Explainable auto-remedies fuelled by advanced GenAI
Billions have poured into GenAI, prompting major tech players to explore its potential beyond mere trivia such as image/video processing and knowledge summaries. However, its adoption in industrial settings faces significant hurdles, notably the "hallucination" issue. Leveraging GenAI for problem-solving necessitates a clear understanding of the problem at hand. Current GenAI initiatives primarily focus on tasks like knowledge modeling but often fall short in grasping problem complexities, leading to hallucinations or irrelevant outputs. Waylay pioneering approach tackles these weaknesses head-on. By integrating machine data, Causal graphs and outcome insights into the training process, Waylay not only enhances the effectiveness of GenAI models but also ensures their high explainability, marking a significant advancement in AI deployment.
Decoding the Future: A Decade of Automation Excellence, GenAI Breakthroughs,
and the Quest for Causality and Explainability
Waylay Automation/Rules Engine
Over the past decade, our automation platform has undergone continuous innovation and evolution. We have incorporated cutting-edge technologies, such as artificial intelligence and machine learning, to enhance efficiency and drive digital transformation.
Throughout this journey, the platform has empowered businesses across diverse industries, making automation a cornerstone of streamlined processes and intelligent decision-making.
Now we are poised to push the boundaries even further.
We assessed rules engine technologies across seven key capabilities
Waylay Rules Engine - Our Foundation Model
We have rigorously evaluated various prevalent types of rules engine technologies by subjecting them to comprehensive testing and scoring based on seven crucial capabilities: Modeling complex logic, Modeling time, Modeling uncertainty, Explainability, Adaptability, Operability, and Scalability. Our Rules Engine benchmarks white paper provides a detailed testing benchmark, aiding in the assessment of these core rules engine capabilities. Rules Engine benchmarks white paper.
Why is the explainability important?
- Explainable rules provide transparency into how decisions are made.
- Identifying Bias: Explainable rules can reveal biases present in the model or the data it was trained on.
- Error Detection and Correction: When LLM-generated outputs are incorrect or undesirable, explainable rules facilitate the identification of errors.
- Compliance and Regulation: In regulated industries such as finance, healthcare, and legal sectors, explainability is often a legal requirement.
- User Understanding: Explainable rules make it easier for end-users to understand how their inputs are processed and interpreted by the LLM.
- Feedback and Improvement: Interpretable rules enable users to provide feedback on the model's decisions.
Generative AI: Unleashing Creative Machines
Generative AI: Unleashing Creative Machines
Generative AI, a pinnacle in technological advancement, empowers machines to autonomously create content across various domains. In Natural Language Processing, models like GPT produce coherent and contextually relevant text, revolutionizing content creation and virtual communication. Beyond creativity, generative AI contributes to simulations and data augmentation, enhancing the robustness of AI systems.
GenAI and Causal Modeling: Crafting Robust Solutions with Intelligent Precision
It is essential to acknowledge that existing models exhibit a degree of hallucination, and their outputs should not be solely relied upon for critical use cases. To address this, we emphasize the importance of combining these models with carefully curated Waylay training sets. These sets delineate causal relations between data and the remedies proposed by our intelligent bots, ensuring a more robust and reliable solution.
Waylay GenAI powered apps
Building GenAI apps
With Waylay, the possibilities for building diverse GenAI applications are endless. Whether you need one-off API endpoints for root cause analysis ("rules explainer") or to request specific remedies, want to craft conversational apps with LLM-neutral bots in just a few hours, or aspire to develop complex multi-agent GenAI applications using Waylay's orchestration framework, you can do it all seamlessly.
01 | Low Code GenAI API builder (LLM neutral)
In under an hour, you can construct a single-entry API endpoint capable of unleashing the full potential of GenAI in conjunction with any third-party API endpoint, seamlessly integrating it into your application. Additionally, you can design an intuitive interface offering multiple choices, all while retaining complete control over the responses, which are subsequently directed to various LLM endpoints—whether with or without RAG — allowing for management of the generated responses. This streamlined process mirrors the architecture behind the Waylay Rules Explainer.
02 | Conversational Apps and Bots
Easily integrate any third-party APIs into your conversational bot with just a single click. Unlike traditional conversational bot development, often taking weeks or even months, the ability to swiftly create LLM-neutral bots enables organizations to deploy solutions rapidly. This agility allows businesses to address immediate needs and respond promptly to evolving market demands, staying ahead of the curve in today's fast-paced digital landscape.
03 | Building mult-agent GenAI apps
Building multi-agent GAI applications involves creating systems that can perform tasks across various domains and interact with their environment and each other autonomously. These applications have the potential to revolutionize various industries by enabling intelligent systems to work together autonomously to achieve complex goals. With Waylay, you can orchestrate different GAI agents and deploy new apps in few hours.
Towards Building Natural Automation Interface (NAI)
Intelligent Bots (iBots)
Waylay introduces a groundbreaking approach to deploying Intelligent Bots (iBots), setting them apart by their independence from a singular foundational model and the absence of reliance on specific customer data. These iBots underwent training using test results from automation graphs, guided by a 'highest probability' rationale for outcome detection.
