Hi everyone, this is Omar. I'm a solution architect. Welcome to the conference, and today's topic is Powering AI Enhanced Cloud Native Integrations. This talks about the performance statistics of AI enhanced cloud native integration platforms, and also we learn about the practical implementation strategies that improve efficiency. Security and reliability across your enterprise systems. I'll go to this PDF and we'll talk about each slide in the next few minutes. First topic would like to cover is performance advantages, model memory efficiency. Fast compilation and static typing. In con model, the goal lightweight, enables the highly efficient parallel processing with the minimal overhead. This model effortlessly handles thousands of simultaneous connections while maintaining the consistency of in the performance side. Then we'll go with the memory efficiency. This mainly focus on the garbage collection algorithms where it dramatically minimize, the memory footprint without disrupting the operations system. Resources remain optimally allocated even on extreme processing loads in case of fast compilation. It is almost like near in instant build cycle, significantly accelerate the development process. Engineering teams can rapidly iterate through complex integration challenges with the minimal weight timings. Static typing enables compact time error, checking sustainably reduce unexpected runtime values in production systems. Mission critical enterprise system maintains exceptionally uptime and reliability by having this, what is the real time impact for your enterprise applications? It reduces the 25% of the development deployment period where the microservices architecture slashed. It deployment cycles by a quarter percentage compared to, its own legacy systems just by, and also it helps to bring faster market of their application in case of reliability improvements. Our this goal and based approach helps. 30% fewer critical instance with a significantly reduced mean time to recovery. It also supports in multi-cloud and also reduces 23% of its time for any concurrency model, which is optimized to reduce resource utilization across different providers and infrastructure costs. Okay, now let's go to how this, a enhanced integration helps in different pages of your deployment or development periods even support as well. First topic we call about, like what is automated data mapping. A go forward AI framework have slashed manual integration efforts by 25%. These intelligent systems analyze complex data structures in real time and generates optimal mapping without human intervention. How in the background it helps is like it. I identify all the existing are across the world. Similar kind of patterns for each data node level. And brings down your entire efforts by 25% by providing the existing, by leveraging the existing, mapping structures across the landscape or your application. It's going to read the backend. Okay? Each node level identify, okay, this is close to this one, like that. So this gives you, reduce the time by 25%, which is a very significant change when it comes to mapping level. Okay, let's go to the intelligent error handling. Advanced day models now anticipates integration failures before they occur. These proactive systems automatically implement self-healing protocols and dynamic adapter. To changing operational conditions. Next topic, accuracy improvements. AI driven transformation engines have delivered a remarkable 30% improvement in data accuracy, where it has very various sophisticated patterns which recogni and also recognize algorithms identify and prevent. Potential quality issues before they impact operations. Let's go to the next slide. What are the performance or this platform benchmark comparison between different, integration applications, like C-P-I-S-A-P, MuleSoft? Apache Camel and Go based solutions here. Mainly if you see the dark color, which is the performance score and light color is the resources efficiency in all these aspects. Mainly we notice go based solutions always, dominates our, gives better results in both the metrics. It always like significantly form. There you have the traditional integration platforms. Our enterprise benchmarking also revealed that our goal-based solutions deliver up to 32% higher performance scores and 34% better resource efficiency compared to legacy systems. These impressive guidance translates directly into reduced infrastructure cost and improves scalability. Also enhance responsiveness by our mission critical integration force. Now, another key topic which I would like to cover today is security only four phases. One is identifying threat detection, analyzing the threats, responding to your threats and the learnings. Okay. Threat detection. Okay. In any security. First, you need to have your threats to be identified. Our robust security libraries implemented sophisticated patterns to recognize the, threats using algorithms. Ous access pattern are identified and flagged in real time microsecond precision. Once you identify the threats, then we analyze. Okay, we have advanced mission learning models. Evaluate that vectors using contextual intelligence. This sophisticated approach has reduced a false po alerts by 23%. Minimize the security response recession. Engineered counter measurements, deploy with subsecond latency. Compromise Systems automatically implement isolation protocol to prevent lateral network traverse. What other learnings? Every security incident enhances future edition capabilities by 27%. Through reinforcement learning algorithms, the system continuously evolves its threat intelligence database that will help. Next identifier, next threat. Detection pattern. Now we go to the edge. Computer integration. In this integration world, you have to have your data to be collected from various I sensors in a real time, field data. Once you have the data