Hi all. This is today I'm going talk about the topic revolutionizing industry with AI and the collaborative workflow intelligence framework. Today we'll be seeing how the Golan and the artificial intelligence are resetting the automation heavy industries. we'll examine various, like industries, like finance, manufacturing, and see how the collaborative workflow, is working. Basically, it is the bridge between the, human expertise and the, machine intelligence. Our framework offers a very structured approach, solutions, Characteristics, there are like a different, a good number of resource, oriented values. we can see from, different, domains such as manufacturing, logistics, and financial services. we'll, slowly, discovering the coming slides, how, these, collaborative, workflow framework enhances, the performance. And cost reduction. Let's go to the next slide. Welcome, the challenges of the modern automation. in this slide, there are like a four, points I have tried to mention here in this slide. One is the balancing automation with human oversight. another is the, real time processing demands. Integration complexity and, workforce, automation, with human oversight. there are organizations which trying to, of. they are trying to, maintain the proper industry standards and, safety standards. that is one of the major challenges the modern industry is facing. So our, framework, will, definitely, take this into account for the, for balancing the. then the, as for the real time processing, so modern industry is dealing with a huge data volumes. So it needs, immediate analysis and response, for the, massive data. it, Then there comes the, integration complexity. we're dealing about, complex, technologies. so we need various, like a flexible solutions to cope up with that. And then there comes the, workforce, adapt. in, in our current days, employees need clear frameworks to, collaborate effectively with the AI systems, rather than the, competing with them. There needs to be exact, balance between the AI and the humans. we can, get the desire. what is, collaborative workflow intelligence framework? Basically, it's a human ai, partnership and it'll take, the ethical oversights and, context, sensitive decisions, in this, in this bidirectional information flow model. And it, used to learn from the continuous, learning and adapt to the, for, adapt to the, just performance, feedback, to get it more accurate. the framework also, has a, like very high performance concurrent, processing architecture. Are like, the collaborative workflow intelligence framework, establishes clear boundaries between the AI and the human decision authorities, creating a symbiotic relationships, that, leverages the strength of the both. It is very high, concurrency model, performance and it be used in the real processing for large, data. This is not just a theoretical concept. it can be, used, practically in different, industries like finance, manufacturing, and it can be used in complex scenarios and where there are like, lot of technical complexity. this framework, definitely benefits from that. So there different, advantages. we can select, like from the other framework, we, we can say of the exceptional performance of, framework. And, there are like a different, garbage collection efficiency, modules, which can enable the microsecond response times, essential for industry automations. it helps seen improving the performance and we have given some benchmark of around, 40 to 60% improvements we are seeing. Language, implementations. Then there comes the, built-in concurrency. it gives, multi, multi-threading, capabilities, perfect for, managing a large amount of data streams. Currently, our industry is dealing with a large amount of data and complex technical. Scenario. And it helps in, just, building, with con on top of any technical complexity. And this is by far, justing the, traditional, threading models. And then there comes the strong type safety. So colon static typing prevents common runtime, errors, crucial for mission sensitive, industry applications where system failures, can have significant consequences in terms of revenue and production continuity. here the, our framework, helps in, So we are talking about the technical strengths here, but also apart from the technical strengths, goal and simplicity and readability also make it, for the, cross-functional teams where the engineers and the data scientists, can collaborate closely. it is also using the standard, library, which dependencies and. To the next slide. We have went through a number of use cases. one is the, manufacturing, sector implementation. so in this, we have went through a few, module like, sensor integration, predictive analytics, intelligent and human direction resolution. in sensor integration, Golan microservices, collect and normalize data from, heterogeneous sensor networks across, factory floors and predictive analysis. it, analysis the pattern, to, predictive the maintenance needs. And then comes the intelligent, so intelligent alerting, the notifications issues. Then, we also, go through the human director resolution. So it makes in the final decision making on the complex and maintenance, and quality issues. so ai, requirements, those complex financial decisions. And it, we have owned to a, like a, successful, like a metrics for these 17% reduction in the, like a production delays, around 23% in unplanned downtime. The, systems ability to integrate, with the existing systems. We are seeing investment in. We are also going to the, logistics, optimization case study where we're doing the route planning, load balancing, exception handling, and