown, backtests showed Optima would have detected the changing interest rate and inflation regime and adjusted allocations accordingly, reducing maximum drawdown by up to 40% compared to correlation-based optimization.
Horizon CPG integrates natively with Shopify (V1), Amazon Seller Central, Walmart Marketplace, SAP, and Target+. Additional integrations include Faire, distributor portals, and 3PL systems. New platform connectors are added based on customer needs.
Business & Getting Started
Causify pricing is based on the solution, data volume, and deployment model. We offer flexible pricing structures including annual subscription and usage-based models. Contact our team at causify.ai/contact for a customized quote and ROI assessment.
ROI varies by use case: Sentinel customers see $2M+ in annual savings from reduced downtime. Grid customers save $5M+ through reduced curtailment. Horizon customers save $5M+ from improved forecast accuracy. Optima customers see 25% better risk-adjusted returns. We provide an ROI assessment during the proof-of-concept phase.
Visit causify.ai/contact to request a demo. Our team will schedule a personalized walkthrough of the platform tailored to your industry and use case, including a discussion of your data readiness and potential ROI.
Yes. We offer a structured proof-of-concept engagement where we connect to your data, build a causal model, and demonstrate measurable value, typically within 4-6 weeks. This de-risks the investment and provides clear ROI evidence before full deployment.
Causify provides dedicated customer success managers, technical onboarding, user training, and ongoing support. Enterprise customers receive 24/7 support with guaranteed response times. We also offer regular business reviews and platform update briefings.
Building a custom causal AI solution requires specialized expertise in causal inference, months of development, and ongoing maintenance. Causify provides a production-ready platform that accelerates time-to-value from months to weeks, with built-in best practices, enterprise security, and continuous updates.
No. Causify is designed for both technical and non-technical users. The platform automates causal discovery and model building. Business users can interact with causal graphs, run scenarios, and view explanations through intuitive dashboards. Data science teams can access deeper configuration and customization options.
Yes. Many customers start with one solution (for example, Sentinel for predictive maintenance) and expand to additional use cases. The platform architecture supports multiple models, data sources, and deployment targets within a single tenant.
Causify includes built-in data quality assessment as part of onboarding. The platform detects missing data, outliers, and inconsistencies, and uses causal imputation methods that are more robust than simple statistical interpolation. During proof-of-concept, we provide a detailed data readiness assessment with specific improvement recommendations.
We offer flexible contract terms. Most enterprise customers start with a proof-of-concept engagement followed by an annual subscription. Contact our sales team at causify.ai/contact to discuss the arrangement that best fits your organization.