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Super-Intelligent Workflows: A Revolutionary Approach to AI

Updated: Dec 26, 2023

Big Tech giants like Microsoft and Google, alongside innovative startups such as OpenAI and Anthropic, are actively pursuing the development of highly intelligent systems and Artificial General Intelligence (AGI). The aim is to create technologies that can automate a myriad of tasks and functions. However, the alignment of this goal with societal and business needs remains a subject of debate, as does the technical feasibility of achieving AGI.

From a business standpoint, the aspirations for AI include:

  1. Protection and Growth of Intellectual Property/Domain Expertise: Businesses seek AI capabilities to safeguard and enhance their intellectual property and domain expertise.

  2. Optimization and Continuous Improvement of Business Processes: The desire is to leverage AI to optimize existing business processes continually, enhancing overall efficiency.

  3. Effective Customer Acquisition, Service, and Upselling: AI is seen as a tool to streamline customer-related activities, from acquisition to service, and to enhance upselling opportunities.

  4. Continuous Learning Across People, Systems, and Knowledge Bases: Businesses aim to deploy AI for ongoing learning, encompassing insights from individuals, system performance, and knowledge base expansion.

The introduction of Super-Intelligent Workflows arises as a strategic solution to meet these business goals. By incorporating artificial intelligence into the current organizational workflow, we achieve capabilities that far surpass conventional performance levels. This innovation holds the potential to achieve a substantial increase, possibly exceeding tenfold (i.e., 10X+). The promise of Super-Intelligent Workflows lies in delivering heightened efficiency without compromising the reliability of existing workflows or the overall security of the corporate environment.

In this article, we delve into the transformative potential of Super-Intelligent Workflows and their impact on advancing business operations and start by defining Knowledge Work and Workflows.

Definition: Knowledge Work

Knowledge work refers to tasks and activities that primarily involve the use of intellectual skills, expertise, and the application of knowledge to create value or solve complex problems. Unlike routine or manual work, knowledge work relies on cognitive abilities, specialized knowledge, and critical thinking. Professionals engaged in knowledge work often analyze information, make decisions, and generate creative solutions. Examples of knowledge work include research and development, software development, legal analysis, financial analysis, design, strategic planning, healthcare diagnostics, and project management. Knowledge work is essential in modern economies, emphasizing innovation, problem-solving, and the application of specialized knowledge to achieve organizational goals.

We've created Super Intelligent Workflows designed to enhance the efficiency of Knowledge Work and its associated domains such as Data Acquisition and Entry.

Definition: Workflows

A workflow refers to a series of organized and repeatable activities or steps designed to achieve a specific outcome or goal. Workflows are commonly used in various fields and industries to streamline processes, improve efficiency, and ensure that tasks are carried out systematically and consistently.

Key elements of workflows include:

  1. Sequence of Steps: Workflows consist of a sequence of steps or tasks that need to be performed in a particular order. Each step typically has a specific purpose and contributes to the overall goal of the workflow.

  2. Roles and Responsibilities: Workflows often involve different individuals or roles responsible for carrying out specific tasks. Clear delineation of responsibilities helps ensure that each task is performed by the appropriate person or team.

  3. Input and Output: Workflows require input, which can be information, materials, or resources, to initiate and complete tasks. The output is the result or outcome of the workflow.

  4. Automation and Technology: In modern contexts, workflows are often supported by automation tools and technology. Workflow management systems help automate routine tasks, track progress, and facilitate communication among team members.

  5. Feedback and Iteration: Workflows may include mechanisms for feedback and iteration. This allows for continuous improvement and adaptation of the workflow based on insights gained during the process.

Workflows are utilized in a wide range of fields, including business, project management, manufacturing, healthcare, and information technology. They can be represented visually through flowcharts or diagrams to clearly visualize the sequence of tasks and dependencies.

For example, a sales workflow might include steps such as lead generation, customer contact, product demonstration, negotiation, and closing the sale in a business setting. Each step is crucial to the overall success of the sales process, and a well-defined workflow helps ensure consistency and effectiveness.

Enterprise Alignment & Integration

Possessing advanced standalone AI capabilities does not inherently guarantee the provision of more valuable enterprise solutions. To succeed in an enterprise context, AI solutions must also facilitate the integration of private data and quantitative capabilities, encompassing proprietary formulas.

Furthermore, enterprise solutions must take into consideration:

  1. Organization Structure and Business Processes

    1. Roles and Responsibilities

    2. Domain Expertise

    3. Access Rights

  2. Systems

  3. Security & Safety Procedures

  4. Technical Delivery Capabilities

Semantic Brain’s Semantic Shield and Semantic Precision have been designed to be readily deployed within and seamlessly integrated into enterprise environments.

Both Semantic Shield and Semantic Precision are easily configurable with No-Code/Low-Code using PCL (Policy Control Language), which is based on YAML. This eliminates the necessity for programming expertise, making it accessible for security, operations, and business professionals to customize.

