Search Results

Introduction: I n today's highly competitive business landscape, Quick Service Restaurants (QSRs) are constantly striving to attract and retain customers. One of the most effective strategies employed when you're learning how to start a quick service restaurant is the implementation of promotions and offers. However, in order to maximize the impact of these initiatives, QSRs need to embrace innovation and advance. At Synergylabs, we specialize in providing cutting-edge solutions that help QSRs achieve optimum utilization of their resources. In this blog, we will explore various aspects of promotions and offers in the QSR industry. Driving Growth in QSR: The Power of Innovative Promotions QSR Discounts and Deals: Discounts and deals are a powerful tool for attracting new customers and increasing sales. By offering limited-time discounts on popular items or bundling multiple products at a reduced price, QSRs can entice customers to try their offerings. Our AI-powered recommendation engine can analyze customer data and preferences to suggest the most effective discounts and deals that resonate with the target audience, ensuring maximum impact. Limited-Time Offers for QSR: Creating a sense of urgency is crucial for driving customer engagement. Limited-time offers leverage the fear of missing out, prompting customers to act quickly. Our algorithms can analyze customer behavior and predict the ideal duration for a limited-time offer, optimizing its impact and driving increased footfall and sales. QSR Coupons and Savings: Coupons have long been a popular promotional tool in the retail industry, and QSRs can harness their power too. With our coupon management system, QSRs can create personalized coupons tailored to individual customer preferences. This not only increases customer satisfaction but also enables QSRs to gather valuable data on customer preferences for future marketing efforts. Exclusive Promotions at QSR: Creating a sense of exclusivity can significantly enhance customer loyalty. By offering exclusive promotions to a select group of customers, such as loyalty program members or social media followers, QSRs can foster a stronger connection with their target audience. Our AI-driven customer segmentation and targeting tools enable QSRs to identify and engage with their most valuable customers, ensuring the success of exclusive promotions. QSR Combo Meal Specials: Combo meal specials provide customers with a convenient and cost-effective way to enjoy a complete meal. Our AI algorithms can analyze customer preferences, purchase history, and real-time inventory data to suggest the most popular and profitable combinations. This not only enhances customer satisfaction but also optimizes resource utilization for the QSR. Seasonal Offers for QSR: Seasonal offers capitalize on specific occasions, holidays, or weather conditions to create excitement and drive sales. By leveraging historical sales data and external factors, our models can predict the ideal timing, content, and pricing of seasonal offers. This ensures that QSRs are well-prepared to meet customer demands and capitalize on seasonal trends. Loyalty Program Rewards at QSR: Loyalty programs are an excellent way to incentivize repeat business and foster customer loyalty. Our loyalty management system enables QSRs to create personalized rewards based on individual customer preferences, purchase history, and engagement levels. This not only encourages customer retention but also provides valuable insights for targeted marketing campaigns. QSR Meal Bundles and Packages: Meal bundles and packages offer customers a convenient and cost-effective way to enjoy a variety of items. Our algorithms can analyze customer preferences and optimize the composition and pricing of meal bundles to maximize customer satisfaction and revenue generation. This promotes upselling and cross-selling opportunities, driving increased average order value for QSRs. Weekly Specials at QSR: Weekly specials add an element of surprise and anticipation for customers. By offering unique and enticing deals on specific days of the week, QSRs can drive customer engagement and increase footfall during traditionally slower periods. Our demand forecasting models can analyze historical sales data and customer behavior to identify the most effective weekly specials for each QSR location. Flash Sales and Discounts for QSR: Flash sales and time-limited discounts create a sense of urgency, encouraging customers to make immediate purchasing decisions. Our algorithms can identify the optimal timing, duration, and discount levels for flash sales based on real-time data and customer behavior patterns. This ensures maximum impact and drives rapid sales growth for QSRs. Conclusion: Incorporating innovative solutions into the QSR industry can revolutionize the way promotions and offers are implemented. Synergylabs is at the forefront of this revolution, that enable QSRs to achieve optimum utilization of their resources and drive maximum customer engagement. By harnessing the power of AI-driven recommendation engines, personalized promotions, and real-time data analysis, QSRs can enhance customer satisfaction, drive sales growth, and gain a competitive edge in the market. To learn more about our transformative solutions and how they can benefit your QSR business, visit our website and inquire about a product demo today. Together, let's unlock the full potential of promotions and offers in the QSR industry.

When deals slip through cracks you didn't even know existed? Sales teams everywhere are wrestling with invisible performance gaps that drain revenue faster than a leaky bucket. Companies that embrace structured audit approaches don't just see modest improvements; they witness dramatic transformations. The data supports this: systematic evaluation drives measurable increases in both team productivity and revenue generation. Take ShelfWatch, for instance.  With real-time data, they slashed store audit time by 50% while delivering more accurate and consistent insights into store performance. That's the kind of transformation your sales organization can achieve. Ready to turn guesswork into strategy? Let's explore how to build a framework that actually works. Essential Components of an Effective Sales Audit Checklist Building your sales audit checklist isn't about creating another bureaucratic document. Think of it as your strategic roadmap: one that guides systematic evaluation and ensures you're capturing insights that actually matter, not just collecting data for the sake of it. When you construct a comprehensive sales audit checklist  properly, you're essentially breaking down your complex sales operations into organized, bite-sized pieces. This approach enables you to identify growth opportunities that might otherwise remain hidden in the noise of daily operations. Core Sales Performance Metrics to Track Let's start with the foundation: revenue per rep, conversion rates by stage, and deal velocity. These aren't just numbers on a spreadsheet—they're your early warning system for performance issues. But don't stop there. Average deal size, win rates, and time-to-close tell you a deeper story. They reveal which team members might need coaching and which strategies consistently deliver results. It's like having a GPS for your sales performance. Customer Journey Mapping Elements Ever wonder where your prospects vanish into thin air? Journey mapping tracks every touchpoint from initial contact to deal closure. This visibility shows you exactly where people drop off, or where they accelerate through your process. Understanding buyer behavior patterns across different segments becomes your competitive advantage. Instead of wondering where to focus your efforts, you'll know precisely which areas need attention for maximum impact. Sales Team Skill Assessment Criteria Here's something most audit guides skip: evaluating the human element. Communication skills, product knowledge, and objection-handling capabilities paint a complete picture of team strengths and gaps. This assessment becomes the foundation for coaching programs that actually work. Don't overlook technical proficiency either. Reps who struggle with CRM systems or sales tools often miss opportunities or deliver inconsistent customer experiences. It's a silent revenue killer. With these components in place, you're ready to move from theory to action through systematic implementation. Step-by-Step Guide to Implement Sales Audit Process Implementation isn't rocket science, but it does require method over madness. Jumping into analysis without proper preparation is like trying to navigate without a map. Your sales audit process needs structure. Pre-Audit Preparation and Data Collection Start by gathering your historical performance data from CRM