This innovative methodology forms the foundation of our training set, drawing efficacy from its exceptional explainability and logical model abstractions inherent in the Waylay Rules Engine.
01 | Unleashing the Potential of Generative Artificial Intelligence (GenAI)
In the ever-evolving landscape of technology, the integration of artificial intelligence (AI) and machine learning (ML) has significantly transformed various industries. Now, a new frontier emerges as Generative Artificial Intelligence (GenAI) steps into the spotlight, accompanied by Natural Language Processing (NLP) bots.
This dynamic duo is poised to redefine automation across diverse sectors.
02 | Moving from Low-code Automation to
AI-driven Automation
Within Waylay, developers can craft concise code snippets or leverage existing ones, linking them through logical operators to define intricate automation rules. By interconnecting these rules or embedding one within another, Waylay can facilitate the creation of highly complex automation scenarios.
Now, imagine interacting seamlessly with the automation engine using natural voice or text commands.
03 | Streamlining Human-Guided Solutions with Auditable Precision
Waylay’s rules engine is built on top of causal DAG, where a BN network is abstracted at the level to which humans tend to think about automation problems. This feature enables automation logic to be explainable to humans in a simple and intuitive way. This concept enables the execution of automation scenarios using human language, with built-in auditability for human comprehension during operation.
04 | Natural Automation Interface (NAI),
an interface capable of bidirectional communication.
An interface capable of bidirectional communication is the essence of what we refer to as a Natural Automation Interface (NAI), an interface capable of bidirectional communication. It can be programmed to execute specific automation scenarios using human language and, crucially, allows for the program's audibility by humans once it's in operation.
Where we go from here?
Get ready for an exciting journey into the future as we unveil a sneak peek into the innovative ventures and groundbreaking initiatives planned for the coming year. We're thrilled to share with you the new and cool developments that will redefine the way we operate and engage. From cutting-edge technologies to transformative strategies, join us on this forward-looking adventure as we shape a year filled with excitement, innovation, and remarkable achievements. Buckle up for a thrilling ride into the future of what's next! 👇
Videos
Automated Remedies for Connected Assets in Salesforce
Digital Twin acts on the unseen rules through innate causal inference modeling. In a demonstration featuring a connected electric bus, the system identifies issues like battery degradation through diagnostic trouble codes and CAN values, prompting proactive alarms. A Rulea Explainer is integrated within Salesforce, providing service agents with detailed explanations and remedies for proactive alarms generated by the system.
Building Natural Automation Interface (NAI) in Action!
In the initial phase of converting text to rules, Waylay systems utilizes advanced (NLP) capabilities to extract nuanced meaning from textual instructions. As we comprehend the context, we accurately translate human intent into a set of logical rules. In the reverse process, when moving from rule definition to human explanation, Waylay generates natural language explanations that explaines the logic embedded in the rules.
Integrating Waylay with Einstein Prompt Builder
At Waylay, we've created a small demo of Einstein for IoT preventive maintenance. We're using Flex Prompt Templates, template-triggered prompt flows and LLM model orchestration.
Blogs
Predictive Maintenance and Generative AI
In this blog post , we'll explore the impact that Generative AI can have on the productivity of service operations in industrial and manufacturing enterprises. And why Waylay is ideally suited to harness the power of large language models in combination with predictive and preventive maintenance asset monitoring rules.
You will also learn how we’ve extended the Waylay Digital Twin application for Salesforce Service Cloud with the Rule Explainer assistant.
Explainable GenAI for NOC service assurance
Complex topologies can generate an avalanche of events that require equally complex correlations, taking its toll on incident analysis and resolution times.
The novel approach that blends the explainability features of the Waylay platform with the GenAI eliminating the need for extensive clicking through different tools and screens, providing a comprehensive explanation in a streamlined manner.
The Transformative Impact of Intelligent Bots in Troubleshooting
In the ever-evolving landscape of technology, the integration AI and ML has significantly transformed various industries. One area where this transformation is particularly noticeable is in the realm of troubleshooting and technical support. Traditionally, case management tools have been the go-to solution for handling these issues, but a new wave of intelligent bots, armed with machine manuals and real-time machine data, it is poised to revolutionize the troubleshooting process.
New Case Management Tools powered by GenAI
In the ever-evolving landscape of technology, the integration of artificial intelligence (AI) and machine learning (ML) has significantly transformed various industries. One area where this transformation is particularly noticeable is in the realm of fraud prevention. Traditionally, case management tools have been the go-to solution for handling these issues, but a new wave of intelligent bots is poised to revolutionize the use case management process.
Almost no data and no time? Unlock the true potential of GPT!
In this blog post , we will explore how the advent of large pre-trained language models are giving rise to the new paradigm of 'prompt engineering' in the field of NLP. This new paradigm allows us to rapidly prototype complex NLP applications with little to no effort and based on very small amounts of data.