performance analysis. So in route planning, we s the optimal, delivery sequences, accounting for. And, load balancing it, helps in allocating in the dynamically allocating the resources. therefore, making the, cost optimization and, exception handling, human dispatchers, review the AI recommendations in critical unusual situations, which. Performance analysis. So here, we going through improvement information and we can get the recognition. And for the performance analysis and, we're getting here, almost 80, 30, 12%, cost reduction in the regional logistic, provider for this kind of, architecture, this kind of framework implementation and on time delivery rates also improved by 14%. And, real time adjustments also. Now coming to the, financial, services application. So we have discovered like a few modules in this, under this financial services application. One is the, document processing. in this state, AI extracts and, the data from the loan applications and the supporting documents. then the risk assessment goal length services from complex risk calculations with, subsecond, millisecond latency and continuous learning system goes through the, feedback on decisions and outcomes. And this will help in making the, optimized solutions. Underwriter focus on complex cases flagged by the system. A mid-size regional bank, implemented this framework for their loan processing operations, which in fact helped in 25% implemented the loan approval efficiency while maintaining their risk profile. So it's evaluating all the complex case scenario applications. And it has particularly good features, environment and making it more, performance has more performance. And, we have gained significant business impact for this framework. And, from the manufacturing production efficiency improvement standpoint, there were, percent, reduced delays. And, 25%, processing efficiency. Also, we can get, when we're just going through the use case of the financial services applications throughput increase, and, for the logistic delivery optimization savings, we are getting eight to 12%, cost reductions. And, 32% for the, like employee satisfaction, when we're just worker engagements across implementations. there. A good number of metrics we can solve for this, f framework, which can use to drivers range of industries. the combination of the LANs, performance characteristics and along with the ai, human collaboration definitely, makes a competitive advantage over the traditional, models. We. Then organizations also report, significant, improvements in the ability to extract and retain the technical talent as the framework, the engaging roles that emphasize the human judgment and creativity rather than the routine processing. So implementation methodology, we can, four, the, the map, the current processes and the decision boundaries. This comes the advisory pilot where we deploy AI in the recommendation only mode. That is the supervised automation. gradually increase automation with the oversight. Fourth is the continuous optimization refined based on the performance data and the feedback. Our implementation approach, emphasizes gradual adoption, starting with systems that provide recommendations but lead decisions to humans. goal's, modularity support this incremental approach through, clear service boundaries. And, change management is also integrated through the process, with education programs that helps, workers understand how the AI will be complement their roles rather than just them. Technical architectural overview. so this C architecture, there are three layers. One is the data collection microservices, and AI processing core and the, human interface components. goal powers the critical performance path, particularly in the data collection and transformation layers where the concurrency demands at the highest. So we have going through various benchmark competition here. So we have just on the previous, systems, and then we have going through the processing times and the memory usage of the previous systems, and also in the Python implementations, Java implementations and goal implementations. These. real time processing much more faster than almost like three to 11 times, processing faster, than the, traditional models. And also it helps, in taking the, less memory, compared to the traditional models. it, allocation of the resources in all the environments. So what will be our next steps in the future research? we definitely need to focus on the, adaptive, learning models. then, we have to define on the systems, where the, they're making the continuous, decision boundaries based on the human feedback. The outcomes, reducing the need for manual retraining. Then, we're using these models in the, already in the cross-functional, industry, be healthcare, energy finance. So all are now tied up into the safety requirements and the unique regulatory, then the workforce evaluation. researching long-term impacts on skill development and the job roles to guide the educational and training programs for the a augmented workspace. It helps, in benefits, lot of benefits in the workforce, evolution. And, then there is the goal line ecosystem expansion. So developing, specialized libraries and frameworks, optimizes further the goal line for ai, human collaborative systems in industrial context. we're dealing with various organizations and ask them to, do their CWIF journey, with a workflow assessment workshop. we can, gain the maximal, amount of the benefits our team provides. Implementation roadmap, tailor to your, industry context and technical maturity, whether it a complex problem, definitely our model can, Yeah, that's all my, like in and.