systems, email platforms, and call recordings. Clean data equals accurate analysis: garbage in, garbage out, as they say. Define your audit scope clearly from day one. Are you focusing on individual reps? Specific territories? The entire sales funnel? Clear boundaries prevent that dreaded scope creep and keep everyone focused on what matters. Sales Pipeline Analysis Framework Now for the detective work: examine how deals flow through each stage. Where do prospects get stuck? Where do they speed up? Pipeline health directly predicts your future revenue, so this analysis is crucial. Calculate conversion rates between stages and benchmark them against industry standards. When conversion rates are low, you've found your improvement opportunities. It's that simple. Lead Generation and Conversion Tracking Not all leads are created equal—your audit should reflect this reality. Evaluate  lead quality , source effectiveness, and nurturing processes with a critical eye. Track time-to-conversion across different lead types and sources. This analysis helps you allocate resources to channels that actually produce results rather than just activity. Ready to take your audit game to the next level? These advanced strategies separate the amateurs from the professionals. Advanced Sales Audit Tips for Maximum Impact Today's sales environment demands sophistication beyond basic performance reviews. These sales audit tips transform good audits into game-changing evaluations that drive real business outcomes. Consider this transformation: VoiceDrop went from failure to success with systematic optimization, achieving a 57% trial conversion rate, which is a 375% improvement. This dramatic turnaround demonstrates what's possible when you approach auditing strategically. AI-Powered Sales Analytics Integration Why analyze 3-5% of sales calls when AI can review 100%? This comprehensive coverage reveals patterns that manual review processes simply can't catch. Machine learning algorithms identify successful conversation patterns and predict deal outcomes. Your team gets actionable insights without spending countless hours reviewing recordings. It's efficiency and effectiveness rolled into one. Cross-Departmental Collaboration Strategies Here's a perspective shift: include marketing, customer success, and product teams in your audit process. Sales doesn't exist in isolation, and external viewpoints reveal blind spots you never knew existed. Regular cross-functional meetings ensure audit findings translate into coordinated action plans. When everyone understands the insights, implementation becomes exponentially more effective. These sophisticated techniques deliver measurable returns—let's examine the specific benefits you can expect. Benefits of Sales Audit for Revenue Optimization Strategic auditing isn't just busy work—it delivers tangible returns that justify every minute invested. The benefits of sales audit extend far beyond simple performance tracking into real business transformation. Identifying Hidden Revenue Leaks Systematic analysis exposes where deals die in your pipeline and why prospects choose competitors. These insights prevent future losses through targeted improvements that actually stick. Pricing inconsistencies, discount patterns, and negotiation weaknesses become visible through comprehensive auditing. Fix these issues, and you directly impact your bottom line. It's that straightforward. Improving Sales Team Productivity Metrics Data-driven coaching replaces guesswork with targeted skill development. Reps improve faster when they understand exactly what needs attention rather than receiving generic feedback. Time allocation analysis reveals where team members waste energy on low-value activities. Redirecting focus to high-impact tasks increases productivity and job satisfaction simultaneously. While these benefits apply universally, maximizing effectiveness requires industry-specific approaches. Industry-Specific Sales Audit Considerations Different business models demand different audit approaches. What works brilliantly for B2B software sales might fail spectacularly in retail or professional services. B2B vs B2C Audit Methodology Differences B2B sales involve longer cycles, multiple decision makers, and complex negotiations. Your audit must account for these extended timelines and relationship factors—it's not just about quick conversions. B2C audits focus more on volume metrics, conversion speed, and customer satisfaction scores. The shorter sales cycles require different measurement approaches and success indicators entirely. SaaS Sales Audit Best Practices SaaS companies need special attention to trial-to-paid conversions, churn rates, and expansion revenue. These metrics don't exist in traditional product sales, making standard audit approaches inadequate. Subscription models require ongoing relationship auditing beyond the initial sale. Customer success and renewal processes become critical audit components that can't be ignored. The right technology transforms these industry-specific strategies from theory into practice. Technology Tools for Modern Sales Auditing Technology transforms auditing from tedious manual labor into an automated insight engine. Your tool selection directly impacts audit quality and efficiency; choose wisely. CRM Integration and Data Synchronization Your audit tools must connect seamlessly with existing CRM systems. Data silos create dangerous blind spots and significantly reduce audit effectiveness. Real-time synchronization ensures audit insights reflect current performance rather than outdated information. Fresh data enables immediate course corrections when you need them most. Establishing clear success metrics ensures you can prove your audit's value and guide future improvements. Measuring Sales Audit Success and ROI Without measurement, you can't prove audit value or secure continued organizational support. Tracking the right metrics ensures your systematic evaluation efforts get the recognition—and resources- they deserve. Revenue growth, conversion rate improvements, and deal velocity increases provide concrete evidence of audit impact. These metrics justify continued investment in systematic evaluation approaches. Team performance consistency and coaching effectiveness matter. Successful audits reduce performance variation across reps and territories, creating more predictable results. Learning from common implementation mistakes prevents frustration and ensures success. Creating Sustainable Sales Audit Practices One-time audits provide temporary insights but don't create lasting organizational change. Building sustainable practices ensures continuous improvement and long-term success that compounds over time. Regular audit cycles prevent performance drift and maintain accountability across your organization. Quarterly reviews provide sufficient time for meaningful changes while maintaining momentum. Scheduled audits become part of your sales culture rather than disruptive events. Teams expect and prepare for regular evaluation cycles, making the process smoother and more effective. Before you implement sales audit practices, addressing common questions helps anticipate challenges and refine your approach.Transform Your Sales Performance Through Strategic Auditing Systematic auditing transforms sales organizations from reactive firefighting into proactive performance optimization machines. The right checklist implementation reveals hidden opportunities while preventing those silent revenue leaks that slowly drain profitability. Teams embracing structured evaluation consistently outperform those relying on intuition and guesswork. Start with basic metrics and gradually expand your audit scope as processes mature and insights accumulate. Your competitors are already using these methods. The question isn't whether to start auditing systematically; it's how quickly you can implement practices that drive measurable results. The revenue you save might just be your own. Your Most Pressing Sales Audit Questions Answered How often should companies conduct comprehensive audits using structured checklists? Quarterly comprehensive audits work best for most organizations, supplemented by monthly mini-audits focusing on specific areas like pipeline health or individual rep performance. What are the most critical elements small businesses should include in their checklist? Focus on conversion rates by stage, average deal size, sales cycle length, lead source effectiveness, and individual rep performance consistency metrics—keep it simple but comprehensive. How can remote sales teams effectively implement systematic audit processes? Leverage cloud-based CRM systems, call recording platforms, and video conferencing tools to maintain visibility and accountability across distributed team members.

