Introduction

The future of Workflow Automation is being driven by advancements in Artificial Intelligence at an ever increasing and evolving pace.

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THE AUTOMATION REVOLUTIONAI Driven Workflow Automation

Artificial Intelligence (AI) is the future of Worklow Automation. This new artificial workforce will achieve new heights of innovation, productivity, and competition. Coupled with advancements in automation technologies across all major industries the Automation Revolution is taking its hold.

With the introduction of Advanced Rules Engines, Expert Systems which mimick the decision-making process of human experts, and the expansion of Algorithmic Toolkits, we are entering an age where AI is quickly becoming an integral part of our society.

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MODELS AND EXPERT SYSTEMSAnalytics and Workflow

AI Models are programs that apply one or more algorithms to detect specific patterns using a collection of data sets. These Models are able to make predictions or make decisions without human intervention.

Expert Systems (ES) are AI programs designed to mimic the decision-making abilities of a human expert in a specific domain, which utilize expert knowledge stored in a Knowledge Base. The integration of AI Models with Expert Systems result in powerfull applications that are both adaptive and well managed.

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AGENTS AND TOOLSPerception, Learning, Reasoning, Knowledge, Language, and Work

AI Agents are programs designed to perceive their environment, make decisions and take actions to achieve a specific goal or set of goals. Agents operate autonomously, meaning they are not directly controlled by a human operator. Agents are able to interact with an environment, while Models are fundamentally passive.

AI Tools are programs that use algorithms to perform specific tasks and solve problems. These tools further extend the functionality of Agents, and define the methods and processes to achieve specific goals resulting in greater control over actionable outcomes.

SERVICES

Our solutions comprise a unique blend of programming techniques to produce AI Models, Agents, Tools, and Program Libraries. Our services include Input Engineering for tuning Foundational AI Models and putting AI Applications to work for you.

  • MODELING

    AI Models are defined by the ability to autonomously make decisions or predictions, rather than simulate human intelligence. These models enabled the programs to take action in direct response to inputs, rather than follow a pre-scripted series of instructions. Complex systems often employ multiple models simultaneously, using ensemble learning techniques, while versatile foundation models are capable of a wide array of applications which can be fine-tuned for specific tasks. A combination of these paradigms will likely become commonplace.

  • AGENTS

    AI Agents, also refered to as "Intelligent Agents," are autonomous programs that perceive and act upon an environment using sensors and actuators, as well as run through cycles of programming to perform functions utilizing AI Tools to make decisions, take actions, and achieve goals. In addition, Intelligent Agents may learn from the environment to achieve those goals. Intelligent Agents are designed to interpret perceptions, reason, and make rational decisions to act in the most efficient way that yields the most positive or desired outcome.

  • TOOLS

    AI Tools provide functionality to AI Agents through the extensive use of algorithms. Algorithms are the procedures, often described in mathematical language or pseudocode, to be applied to a dataset to achieve a certain function or purpose. These tools enable enhanced automation, data collection, data processing, data analysis, content generation, and much more. AI Tools are the engines of intelligence that have revolutionized the way we process, analyze, and interpret data, thus accelerating discovery while greatly enhancing our predictive capabilties.

  • LEARNING

    AI Models capable of Machine Learning (ML) are able to autonomously optimize their performance over time. Elementary AI Models are a series of if-then-else statements, with rules programmed explicitly by a data scientist. Such models are alternatively called Rules Engines, Expert Systems, Knowledge Graphs or Symbolic AI. ML Models use Statistical AI rather than Symbolic AI. Whereas rule-based AI Models must be explicitly programmed, ML Models are “trained” with sample datasets whose data points serve as the basis for future predictions.

PHILOSOPHY

Our philosophy is based on the integration of our core principles of Information, Impact, Reason, Action, and Accounting into our solutions.

  • 1

    INFORMATION

    DATACOLLECTION

    DATAANALYSIS

    VARIABLEIDENTIFICATION

    SORTINGPRIORITIZATION

    IMPACT

  • 2

    IMPACT

    NEGATIVEIMPACT

    POSITIVEIMPACT

    POTENTIALIMPACT

    INDIFFERENCENO IMPACT

    REASON

  • 3

    REASON

    VARIABLEINFERENCE

    LOGICOPERATIONS

    PARAMETERESTABLISHMENT

    MODEL/PROCESSGENERATION

    ACTION

  • 4

    ACTION

    EXECUTEMODEL/PROCESS

    OUTPUTANALYSIS

    BACKPROPAGATIONANALYSIS

    MODEL/PROCESSEVOLUTION

    ACCOUNTING

  • 5

    ACCOUNTING

    DATAANALYSIS

    IMPACTANALYSIS

    PARAMETERANALYSIS

    OUTPUTANALYSIS

    INFORMATION

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