For a comprehensive understanding of how Semantic Shield can be seamlessly integrated with DevSecOps to provide AI security, safety, and alignment, please refer to the whitepaper available. YAML is a standard in enterprise settings, especially in DevOps.

Semantic Brain Approach

AI Innovation

BizML(Business Machine Learning) - Quantitative AI

Numerous business applications demand a combination of language and quantitative analysis. While ChatGPT/LLMs may not excel at quantitative analysis, traditional Quantitative AI faces challenges, with an estimated 70% of projects failing to achieve a return on investment, often due to insufficient high-quality data.

Semantic Brain's Super-Intelligent Workflow solution takes a different approach for Quantitative Analysis. Instead of relying on LLMs, it utilizes:

  1. Conventional software for deterministic calculations.

  2. BizML for stochastic calculations.

With BizML, companies harness domain expertise for Feature Engineering and Feature Selection, resulting in solutions that:

  1. Demand considerably less data, occasionally functioning effectively with very small datasets (e.g., 68 records).

  2. Exhibit a 5% to 20% improvement in accuracy or reduce the error rate by 10% to 50%.

In essence, BizML has the potential to significantly enhance business outcomes.

SAG(Semantic Augmented Generation) - Language AI

Existing approaches for leveraging private data encompass:

  1. RAG(Retirieval Augmented Generation)

  2. Fine-tuning and constructing domain-specific LLMs

While employing these methods individually or in combination (1 & 2) yields satisfactory outcomes in various domains (e.g., analyzing a 10-K SEC filing on EDGAR),

Semantic Brain has introduced a pioneering approach that combines LLM, Graph DB, and RAG to achieve significantly superior results.

Architecture & Packaging

As previously discussed in the "Enterprise Alignment & Integration" segment, Semantic Brain's offerings, Semantic Shield, and Semantic Precision, can be seamlessly implemented and integrated into enterprise environments. These products seamlessly connect with DevSecOps tools and adhere to DevSecOps best practices, expediting the delivery of secure and reliable solutions. Tailoring to enterprise requirements, multiple instances of Semantic Shield and Semantic Precision can be deployed.

During runtime, Semantic Shield and Precision generate Multi-Agent Multi-User Agents. These agents amalgamate specific AI algorithms/tools and data, and their conduct is configured through PCL (Policy Configuration Language). The agents are terminated upon the completion of the workflow.

Metrics & Examples

The assessment and enhancement of performance differ from one use case to another. In specific instances, there might be the opportunity to explicitly define performance improvements. An illustrative example for Semantic Brain involved achieving a remarkable increase of up to 25 times more conversions per advertising dollar spent.

In the majority of cases, our methodology for performance evaluation follows the formula:

This comprehensive approach allows for a nuanced evaluation of performance, considering both quality and quantity improvements across various scenarios; and can be visually represented as follows.

Sample workflows and related quality and quantity metrics


Quality Metric(s)

Quantity Metric(s)


% of wins

Number of trades

Call Center

First Call Resolution Rate

Call Handling Time(inverted)

Social Media Posts

Views per Post/Engagement Rate

Number of Social Media Posts

Conclusion & Recommendations

Semantic Brain's Semantic Shield and Semantic Precision are meticulously crafted to empower Super-Intelligent Workflows. Following deployment, customization becomes straightforward through the editing of PCL (Policy Control Language), facilitating the creation of easily configurable workflows accommodating multiple users and agents.

Importantly, this design is seamlessly compatible with your existing systems—namely, Language AI, Quantitative AI, Backends(operating as plugins) and DevSecOps.

A notable strength lies in its capacity to harness domain expertise within organizations. The solution is intelligently divided into smaller knowledge domains, allowing Language AI optimization through SAG and Quantitative AI optimization through BizML.

This plugin architecture seamlessly integrates with your current Language AI and Quantitative AI stack, allowing the incorporation of best-of-breed systems throughout your organizational journey. As a result, Super-Intelligent Workflows not only initiate immediate improvements in knowledge worker productivity from day one, fostering profit growth and competitive advantage, but they also enable rapid scalability, continually empowering you to extend your leadership position.

In contrast to contemporary approaches to AI that emphasize cost-cutting and replacing people, our dedication at Semantic Brain is to deliver transformative enhancements. Our initial focus is on achieving order-of-magnitude improvements (10X+) for workflows within the Financial Services vertical and Integrated Marketing, Sales & Support horizontals. Rooted in an inherently vertical and horizontal agnostic approach, we ensure applicability across diverse industries. Looking forward, our commitment extends to making a substantial impact in the Healthcare sector, marking it as our next priority. By consistently pushing the boundaries of innovation and adaptability, Semantic Brain remains at the forefront, driving significant advancements in workflow optimization across various domains while distinctly focusing on profit growth and augmenting people.

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