Introduction Navyug Cycles, a contract manufacturer for Decathlon, required a cutting-edge track and trace system to gain visibility into their production processes. The goal was to capture crucial data on cycle times , defect rates, reasons for defects, slow production causes, and enable advanced analytics to attribute defects to specific stations and workers. This implementation aimed to significantly improve production efficiency, reduce defects, and promote data-driven decision-making. Key Challenges Integration with Existing Infrastructure: Design and deploy a state-of-the-art data gathering and tracking system compatible with existing manufacturing setups. Unit Traceability: Ensure the ability to trace each unit across the entire manufacturing line. Station and Worker Attribution: Accurately associate and measure the performance of each unit at various process stations. Defect Management: Implement guided quality checks to attribute defects or rework to specific stations or workers. Sustainability Goals: Measure energy consumption and calculate the carbon footprint for every production cycle. Solution SynergyLabs proposed and implemented a holistic solution combining hardware and software to meet Navyug Cycles’ requirements. Below are the details: Laser QR Code Marking: Production begins by marking each metal BB shell with a durable laser QR code. This ensures resilience in the harsh factory environment, enduring welding gases and polishing agents. Custom Station Additions: Each station is equipped with a scanner that reads the QR code on the unit. The process at the station can only commence after a successful scan, enabling precise cycle time measurement. Integrated Welding Machines: Panasonic tick welding machines were integrated into the system to record gas consumption, cycle start, and end times. Worker Identification: Fingerprint scanners were deployed at stations to record the worker’s identity, ensuring accountability for production steps. Enhanced Quality Checks: Quality check stations were equipped with rugged touch-screen tablets. These devices featured pre-configured quality checklists overlaid on images of the cycle frame. Inspectors could tap on joins, mark them as “OK,” “Rework,” or “Rejected,” and route units to the appropriate bins or rework stations. Our Role SynergyLabs played a pivotal role in the successful implementation of the system: Initial Design: Conducted factory visits and collaborated with Navyug Cycles’ senior management to design the track and trace system. System Development: Developed and implemented both software and hardware components, partnering with Variety Innovation for hardware solutions. Deployment and Training: Executed deployment and proof of concept (POC), followed by handover. Comprehensive training was provided to shop-floor staff to ensure effective usage of the system. Outcomes Comprehensive Process Visibility: Captured complete details of the production process, including precise cycle times and unit traceability from start to finish. Enhanced Efficiency: Improved station-level efficiency and overall production performance through data-driven insights. Defect Reduction: Significant decrease in defect rates due to clear attribution of defects to specific stations and workers. Analytical Insights: Enabled proactive maintenance by identifying stations with consistently high defect or rework rates. For example, management could now schedule maintenance of welding equipment based on analytical findings. Improved Production Planning: Accurate forecasting and production planning were made possible through real-time data and analytics. Real-Time Monitoring: Provided stakeholders with real-time visibility into production processes, fostering collaboration and quick decision-making. Sustainability Metrics: Enabled the measurement of energy consumption and carbon footprint, aligning production processes with sustainability goals. Conclusion : Track and Trace System The track and trace system designed by SynergyLabs has revolutionized Navyug Cycles’ production processes. By integrating advanced hardware and software solutions, the company now benefits from improved efficiency, reduced defect rates, and actionable insights. This successful implementation underscores SynergyLabs’ commitment to delivering cutting-edge solutions that empower businesses to achieve operational excellence and sustainable growth.

As video becomes an increasingly important form of communication, intelligent video analytics (IVA) is gaining prominence as a tool for understanding and processing video data. IVA is a set of technologies that use computer vision and machine learning algorithms to automatically analyze video content, extract insights, and provide actionable information. However, even with the most advanced IVA technology, there is still a crucial role in human interpretation. In this blog, we'll explore the importance of human interpretation in IVA and discuss some of the ways in which it can enhance the accuracy and effectiveness of these technologies. Understanding the Limitations of Intelligent Video Analytics First, it's important to recognize the limitations of IVA. Despite its sophisticated algorithms and powerful computing resources, IVA technology is still subject to a number of factors that can limit its effectiveness. These include: Ambiguity in video content Even the most advanced IVA systems can struggle with certain types of video content that are ambiguous or difficult to interpret. For example, video footage shot in low light conditions or with a lot of motion can be challenging for IVA systems to analyze. Biases in algorithms As with any machine learning technology, IVA systems can be subject to biases in their algorithms. This can result in an inaccurate or incomplete analysis of video content. Lack of context IVA systems may struggle to interpret video content without sufficient context. For example, they may not be able to distinguish between a person walking their dog and a person stealing a dog without additional information. The Role of Human Interpretation in IVA This is where human interpretation comes in. By providing additional context, identifying potential biases, and making judgment calls on ambiguous content, human experts can help to enhance the accuracy and effectiveness of IVA technology. Here are some of the ways in which human interpretation can improve IVA: Data labeling: One of the most important roles for human experts in IVA is to label data. This involves identifying key features in video footage that the IVA system can use to make predictions. By labeling data accurately, human experts can help to improve the performance of the IVA system over time. Contextual understanding Human experts can provide additional context for IVA systems, helping them to interpret video content more accurately. For example, a human expert might be able to recognize that a person entering a restricted area is actually an authorized employee, and can provide that context to the IVA system. Bias detection Human experts can also help to identify potential biases in IVA algorithms. By examining the data and output of the IVA system, they can determine whether there are any patterns that might suggest bias. This can help to improve the accuracy and fairness of the system. Judgment calls Finally, human experts can make judgment calls on ambiguous content. For example, if an IVA system flags a particular behavior as suspicious, a human expert can review the footage and determine whether there is actually cause for concern. This helps to reduce false positives and ensure that the IVA system is only raising alarms when necessary. SynergyLabs Technology is a leading provider of advanced IVA solutions. Our technology uses computer vision and machine learning algorithms to analyze video content and provide real-time insights to customers. However, SynergyLabs Technology recognizes the importance of human interpretation in IVA and has developed a unique approach that combines the power of automation with the insights of human experts.

