Explainable auto-remedies - a fusion of causal modeling and GenAI

Billions have poured into GenAI, prompting major tech players to explore its full potential. 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 real time data, causal graphs and outcome insights into the training process. We combine historical records/manuals with real-time machine data to find the root cause and provide the right remedy - reducing the risk of human error, ensuring consistency that allows human workers to focus on more complex and strategic aspects of their jobs.

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Decoding the Future: A Decade of Automation Excellence, GenAI Breakthroughs,
and the Quest for Causality and Explainability

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

Waylay GenAI powered apps

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)

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. More about this you can find in this video.



Waylay GenAI for Field Service Operations Generates 120% ROI in the first Month in Production and 267% every month thereafter

In this blog post, we discuss the ROI for implementing Waylay DT and GenAI that shows a significant improvement over the traditional approach, with potential returns exceeding 120% in the first month and 266.7% in subsequent months. This highlights the efficiency and cost-effectiveness of the new system in handling CRM tickets.

Improving Customer Experience through Multi-Language Support and Efficient Repair Instructions

Waylay Digital Twin efficiently retrieves relevant solutions from different documents, improving accuracy even when specific manuals are lacking detailed instructions. Additionally, we integrated real-time language translation to provide precise repair instructions globally, ensuring technicians receive accurate guidance regardless of location. More about this you can find in this blog post.

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.

Solving the weak spot of current GenAI solutions

Billions of dollars have flooded into GenAI, attracting major tech players to look into its potential beyond mere trivia such as image/video processing and knowledge summaries. Yet, its integration into industrial settings encounters notable hurdles, particularly the "hallucination" issue. Utilizing GenAI for problem-solving demands a profound understanding of the specific issue at hand. More about this you can find in this blog post.

When Increasing GenAI Model Temperature Helps: Beneficial 'Hallucinations'

It might sound surprising, but sometimes a bit of 'hallucination' is just what we need. In industrial applications, leveraging these creative outputs can uncover solutions that aren't immediately obvious, enhancing the troubleshooting process and driving innovative problem-solving.

White paper on Waylay GenAI automation

We explore two distinct approaches to leveraging GenAI within the context of Waylay. The first allows us to design and explain the actions taken by the algorithm, providing a clear understanding of its behavior using GenAI. In the second approach the large language model (LLM) takes the reins to create a complete end-to-end automation scenario based on its reasoning, introducing a new level of autonomy and complexity. More about this you can find in this article.

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