collection in the local itself. You identify, local, runtime excludes the optimized data genetics. Once it has it enriches based on embedded, machine learning models and understands its contextual understanding, then we'll go to the cloud synchronization after having a and enrichment. The filter data transmits security to them securely into the cloud infrastructure where the insightful distribution will come, which has actionable intelligence device generates decisions. Whether how it takes, this is all microservices based, communications analysis, processing, enrichment, syncing with cloud. Then. It goes to the distribution, our lightweight runtime and concurrent processing capabilities. Revolutionize edge computing integration with sophisticated AI capabilities. Implement this implementation has demonstrated reduced data processing latency by 18% while decreasing cloud bandwidth consumption and storage overhead by 15%. Delivering. Substantial operational cost savings and performance improvements, DevOps, tooling improvements you have mainly. Automated pipelines we have mainly with the go in the go, we have power with CICD pipelines, which reduces the deployment time by 40%, while validating the integration points with the 99.8% accuracy, which is, close to a hundred percent, which has a seamless deployment occur without human intervention eliminating, most of the manual errors. Intelligent testing, machine learning algorithms identify and prioritize critical test scenarios with, 87% greater efficiency. Also, these testings s strategically focus on high risk integration points, which reduces the overall testing time by 35%. Once the testing is there in the production system, you have to have a product protective monitoring. This advanced anomaly detection identifies potential issues 15 minutes before, before their impact. Our system initiates automated resolution protocols for 73% of instance before operational disturbance per, we have mainly four different type of, implementation patterns, based on APIs. API Gateway pattern where Go Functions has a high performance and resource. Efficien API. Gateways that intelligently routes request orchestrate services and implement robust authentication with minimal computational overhead. Here, this is the main thing. The next one is event driven architecture. Go competency model with the channels, create sophisticated. Event processing pipelines enabling seamless communication where distributed system response dynamically, that changes the state across your integration platforms. And the sidecar pattern is something like, it's like very close to legacy related things where, you keep, word services separate alongside your, Companion processes and also introduced advanced a capabilities enhanced functionalities without requiring modifications to your legacy data code database where it keeps, we keep running that one and parallelly, you go and develop, go services, waste integration approach. Next one is triangular feet pattern, in this. Go. Implementation incrementally replaces your legacy. So integration components through s interception points, facilitating non deceptive migration while maintaining the system in stability and integrity throughout the transformation process. Now it has, every pattern has its own advantages, based on the, our enterprise application integrations, current, Approach and the best of the best goal and, architecture will find out which one is going to useful the imple, while implementing this one. Okay. What are the success metrics, which saw 22% higher success rate in enterprise integration initiatives that be completed mostly on time and within the budget? 30% deployment efficiency. It accelerates continuous delivery pipelines with, fewer roadblocks, 25% less manual work intelligence automation eliminate repetitive integration topics, that reduces your, manual hour by 25%. At the same time, 30% improved accuracy, what we observed, okay, what happens? When you are doing this less manual work and automation that improves the, accuracy to reduce any errors by doing the APIs across your system boundaries, 18% reduce latency when it reduces, when it, all these measurements you have improved accuracy, deployment, efficiency, and all response automatically it reduces. Communication latency by 18% for all your, mission critical transactions. Processing our nation's implementing goal line based integration platform consistently achieve a measurable improvements across all key different indicators resulting in substantial return on investment and competitive advantage in their digital transformation. Initiatives, how we implement, just to give you the roadmap first, we assess that, where we conduct a thorough analysis of our existing, system and performance bottlenecks. Then we map specific business processes that would benefit go competency model. We may not go to every point needs to be replaced, but we can definitely suggest what are the benefits by doing this. Then we can implement pilot project where we develop a targeted, go microservice with the clearly defined boundaries. Select a high visibility integration pattern challenge, and to demonstrate measurable improvements, framework deployment, development, and deployments in this. Established standard, go design patterns and reduced components. We engineer robust connectors for mission critical enterprise systems and data sources. Then we can scale up the deployment strategically, expand go implementations across our integration landscape, implement comprehensive metrics to quantify performance gains and written on management. These four steps, gives confidence to customers and which is a very targeted, approach where we can eliminate a lot of, overhead on the customer by using, the set security measures and approach of the development, all the things. Thank you, for your time. I'm open to all the questions. Have a nice day.