Are you considering how to use AI in telecommunications for your business? In this article, we explore the potential of AI and its implications for digital transformation. Discover how using AI can help your business reach new heights and stay ahead of the competition in an ever-evolving world of technology. What is AI ,and what does it do? AI is a process of programming computers to make decisions for themselves. This can be done through a number of methods, including but not limited to rule-based systems, decision trees, genetic algorithms, artificial neural networks, and fuzzy logic systems. AI has a number of potential applications in the telecommunications industry. These include: Automated customer service: AI can be used to provide automated customer service, such as through chatbots. This can help to improve customer satisfaction by providing 24/7 support and reducing wait times. Network analysis and optimization: AI can be used to analyze data from telecom networks in order to identify trends and optimize performance. This can help to reduce network congestion and improve overall efficiency. Fraud detection: AI can be used to detect fraud in telecom networks. This can help to protect customers and reduce financial losses for service providers. Targeted marketing: AI can be used to collect data about telecom customers and then target them with personalized marketing messages. This can help to increase sales and revenue for service providers. What is the impact of AI in the Telecommunications industry? In the past decade, AI has made significant inroads into the telecommunications industry. The impact of AI in telecommunications is evident as, with the rapid expansion of 5G networks and the increasing demand for data services, telcos are under pressure to find new ways to improve their efficiency and customer experience. AI can help telcos to meet these challenges in a number of ways. For example, AI-powered chatbots can provide customer support 24/7, reducing the need for human agents. AI can also be used to automate tasks such as network maintenance and repairs, freeing up staff to focus on more value-added activities. In addition, AI can be used to improve marketing and sales efforts by providing insights into customer behavior and preferences. And with the advent of 5G, telcos will be able to offer new services such as augmented reality and virtual reality, which will require advanced AI capabilities. Thus, it is clear that AI will play a key role in the future of telecommunications. By helping telcos to improve their efficiency and customer experience, AI will enable them to stay ahead of the competition and maintain their position as leaders in the digital transformation of society. The future of digital transformation in telecommunications Digital transformation is inevitable in the telecommunications industry. The future of digital transformation in telecommunications lies in the adoption of artificial intelligence (AI). AI has the potential to revolutionize the telecommunications industry by automating tasks, improving customer experience, and increasing operational efficiency. In the past, digital transformation initiatives in the telecommunications industry have been hampered by a lack of data and siloed systems. However, AI changes this by providing a way to collect and analyze data at scale. With AI, telecom companies can finally realize the full potential of digital transformation. AI-powered automation can help telecom companies improve customer experience by reducing wait times and increasing accuracy. Additionally, AI can be used to improve operational efficiency by automating tasks such as fraud detection and network optimization. The future of digital transformation in telecommunications is bright. With AI, telecom companies can overcome the challenges that have held back previous digital transformation efforts. By automating tasks, improving customer experience, and increasing operational efficiency, AI will enable telecom companies to leap ahead of their competitors.

Is AI anything new? No, it is not. It has long been there. But, today the scopes of AI have changed over the years. Thanks to the abundance of data and advances in algorithm coding. However, unlike the private sector, AI adoption is still nascent in the public sector, although AI applications can be leveraged for a wide number of use cases. AI alone can transform the way the public sector and educational institutions execute their operations. When accomplished strategically, AI can help these constituents unlock valuable potentials of the data in the following way: Reduction in repetitive tasks No wastage of talent and motivation Improved labor efficiencies Higher concentration in tasks that need creativity and oversight Since the government workforce is a little older as compared to the private sector, AI can help a better transition to abolish the traditional view of government work like that of boring meetings and form fill ups. With AI becoming more ubiquitous, you can find many applications of Artificial Intelligence to be relevant inside and outside of the government organizations. These public sector use cases are readily driving performance improvements. Let’s check with them. AI Applications in Public sector We would highlight some of the specific use cases, although quite common in nature, they are capable of offering immediate benefits. 1. Agent-based simulation for decision-making Owing to agent-based simulation, it refers to the computational science that concentrates on independent active components of a system that interact with each other. In agent-based simulation modeling, active components are known as agents. The role of ABM is to detect interactive agents and their behavior. And these agents could be anything from people to vehicles, equipment to products and companies to entities. How does the model work? ABM establishes a relationship between agents depending on environmental variables, and it runs the simulation. It then measures interactions of independent behavior and brings out the dynamics from the model. SynergyLabs uses ABM with different key variables in one AI-based platform to extract efficient results. Hence, ABM is a novel and innovative way to leverage it for your organization. And Public policy management is a field that can benefit from agent-based simulation . Nonlinearity is the base of complex systems like policy management, where the dynamics and nonlinear behaviors are tough to capture due to the presence of heterogeneous agents. As a result, decision-making is tough. To date, public policy assessors failed to define efficient public policy management process despite using several methods. However, simulations allow for dynamics and nonlinearity captures to solve the problem, and make decisions efficient and useful for management organizations. Benefits it can offer are; Exhaustive interpretation of complex theories Insightful observation from interactions between different entities and strategic methodologies Examining new ideas Anticipating the impact of policy Developing a new theory Determining the need for an improved approach for decision making 2. Behavioral Analytics Data analytics is critical to behavioral analytics that provides insights into people’s behavior or actions involved with online purchasing, social media activity, and gaming. The idea behind data analytics is to identify possibilities to optimize scopes and foster certain business outcomes. Behavioral analytics deals with demographic and geographic data, and sometimes more than this to uncover additional data. The primary objective of this phenomenon is driving conversions by creating and iterating patterns and dynamics from customer data. The same application can be employed across different governmental bodies to unlock key potential, improve efficiency and transparency in public management. How it helps: The government can make use of a large volume of data and acquire more insights using data analytics Predicting Behavior Making predictions based on the available data is useful to determine a careful balancing act much ahead of an important public meet or convention. For example, a music concert is likely to take place in a place. So, based on the public space data, you can detect the number of visitors, gauge pressure in a specific area, identify busy or less busy locations and decide about the probabilities of congestions in an area. Thanks to data analytics, it helps deploy necessary police security in an area where it is highly needed. Other than this, predicting crime and fraud, improving emergency response, increasing cost-savings are some essential applications of AI. 3. Converting Paperwork into Digital Asset We know most of the government functionaries are too much engrossed with tedious and iterative paper work or content management. But, the problem with these papers is their non availability when needed. There comes the need of converting paperwork or any content type into digital assets. The content can comprise anything from images, documents, audio/video clips, and creative files. These are valuable resources of any organizations and digital asset management can help you centralize, organize, and make use of these files anytime anywhere you want through a centralized server. The operation runs on cloud computing, an integral part of AI and the conversion from paperwork to a digital asset is done via Intelligent Character Recognition and Optical Character Recognition. In addition, an advanced AI metadata tagging helps find files faster while giving the designated person access to important files to improve efficiency. 4. Crime Reduction and Prevention The government must make efforts to prevent crime to ensure safety for its people. AI here offers a great contribution to crime reduction and prevention. For crime detection, AI can offer real-time information about a crime. It uses machine learning to predict when and where a crime is most likely to occur. The system uses historical data to predict future crime. On the other hand, AI offers better insights into the scopes of preventing crimes by identifying patterns accurately. It rightly complies with the principle to prevent crime in the first place to avoid major threats to life and property and ensure security. Conclusion SynergyLabs’ case study can highlight how AI can be of great use to improve efficiency and increase productivity in organizations. The similar way, government organizations can rethink the scopes and benefits of AI so as to bring a massive transformation that reinstates trust and credibility to government endeavors. SynergyLabs offers AI-based consultancy to organizations of every type. If you have any query, we would love to connect.

