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Powered by Generative AI and Causal AI with customization to fit to any industry.
Advanced root cause analysis, deep insights and integrations with internal systems.
We value your compliance and flexible to deploy on-primises applications at offline.
Our team is keep innovating and committed to continuos improvements.
Our research is to unlock insights and prioritize efforts with Causal and Generative AI solutions
Our platform and servies are deployed with low dependency on manual efforts.
Define the problem and the objectives of the causal analysis.
Clarify the goals of the analysis.
Determine the boundaries of the analysis.
Analyze the data to understand distributions, relationships, and patterns.
Understand causal relationships within the data and summarize
Create visualizations to explore data trends and patterns.
Select relevant variables for the causal analysis.
Assess the importance of each feature and reduce the number of variables.
Identify Outcome, Treatment, Instrumental and Confounding variabales
Identify potential causal relationships from the data.
Use graphical models like Bayesian networks.
Apply algorithms like PC, FCI for causal discovery.
Analyze the identified causal relationships.
Examine direct and indirect paths of influence.
Analyze mediating variables in causal pathways.
Draw causal conclusions from the analysis.
Evaluate hypothetical scenarios to infer causality.
Assess the robustness of causal inferences.
Validate the causal model and results.
Use cross-validation techniques to test the model.
Validate the model with external datasets.
Prepare and present the final report with findings and recommendations.
Generative AI powered Insights on the results.
Write a comprehensive report detailing the findings.
High patient readmission rates lead to increased costs and strain on healthcare resources. Patients often return to the hospital shortly after discharge, indicating that underlying health issues or insufficient post-discharge care are not being addressed effectively. This can result in poor patient outcomes and financial penalties for healthcare providers.
Causal AI can analyze patient data, including medical history, treatment plans, and demographics, to identify underlying causes of readmissions. By understanding which factors, such as specific medications, post-discharge care practices, or patient behaviors, significantly impact readmission rates, healthcare providers can implement targeted interventions.
By identifying and addressing root causes, hospitals can reduce readmission rates, leading to cost savings, improved patient outcomes, and better resource allocation. This also enhances the hospital's reputation and compliance with regulatory standards, ultimately contributing to overall healthcare quality improvement.
High customer churn in a financial services company affects profitability and customer loyalty. Customers may leave due to factors such as poor service, fee increases, or lack of personalized offers. This attrition is costly as acquiring new customers is more expensive than retaining existing ones.
Causal AI can analyze transaction histories, customer interactions, and demographic data to identify the causal factors leading to customer churn. By understanding which specific actions or events, like poor customer service or fee changes, drive customers to leave, the company can develop targeted retention strategies.
By understanding the root causes of churn, the company can implement strategies such as personalized offers, improved customer service, and fee adjustments, leading to higher customer retention, increased revenue, and enhanced customer satisfaction. This strategic focus helps in building long-term customer loyalty and profitability.
Ineffective marketing campaigns lead to low conversion rates and wasted marketing spend. Retailers often struggle to understand which marketing actions are genuinely driving sales, resulting in suboptimal resource allocation and missed opportunities for revenue growth.
Causal AI can examine customer data, purchase histories, and marketing campaign details to identify causal relationships between marketing actions and customer conversions. By determining which campaigns are effectively driving sales, retailers can optimize their marketing strategies.
By pinpointing the most effective marketing strategies, retailers can increase conversion rates, boost sales, and achieve higher ROI on marketing efforts. This leads to better resource allocation, improved customer engagement, and a stronger competitive position in the market.
Frequent equipment breakdowns cause production delays and increased maintenance costs. Unplanned downtime disrupts the production schedule, leading to inefficiencies and higher operational expenses, ultimately affecting the company's bottom line.
Causal AI can analyze equipment performance data, maintenance logs, and production metrics to identify causal factors contributing to breakdowns. By understanding the specific conditions that lead to equipment failure, manufacturers can implement targeted preventive maintenance strategies.
By understanding the root causes of equipment breakdowns, manufacturers can reduce downtime, lower maintenance costs, and improve production efficiency. This results in increased operational reliability, cost savings, and enhanced overall productivity, contributing to a more robust manufacturing process.
Inefficient delivery routes lead to higher fuel costs and longer delivery times. These inefficiencies can result in increased operational expenses and decreased customer satisfaction, as timely and cost-effective delivery is critical in the logistics industry.
Causal AI can analyze route data, traffic patterns, and delivery schedules to identify causal factors affecting delivery efficiency. By determining which variables most significantly impact delivery times and fuel consumption, logistics companies can optimize their delivery routes.
By optimizing routes based on causal insights, logistics companies can reduce fuel costs, shorten delivery times, and improve overall efficiency. This leads to cost savings, increased customer satisfaction, and a competitive advantage in the market, enhancing the company's reputation and profitability.
High energy consumption in industrial processes leads to increased operational costs and a larger carbon footprint. Companies face the challenge of balancing production efficiency with sustainability goals and regulatory compliance.
Causal AI can analyze energy usage data, production schedules, and environmental conditions to identify causal factors driving high energy consumption. By understanding which processes or conditions contribute most to energy waste, companies can implement targeted energy-saving measures.
By implementing energy-saving measures based on causal insights, companies can reduce energy consumption, lower operational costs, and decrease their carbon footprint. This results in cost savings, improved sustainability, and compliance with environmental regulations, contributing to a more sustainable and efficient operation.
Our clients consistently express appreciation for our unwavering commitment to innovation and tailored AI solutions. They commend our team's deep expertise, responsiveness, and ability to translate complex AI concepts into practical, impactful solutions. Client testimonials affirm our dedication to delivering cutting-edge AI services that drive success and exceed expectations.