Semantic Brain Platform: Your Gateway to Innovation, Growth, and Transformation. Welcome to the second in a series of blog posts introducing the Semantic Brain Platform for Generative AI and Analytics. This article provides an overview of how the Semantic Brain Platform can ignite product innovation and offers details with a financial services example.
Understanding the Problems
In current popular consciousness, Artificial Intelligence (AI) often conjures images of ChatGPT, Large Language Models (LLMs), and the realm of Generative AI. The common perception revolves around AI's association with search, chatbots and content generation, sidelining the profound impact it can have on more traditional uses like Enterprise Applications and Analytics.
In this context, current solutions are generally being implemented as large monolithic applications, mirror the early days of Information Technology (IT), and are leading to the following problems:
Language AI Safety & Security: Language AI/LLMs pose new unique security and safety challenges many of which have been identified in the OWASP Top 10 for LLM Applications report. Monolithic architectures either heighten the likelihood of identified risks or amplify the impact of those risks. NOTE: Many of the OWASP Top 10 for LLM Applications risks also apply to Conventional Search(i.e. not Semantic Search) and Chatbots(e.g. Rasa, Dialogflow).
Language & Quantitative AI Dependability
Language AI: LLM Hallucinations(irrelevant or random information that holds no pertinence to either the input or the desired output) are preventing many Language AI/LLM projects from being promoted to production.
Quantitative AI: 70% of Enterprise Quantitative AI projects have fallen short in delivering a return on investment (ROI). The reasons behind these failures encompass various factors, including insufficient quality data hindering project completion, model accuracy issues, and a lack of explainability and reliability, meaning they don't extrapolate effectively.
It is also important to note that LLMs are not very good at quantitative analysis.
Enterprise Integration Challenges: A notable portion, if not the majority or all, of AI products emerging from research lack the essential hooks and harnesses needed for smooth integration within the Enterprise. These challenges span from imperative security requirements like seamless IAM(Identity and Access Management) integration to alignment with architectural principles such as Microservices.
The above factors are limiting AI adoption and success in the Enterprise
Our Approach to Evolving the Enterprise
Our approach to igniting product innovation and transforming your business into a Super Intelligent Enterprise starts with leveraging your conventional software, AI and DevSecOps stack and best practices. Initial starting points can be adding Semantic Shield, Semantic Precision or both.
Semantic Shield: This is an open-source initiative focusing on AI security, safety, and alignment. Tailored for seamless integration with DevSecOps, the product boasts a key feature known as PCL (Policy Configuration Language). PCL facilitates granular policy management through a YAML file, eliminating the need for programming expertise. Semantic Shield has the added benefits of:
Enabling active monitoring of Conventional Search and Chatbots to increase security.
Enabling passive monitoring of all processes(Conventional Software and AI) to enhance security, dependability and product planning.
Semantic Precision: Can seamlessly interoperate with one or both Language AI and Quantitative AI contexts. Language AI is designed to boost dependability using techniques like RAG, validation, and retry/recovery, utilizing PCL (Policy Configuration Language) for easy customization—similar to Semantic Shield.
In the realm of Quantitative AI, Semantic Precision achieves enhanced accuracy (by 5% to 20%) with explainability, all while demanding significantly less data. The approach used to achieve this is presented in more detail in the following blog post.
Product Innovation - Wealth Management Example
A paradigm shift is essential to grasp the transformative power of AI. By reframing AI as 1) an assembly of specialized agents(aka micro-agents), 2) more than just an automation solution—embracing intelligence augmentation and Human-in-the-Loop (HITL), and 3) a superior means for multiple individuals to collaborate seamlessly, we open the doors to a new era of possibilities. In this article, we delve into the untapped potential of AI, exploring how it transcends the limitations of conventional thinking, redefining the landscape of Enterprise innovation and collaboration.
The diagram and accompanying descriptions below demonstrate the synergy between micro-agents and Human-in-the-Loop (HITL) to provide robust capabilities while enhancing dependability and security. The showcased use case involves a retail investor inquiring, 'Should I invest $10K in Apple stock?' This example represents one of several potential designs for delivering wealth management services, with the depicted sequence serving as a plausible happy path. Together, these elements underscore the potential effectiveness of combining micro-agents and HITL.
Note: In the diagram above agents such as Investor Proxy, Advisor Proxy and Investment Analysis Agent are not deployed but rather instantiated on demand.
Key Components
Potential wealth management solution design
Application Front-End: Companies have the flexibility to utilize application front-ends as-is or incorporate conversational interfaces based on their specific needs.
Semantic Shield, Investor Proxy & Advisor Proxy
Investor Proxy & Advisor Proxy: Both integrate Semantic Shield. The Investor Proxy is tailored with access rights and capabilities specific to investors, providing information such as the current portfolio and investment history. Similarly, the Investment Advisor Proxy caters to the requirements of investment advisors.
Semantic Precision & Investment Analysis Agent
Investment Analysis Agent: This a specialized component that encapsulates Semantic Precision, dedicated to conducting accurate analyses of equities.
Plugins-Investing: LLMs can directly integrate with these backend services for data acquisition and computations. Investing organizations can efficiently reuse many of their existing backend services either as they are or with minimal modifications.