The manufacturing industry is evolving faster than ever before. As the use of Artificial Intelligence (AI) and Robotics becomes increasingly prevalent, companies must adopt these technologies or risk not being able to stay competitive in the future. In this article, we'll discuss what you need to consider as manufacturers when it comes to integrating AI into your production process and what the potential implications are for companies that don't keep up with the times. What is Artificial Intelligence? Artificial intelligence (AI) is a process of programming computers to make decisions for themselves. This is done by feeding the computer data and then letting the computer learn from that data. The more data that is fed into the system, the more accurate the computer’s predictions will become. One of the most important aspects of AI is its ability to evolve over time. As new data is fed into the system, the computer will be able to adapt its predictions and decision-making processes accordingly. This means that AI systems have the potential to get better over time – something that is not possible with traditional manufacturing methods. AI is already being used in manufacturing to streamline production processes and reduce costs. In the future, AI systems will become even more sophisticated and will be used to create customized products for individual customers. This will revolutionize manufacturing as we know it, making it faster, more efficient, and more responsive to customer needs. Why AI in Manufacturing? The industrial revolution brought forth many changes in manufacturing. With the introduction of new technologies, manufacturers are now able to produce more goods at a lower cost and with less waste. However, as the world continues to change and technology advances, manufacturers must adapt or die. One of the biggest changes in manufacturing is the introduction of artificial intelligence (AI). AI has the potential to revolutionize manufacturing by providing companies with the ability to automate tasks, improve decision-making, and optimize operations. There are many reasons why AI is becoming increasingly important in manufacturing. First, AI can help companies automate tasks. This can free up employees to focus on other tasks or areas of the business. Second, AI can help companies make better decisions. By analyzing data and trends, AI can provide insights that humans may not be able to see. Finally, AI can help companies optimize their operations. By understanding how machines are operating and where bottlenecks are occurring, AI can help companies make improvements to their processes. The bottom line is that AI is changing manufacturing for the better and those companies that embrace it will be the ones who succeed in the future. The Future of Manufacturing with AI In the past few years, there has been a lot of talk about the future of manufacturing and how artificial intelligence (AI) will change the landscape. Some believe that AI will lead to the demise of manufacturing as we know it, while others believe that AI will simply help manufacturers become more efficient and productive. No one knows for sure what the future of manufacturing with AI will look like, but one thing is certain: companies that don't adapt to the new technology will likely die off. Those who embrace AI and use it to their advantage will be the ones who thrive in the future. So, what does this all mean for your company? If you're still using traditional methods of manufacturing, it's time to start considering how AI can be used to improve your processes. Otherwise, you risk being left behind by your competition. There are many different ways that AI can be used in manufacturing, so it's important to do your research and figure out which applications would work best for your business. But don't wait too long to make a decision — the longer you wait, the harder it will be to catch up. This is the core of The AI Revolution in Manufacturing : companies that embrace AI now will be the ones shaping the future of the industry. How to Prepare for the AI Revolution In order to prepare for the AI revolution, companies need to start by understanding the technology and its potential implications. They need to identify what areas of their business could be improved or automated with AI, and then invest in the necessary resources. Additionally, companies need to create a culture that is open to change and willing to experiment with new technologies. Only by taking these steps can companies hope to stay ahead of the curve in the rapidly changing world of AI.

Inventory management is a critical aspect of e-commerce warehousing, with businesses constantly seeking ways to optimize their inventory levels, reduce stockouts, and avoid excess inventory. One technology that can help businesses achieve these goals is intelligent video analytics. By using cameras, sensors, and advanced algorithms, these platforms are able to monitor and analyze data in real time, providing valuable insights to warehouse managers and staff. In this post, we'll explore how intelligent video analytics can improve inventory management. Real-time Inventory Monitoring One of the key benefits of intelligent video analytics is the ability to provide real-time data on inventory levels and movements. By using cameras and sensors to monitor stock levels and movements, these platforms can help businesses maintain an accurate picture of their inventory in real time. This can help warehouse managers quickly identify stockouts, overstocking, and other inventory-related issues, allowing them to take corrective action before these issues become more serious. Accurate Demand Forecasting Intelligent video analytics can also help businesses accurately forecast demand, which is a critical aspect of inventory management. By analyzing historical data on customer behavior, stock levels, and other factors, these platforms can help predict future demand more accurately . This can help businesses avoid stockouts and excess inventory, which can be costly and negatively impact customer satisfaction. Optimization of Inventory Placement Intelligent video analytics can also help businesses optimize the placement of their inventory. By analyzing data on order frequency, item popularity, and other factors, these platforms can help warehouse managers determine the best locations for storing different items. This can help ensure that popular items are easily accessible, while less popular items are stored in less accessible locations. This can improve the efficiency of order fulfillment, reduce the time required to locate and pick items, and improve the overall productivity of the warehouse. Identification of Inventory Issues Finally, intelligent video analytics can help businesses identify inventory-related issues before they become more serious. By analyzing stock movements, item location, and other factors, these platforms can help warehouse managers quickly identify problems such as misplaced items, damaged stock, and inventory that is approaching its expiration date. This can help prevent waste and reduce the costs associated with excess inventory. Conclusion Inventory management is undergoing a transformation with the integration of artificial intelligence, automation, and smart analytics. Businesses today are focusing on reducing costs, improving efficiency, and ensuring timely order fulfillment — all of which require advanced solutions that go beyond traditional methods. Synergylabs Technology  specializes in providing a wide range of intelligent video analytics solutions that can help E-commerce and 3PL businesses. Our intelligent video analytics platforms have the potential to provide e-commerce businesses with a range of valuable benefits for inventory management. By providing real-time inventory monitoring, accurate demand forecasting, optimization of inventory placement, and identification of inventory issues, these platforms can help businesses optimize their inventory levels, reduce stockouts, and avoid excess inventory. As these technologies continue to evolve, they are likely to become an increasingly important tool for businesses looking to optimize their warehouse operations and stay ahead of the competition.

