Harness the power of AI analytics for smarter decisions and optimal outcomes
Decision Analysis powered by AI models and algorithms (AInalyst) supports decision makers to evaluate the alternatives in situations involving uncertainty and multiple criteria with the objective of optimizing the value of the outcomes to make data driven decisions.
Evaluate Decision alternatives by combining internal and external data sources that provide context beyond the scope of the individual event or transaction, enabling more accurate decisions.
AInalyst leverages AI and machine learning algorithms to analyze vast amounts of data to identify patterns, trends, and anomalies. AInalyst integrates various data analysis methods and predictive analytics to assist decision-makers in real time.
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Optimizing Outcomes with Decisions Powered by AI
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Empower your teams with Knowledge, Insights, and Models to decide better!
Choosing the most appropriate decision analysis method depends on the specific problem, available data, and the preferences and constraints of decision-makers. Decision AInalyst uses a combination of methods to address different aspects of a complex decision.
Knowledge Graph
Knowledge Graph makes it easier for decision-makers to access relevant knowledge and expertise from multiple sources within an organization. Knowledge Graphs are used for recommendation systems, semantic search, and natural language understanding.
Natural Language Processing (NLP)
NLP methods based on Large Language Models (LLMs) extract insights from unstructured text data, such as customer feedback, reviews, or social media content for quick data processing.
Simulation
Simulation tools allow decision-makers to create virtual models of real-world scenarios. By simulating different decisions and their potential outcomes, decision-makers can assess the consequences of their choices before implementation.
Decision Analysis
Decision analysis compares alternatives against criteria, providing a structured way to evaluate and score options.
Diagnostic analysis
Diagnostic analysis focuses on historical data to understand the causes of past events or outcomes. It identifies patterns, trends, and anomalies in data to diagnose the factors that contributed to a specific event or result.
Sensitivity Analysis
Sensitivity analysis assesses how changes in input variables affect the outcomes and helps identify critical factors in decision models.
Decision Trees
Decision trees visually represent decisions, outcomes, and probabilities in a tree-like structure. They are helpful in modeling sequential decisions and analyzing uncertainty.
Scoring
Calculate the score about a transaction, customer, channel partner, or any entity based on current and historical information. The score indicates risk, propensity, or likelihood and can be used to simplify the decision to escalate, auto-process, or seek additional information.
Improved Decision Quality, Resource Optimization
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Improved Decision Quality
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Resource Optimization
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Cost Savings
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Continuous Improvement
Improved Decision Quality
By incorporating quantitative analysis and artificial intelligence and machine learning modeling, decision analysis removes the time consuming task of analyzing data and analyzes the quality of decisions. Ultimately, leading to better business outcomes and putting you at a competitive advantage.
Resource Optimization
Decision analysis helps allocate resources more efficiently by identifying the most cost-effective and beneficial alternatives.
Cost Savings
By helping organizations make more informed decisions, decision analysis can lead to cost savings through better resource allocation and risk management.
Continuous Improvement
Decision Analysis supports ongoing learning and adaptation by providing a framework for reviewing and revising decisions based on new information or changing circumstances.
FAQ on Decision AInalyst
How does Decision AInalyst differ from Business Intelligence?
Business Intelligence (BI) primarily focuses on descriptive analytics, which involves summarizing historical data to provide insights into what happened in the past. Decision AInalyst, using AI-powered analytics, encompasses a wider range of analytics, including descriptive, predictive, and prescriptive analytics, enabling organizations to forecast future outcomes and recommend actions to optimize results.
BI is commonly used for generating regular operational reports, tracking key performance indicators (KPIs), and producing standard management dashboards. Decision AInalyst, using AI-powered analytics, is suitable for use cases that require predictive capabilities, such as demand forecasting, fraud detection, recommendation systems, and natural language processing. Decision AInalyst is also used for prescriptive analytics, which recommends specific actions to optimize outcomes.
What format can data and knowledge sources be?
Decision AInalyst can handle both structured and unstructured data. It is designed to work with a variety of data sources, including text, images, audio, and video. This flexibility allows it to extract knowledge and insights from a wider range of data types.
How will Decision AInalyst integrate with existing systems?
Circuitry.ai's Decision intelligence platform includes a robust ETL tool with 400+ connectors to integrate with existing enterprise software and data sources. Decision AInalyst also uses powerful data visualization software that can be accessed from enterprise applications. Decision AInalyst services are available as REST API services to support seamless integration with existing business processes and applications.