ConversionAI-Analytics: Scrutinizes data on product usage and language input and output to provide recommendations, facilitating ongoing enhancements for the product.
Sample Sequence
A plausible happy path
User inputs "Should I invest $10K in Apple stock?"
The user input is forwarded to the Investor Proxy.
Investor Proxy analyzes the user prompt. The component retrieves the investor's current portfolio and determines she is currently underinvested in tech. This activity assumes that the Investor and Investor Proxy are the only entities with access to the investor's portfolio and historical investment activity.
Investment Analysis Agent performs a fundamental and technical analysis of Apple stock.
Advisor Proxy looks up analyst reports for Apple stock, and the reports state that the Apple stock is a buy. This activity assumes that the Advisor and Advisor Proxy are the only entities with access to the analyst report.
A consolidated message(based on the current portfolio, fundamental and technical analysis, and analyst reports) recommending buying Apple stock is forwarded to the Advisor.
The Advisor agrees with the analysis, makes minor edits to the report and recommends buying Apple stock.
$10K of Apple stock purchase is recommended to the Investor and a custom report with the justification and related risks is sent to the Investor.
Product Innovation & AI in Enterprise
At the core of integrating AI into Enterprise practices lies the fundamental objective of harnessing knowledge, particularly proprietary knowledge, and perpetually expanding this reservoir to enhance product and service offerings. The utilization of AI becomes pivotal in this pursuit, allowing organizations to glean insights, refine processes, increase efficiency, and ultimately deliver superior products and services.
When considering the deployment of AI, especially in contrast to tools like ChatGPT, it becomes evident that the latter may not effectively leverage proprietary knowledge. Relying solely on ChatGPT for product and service delivery falls short of harnessing the internal expertise and proprietary insights crucial for sustained innovation.
DevSecOps and Product Innovation
DevOps revolutionizes software development, accelerating product delivery and enhancing competitiveness. DevSecOps extends this paradigm, integrating security throughout the software development lifecycle for robust applications.
Semantic Shield, with its AI Security, Safety, and Alignment, becomes integral to DevSecOps. This integration ensures the swift and secure deployment of AI-infused products. A key advantage is the Policy Control Language—an easy-to-use YAML file—for scalable security policies without coding.
By adopting DevSecOps with Semantic Shield, organizations can expedite the release of secure AI-driven products, striking a balance between speed and safety.
Semantic Brain Platform and Product Innovation
Empowering product innovation, the Semantic Brain Platform seamlessly integrates with existing Language and Quantitative AI stacks, offering continuity for customers. Its adaptable architecture allows numerous backend services to serve or be modified as plugins, enriching operations with domain-specific data and calculations, including robust financial analysis.
Augment your stack with the pivotal core add-ons: Semantic Shield and Semantic Precision. By integrating Semantic Shield, you fortify your system, enhancing Security, Safety, and Alignment. Meanwhile, Semantic Precision, utilizing techniques like RAG, significantly boosts dependability, laying the groundwork for a resilient and reliable AI infrastructure.
By implementing these core add-ons, customers achieve the flexibility to develop a range of products. These products can encompass functionality facilitated by interactions, spanning from straightforward scenarios where individual user groups (i.e., single roles) engage with a single specialized agent, to more intricate situations where multiple user groups (i.e., multiple roles) interact with diverse configurations of specialized agents. The Semantic Brain Platform offers reference implementations tailored for specific industries, including Financial Services and generic Customer Services Bots, streamlining the development process.
To further refine products, leverage ConversionAI-Analytics. Conduct Semantic and Behavioral product usage analysis using tools like Google Analytics 4, Adobe Analytics, MixPanel, and Google Search Console. These tools not only enhance external customer experiences but also optimize internal processes, fostering a culture of continuous improvement and innovation. The Semantic Brain Platform serves as a catalyst, propelling Enterprises into a future where product innovation is not just a goal but a dynamic reality.
Conclusion & Recommendations
Automating customer service and content generation is just the beginning as businesses explore AI for innovation. Despite potential cost savings, focusing solely on these areas poses short-term challenges, particularly regarding safety and reliability—a high-stakes proposition.
On the flip side, strategically deploying micro-agents for product enhancement provides immediate results and significantly reduces security concerns. This approach not only mitigates risks but also promises a higher ROI.
The real opportunities lie in enhancing existing software with AI. Integrating AI seamlessly into the development lifecycle, combining AI with DevSecOps, accelerates the creation of robust applications.
Creativity is key in navigating this landscape. Enterprises should assess their products, identifying areas with high returns and low risks. The time to embark on this journey is now. The Semantic Brain offers versatile capabilities, ready to propel businesses into a future where AI-driven innovation is not just possible but strategically imperative.
Related References
Whitepaper describing how Semantic Shield can be integrated with DevSecOps to accelerate the development of secure and robust AI/Hybrid Apps - https://www.semanticbrain.net/whitepaper
BizML technology developed by Semantic Brain increases the accuracy and reliability of Quantitative AI and is a key component of Semantic Precision and ConversionAI - https://www.semanticbrain.net/post/introducing-bizml-business-machine-learning-optimizer
Microservices architecture for Quantitative AI that works well with Micro-Agents - https://www.semanticbrain.net/post/the-future-of-ai-agile-microservices