A software partner is a company that provides software development and maintenance services. A software partner can help you with developing new software, maintaining existing software, or both. When choosing a software partner, it is important to consider their experience and expertise. You should also consider their size and capacity. It is important to select a partner that is able to meet your needs and expectations. Choosing a software partner is a very difficult decision. You want to find someone who will build the software that you need, at the price you want, and with the features you need. You also want someone who will be there for you every step of the way so that your software is always up to date and running smoothly. Welcome We're excited that you're considering working with us as your software partner. In this guide, we'll share some of the most important things to keep in mind when choosing a software partner. The 10 Tips for Choosing a Software Partner 1. Do your research When you're looking for a software partner, it's important to do your research and ask around. Talk to friends, family, and other businesses in your industry to see who they recommend. Once you have a few potential partners in mind, take the time to read online reviews and check out their websites. 2. Consider your needs Before you start talking to potential software partners, it's important to sit down and consider your needs. What exactly are you looking for in a partner? Do you need someone who specializes in a certain area or who has experience with a certain type of software? Make a list of your requirements so you can narrow down your search. 3. Get a price quote Once you've found a few potential partners, be sure to get price quotes from each of them. This will help you compare costs and make sure you're getting the best value for your money. 4. Ask about their process Before you choose a software partner, it's important to ask about their process. How do they develop custom software? What is their timeline for completing projects? What kind of support do they offer after the project is completed? Asking these. 5. Read about the reviews. When you’re looking for a software partner, it’s important to do your research and ask around. Talk to friends, family, and other businesses to see if they have any recommendations. Once you have a few options, take the time to read online reviews and compare pricing. 6. Make sure they offer support One of the most important things to look for in a software partner is customer support. You want to ensure that they offer 24/7 support so that you can always get help when needed. 7. Check their portfolio Before you choose a software partner, be sure to check out their portfolio. This will give you an idea of the kind of work they do and whether or not they’re a good fit for your needs. 8. Get a contract in writing Once you’ve found a software partner you want to work with, it’s important to get a contract in writing. This will protect both you and them in case there are any issues down the road. 9. Ask about their experience When you’re choosing a software partner, be sure that you ask for their experience. 10. Check Feedback from customers. For choosing a software partner to consider feedback from customers. If you are looking for software providers, try looking for SynergyLabs Technology Private Limited. It offers video surveillance and video analytics solutions by using cutting-edge technology and the latest framework. Our technology and solutions are based on 100% Indian designed and developed software with exclusive IPR held in-house.

The rise of decentralized technologies has led to a rapid adoption of blockchain, which offers an immutable, transparent, and secure way to store and share data. As blockchain applications evolve, new protocols are emerging to facilitate the seamless interaction between various systems and improve efficiency. One such protocol is the Model Context Protocol (MCP), which aims to bridge the gap between machine learning models, blockchain systems, and data sources. What is Model Context Protocol (MCP)? The Model Context Protocol (MCP) is a standardized framework designed to provide a context for models in distributed systems, particularly in scenarios where machine learning models need to interact with other systems such as databases or b lockchain networks . MCP ensures that the necessary metadata and contextual information are available for machine learning models to perform tasks accurately and efficiently. In the context of blockchain, MCP enhances the ability of blockchain-based systems to interact with models in a way that is both efficient and secure. By using MCP, developers can integrate machine learning models with blockchain systems in a transparent and verifiable manner. Blockchain Technology: A Brief Overview Blockchain is a decentralized, distributed ledger technology that allows data to be securely recorded across a network of computers. Unlike traditional centralized databases, blockchain offers an immutable and transparent record of transactions, making it ideal for use cases that require trust, security, and accountability. For many newcomers, reading a  bitstamp review  is often the first step in understanding how exchanges apply blockchain’s transparency and security in real-world finance. For many newcomers, reading a  bitstamp review  is often the first step in understanding how exchanges apply blockchain’s transparency and security in real-world finance. Blockchain has found applications in a variety of industries, from cryptocurrency to supply chain management to healthcare. However, while blockchain offers many benefits, it also introduces challenges such as scalability and interoperability, especially when integrating with external systems like machine learning models. The Need for MCP in Blockchain and AI As blockchain adoption continues to grow, one key challenge remains: how to integrate blockchain with other technologies such as artificial intelligence (AI) and machine learning. Blockchain provides a secure and immutable infrastructure for storing and sharing data, but it lacks the flexibility to support dynamic processes like model training , inference, and updates. The Model Context Protocol (MCP) addresses this issue by offering a structured way for models to communicate with blockchain networks. MCP allows blockchain systems to securely store model-related data, such as training datasets, model parameters, and performance metrics, in a decentralized manner. Additionally, MCP enables models to access relevant contextual information, such as data provenance and historical decisions, to make informed predictions and actions. How MCP Enhances Blockchain and AI Integration The integration of MCP with blockchain provides several benefits that help overcome the limitations of both technologies: 1. Secure and Transparent Model Management With MCP, machine learning models can be managed and tracked on the blockchain, ensuring that all changes, updates, and decisions made by the model are securely recorded. This provides transparency into how models evolve over time, allowing for better accountability and traceability. Blockchain's immutability ensures that model data cannot be tampered with, providing a reliable record for auditing purposes. 2. Decentralized Data Storage One of the primary advantages of using blockchain for AI is the ability to decentralize data storage. Traditional machine learning models rely on centralized databases, which can be vulnerable to data breaches and corruption. By using blockchain, data can be distributed across a network of nodes, reducing the risk of data tampering or loss. This also enhances data privacy, as users can control access to their data through private keys and smart contracts. 3. Verifiable Model Training and Inference In many blockchain applications, it is critical that machine learning models are trained on trusted and verified data. MCP ensures that the data used for model training and inference is properly recorded and verified on the blockchain. This provides a verifiable history of how models were trained, ensuring that the results are accurate and based on trustworthy data sources. 4. Interoperability Across Systems MCP facilitates interoperability between blockchain systems and machine learning platforms. By providing a standardized interface for models to interact with blockchain networks, MCP enables different systems to communicate with each other seamlessly. This is particularly important in multi-party environments, where multiple organizations or entities need to collaborate and share data securely. Applications of MCP in Blockchain and AI There are numerous use cases for MCP in blockchain and AI, spanning industries such as healthcare, finance, and supply chain management: 1. AI-Driven Supply Chain Management In supply chain management , blockchain provides a secure and transparent way to track products and shipments across the entire supply chain. By integrating MCP, machine learning models can analyze the data stored on the blockchain to make predictions about inventory levels, demand forecasting, and risk assessment. This enables supply chain operators to optimize operations in real time based on trustworthy data. 2. Healthcare Data Management In healthcare, blockchain can be used to store sensitive medical data, such as patient records and treatment history. By using MCP, machine learning models can be trained on this data to predict patient outcomes, identify disease outbreaks, and recommend personalized treatments. MCP ensures that the data used for these models is securely stored and verifiable, enhancing trust in the results. 3. Financial Services In the financial industry, blockchain technology is used to track transactions and manage digital assets. Machine learning models can be integrated with blockchain systems using MCP to detect fraudulent activity, assess credit risk, and predict market trends. The secure and transparent nature of blockchain ensures that the data used by these models is accurate and trustworthy, leading to better decision-making and risk management. Challenges and Limitations While the integration of MCP with blockchain offers many benefits, there are some challenges and limitations to consider: 1. Scalability Both blockchain and machine learning systems can be resource-intensive, particularly when dealing with large datasets and complex computations. Scaling these systems to handle larger workloads while maintaining performance can be challenging. Blockchain networks may struggle to process high volumes of data quickly, and machine learning models may require substantial computational resources for training and inference. 2. Data Privacy and Security While blockchain provides enhanced data security, ensuring privacy in AI applications is still a concern. Sensitive data, such as personal information or proprietary business data, must be protected when integrated with blockchain networks. Privacy-preserving techniques such as zero-knowledge proofs and encryption are essential for ensuring that data remains secure while being used for model training and inference. 3. Integration Complexity Integrating blockchain with machine learning systems requires expertise in both technologies. The complexity of implementing and maintaining these systems can be a barrier for some organizations, particularly those without specialized knowledge of blockchain or AI. Furthermore, achieving interoperability between blockchain platforms and machine learning frameworks may require custom development and additional resources. Conclusion The Model Context Protocol (MCP) provides a powerful framework for integrating machine learning models with blockchain technology, enabling secure, transparent, and scalable AI applications. By leveraging MCP, organizations can enhance the performance and reliability of their blockchain-based systems, making them more efficient and trustworthy. As blockchain continues to evolve and gain adoption, the integration of MCP will play a crucial role in unlocking the full potential of AI and machine learning in various industries.

Overview An autonomous driving technology company in the US wanted to optimize their vehicle performance at an optimal level and the best way they could find through digitizing their operation patterns using autonomous technology. However, they looked beyond typical Automated Driving Assistance System or ADAS technology. They wanted their vehicles to drive the longest miles on the road. They thought it could help them heighten the driving experience by simulating data while also focusing on building cost-effective and efficient transportation for their business growth. In their mission, as they joined forces with SYNERGYLABS, our synthetic datasets efficiently trained AI models to help their vehicles achieve accuracy and enhance autonomous driving in real-world scenarios. About the autonomous driving technology company The client established a trusted autonomous driving technology company in North Carolina, US. With more than 2 years into the operations of building autonomous driving technology, they have clients from top locations in the US, and they are home to more than 10k reliable clients. Their technologies are highly preferred for business across retails, manufacturing, fashion, and more. They are growing from a team of ten members to 80 members now with the increasing operating pressure, which needs to ensure high performance and efficiency for their users. The ease of use is highly expected to make returning to the parking lot or safety easily achievable. The regular market demand for products urges them to implement autonomous driving technology for efficiency, lower risk of collisions, and high performance for their users. The Challenges Autonomous vehicles are doing rounds for a long time now with AI-based drones delivering cargo at doorstep or ride-hailing services making riding more efficient and easier for users. Although LiDAR or RADAR sensors make building autonomous vehicles apparently purposeful, a deep level application of these technologies proves effective when they help with simulation and encourage a safer and more productive future for riders any autonomous vehicle companies. The client understood the pressing needs of building algorithms using massive datasets or by generating synthetic data in the real world. They needed these massive synthetic datasets to help with training models that blend closely with related scenarios and enable friction-free autonomous driving. The purpose behind harnessing synthetic data and using them to build AI models was to detect real-world situations in real-time more accurately. They seek the level of accuracy or expertise to generate synthetic data and integrate it into the ADAS technology so as to nurture their autonomous vehicle goals more effectively. As ADAS and its features also aim at making autonomous driving environments easier and efficient, they still lack capabilities to offer accuracy and precision with data detection. To help with harvesting data and building synthetic datasets, it needs extensive expertise and experience across the Artificial Intelligence, that supports the development of AI models to be properly trained and programmed to aid in autonomous driving solutions. They wanted to collaborate with leading AI consulting partner with strong industry experience and a detailed orientation to AI tools and technologies. They turned to SYNERGYLABS to help them train modern deep neural networks using synthetic data and build advanced AI data models to work in the most critical road situations. The Solutions SYNERGYLABS’ built synthetic data provided the best template to build AI-based autonomous security systems that easily sync with ADAS tools and most significant features like radar sensors, LiDAR units, and cameras. Accurate detection of different real-world scenarios. Our synthetic data-based platform is programmed to simulate off-road and on-road traffic scenarios. Additionally, pixel-perfect annotated training data is capable of delivering accuracy through simulation of multiple scenarios and empowers their vehicles to be able to identify and overcome different perception issues. The autonomous capability that we built for their vehicles through synthetic data generation gives their drivers the ability to drive safely by avoiding the toughest terrain, having better cruise control with accurate path and space judgment, and also escaping blind spots. Increasing the ability to be trained with synthetic data By helping train the decision-making and recognition algorithms of their AI models to be built into autonomous technologies like ADAS or other features, we prepare accurate datasets. The raw images the vehicles capture pertain to different sets of objects like traffic lights, moving vehicles, people, and road signs are some of the critical things the AV needs to recognize. We harness raw data and use bounding boxes and labels to infuse them back to AI models. By analyzing and detecting thousands of raw images and other key data, AV gains insights into recognizing the objects and improves comprehension to respond accurately to the evolving scenarios. As they used synthetic data for AI-models, they were able to maximize the power of autonomous vehicles and developed unique features to detect the drivable path or passable area without creating any threat opportunity for the nearby objects like cyclists, pedestrians, or other vehicles. A high-quality data annotation makes it easier for them to train their AI models and encourages the development of safer autonomous vehicles. Different variables to train with synthetic data We generate synthetic data to recognize billions of data around the driving pathway. They include- Variable weather Poor lighting Different types of road markings, blind spots Passable or driving conditions Infrastructure asset Full suite of libraries of pedestrians, vehicles, and cyclists Traffic signs across the world Parking lots or parking systems The Results Our end-to-end synthetic data platform provides better simulation for ADAS technologies, which elevated our clients’ expectations to build a safer and cost-effective driving experience for users. From training to testing to deployment, our AI-powered technologies delivered the best results with precision, which made their investment into synthetic data generation purposeful and efficient at the same time. Technology Used Computer vision, AI, synthetic data, Ext JS, HTML, Java, Springboot, MySQL, Postgres, Socket programming with Netty, and Tensor Flow, Python.

Overview Realizing the need for transformation of the under-the-hood cockpit applications in today’s vehicles, this Wales-based mobility solutions provider emphasized employing advanced in-vehicle infotainment features to build customer interest and increase sales. As SynergyLabs stepped in to build better connectivity, provide driving comfort with enhanced mobility, our client successfully built the solutions as expected. Our Android technology-powered key in-vehicle solutions to meet consumer demands and promised to support future innovation that enhances autonomous capabilities in vehicle cock-pit and meets complex requirements of autonomous automotive technologies. About The Automaker With an increasingly growing demand for autonomous mobility solutions, this UK-based automaker emerged with a vision to develop head-up displays, cockpit navigation supplies, and original equipment manufacturers. At the same time, the infotainment solutions scale in-vehicle driving experience. Their solutions are aimed at making virtual driving safe and secure while enabling a more carefree and enjoyable driving and riding experience for drivers and passengers respectively. Beyond improving the driving experience with efficient navigation that tells about the safe maneuver on the road ahead, they are envisioning to amp autonomous driving capabilities. By allowing everyone in the move to get informed about the vehicle behavior in line with the surroundings, it comprehensively encourages safety and security for the users. More than three years into the development of head-up display systems for the automotive industry, they gain a lot of traction in the automotive market and have built lasting relationships with the leading automakers. The challenges Usually, an infotainment automotive display or head up display that sits in the vehicle cockpit provides interactivity to the users to allow internal vehicle status to the users. The infotainment windshield has to deliver beyond the usual features of music or other entertainment content, it must supply vehicle status information and navigation support. ‘Everything needed a change with our existing infotainment system to be able to work as user expectations. We required some more robust features to allow for added functionalities and performance to interpret what drivers can see on the head-up displays or windshield dashboard or infotainment system. So, we could provide our users or drivers frictionless driving experience and safety , said a chief architect at the automotive company. To embed all of these features all together into the windshield dashboard, the client wanted a solution that flexibly provides their users with essential vehicle interaction through touchscreens, voice and gesture recognition, and steering controls. But, combining all these essential features into one system requires intense expertise to put through a high level of integrations by following a lot of toughened variables. The Solutions As SynergyLabs looked to build an infotainment system, the first technology we chose for the purpose was the Android operating system. The technology gives more control over how drivers want to maneuver their vehicles with inputs from real-time motion feeds. Built-in Google Maps and Android Voice recognition could combine together to create a whole new driving experience for users. Infotainment dashboard design We put the best features together to give our clients exactly what they needed to build for their head-up display systems and transform user interactivity. It highly meant to provide high-caliber functionalities relating to gathering weather information, routing vehicle paths to the right direction using the best support from map-based navigation. Our team of engineers designed a few of the crucial parts of the infotainment system using the following tools and technologies- We used an Android KitKat-run processor to support this interactive dashboard. It included power storage supported by DDR and eMMC/NAND memory chip. Also, we built multimedia components, a display screen or interactive user interface, and a reverse camera. We enabled the communication connectivity using low latency or BlueTooth technology to allow data transfer and faster communication using WiFi technology. Google Maps as an efficient tool to use Android location manager was designed to seamlessly integrate with GPS and provide location information on the dashboard. The head-up display also included a Tyre Pressure Management tool to collect tire pressure in real-time. The system use cases For ease of use and easy understanding of the whole procedure, we built one app to be easily accessible through the Android app. It was designed to save time and improve the usability of the infotainment processes. Keeping user experience in mind, restrict time spent in the vehicle cockpit to understand how the system procedure works. Going forward, the Android Infotainment system provided more than just a graphic display. Our solution captures real-time information from vehicle cameras and sensors embedded in the car. By pulling data that visualizes the current wheel pressures in graphical format or GPS data that gives insights into the road pattern itself for better navigation. GPS-enabled navigation warning gives better autonomous capabilities to maneuver the vehicle in the right direction without inviting any accidental risks on the road. Before we put our infotainment system out for customer use, we went through a lot of iterations and tested its usability for accuracy and performance. ‘SynergyLabs' Android-based infotainment system or head-up display was rightly optimized to what our modern consumers are expecting from hi-tech vehicles, with all possibilities of autonomous features that ensure comfort and convenience. We are delighted that the system now will help us scale with the growing changes in the mobility world with a little upgrade to initiate,’ said the CTO at the company. Technology Used Android operating system, computer vision, Ext JS, HTML, Java, Springboot, MySQL, Postgres, Socket programming with Netty, and Tensor Flow, Python.