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Applied Innovation

Revolutionizing Knowledge Management Systems with AI and Generative AI

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Applied Innovation

Revolutionizing Knowledge Management Systems with AI and Generative AI

Knowledge Management Systems (KMS) have become critical for high-performing businesses in today’s quickly changing business technology environment. With the combination of Artificial Intelligence (AI) and Generative AI (Gen AI), these systems are being turned into super-powered tools that expedite workflows, increase productivity, and give a competitive advantage.

The Power of Knowledge Management Systems

Knowledge Management Systems are designed to store, organise, and retrieve information, ensuring that employees have access to the facts they require to make sound choices. High-performing businesses use KMS differently than their mid- and low-performing peers, utilising these systems to provide insights for business choices and automating procedures. These firms may improve their efficiency, productivity, and innovation by integrating diverse data sources and using AI solutions.

A strong KMS is the cornerstone of all knowledge management activities. Using technology to simplify workflows and avoid duplication of effort is critical for increasing productivity and efficiency. Furthermore, integrating decarbonisation initiatives with corporate objectives via KMS can improve customer experience and service quality. Measuring the performance of these systems using metrics like before-and-after analysis is critical for continual improvement.

Metrics for Success

Implementing AI-powered knowledge management necessitates careful evaluation of numerous parameters. Access to data and information is critical for all stakeholders, including workers, agents, and partners. Making sure that information is intelligible and available in several languages increases its efficacy. Regular interaction and feedback assist to assess the use of information, ensuring that it is used efficiently and effectively.

Modern customers expect rapid and simple access to information, necessitating the creation of succinct, current content. This method addresses the current trend for concise information, making it easier for users to digest and implement what they learn.

The Impact of AI and Gen AI on Knowledge Management

The integration of AI and Gen AI into knowledge management systems represents a substantial shift in data handling. Historically, information was spread across several platforms, making it difficult to digitise and automate. Today’s focus is on consolidating and comprehending this data in order to develop accurate, personalised, and predictive information. AI and Gen AI have the ability to transform knowledge management across many industries, including healthcare, where compliance and security are critical.

AI usage in healthcare differs between primary, secondary, and tertiary care settings. Primary healthcare makes the most extensive use of AI to increase patient happiness, but tertiary healthcare, which involves higher-risk treatments such as surgery, is slower to embrace because to the inherent hazards. AI in the diagnostic sector, notably for image processing and prediction, serves as a clinical decision support system, although regulatory limitations and the technology’s “black box” nature pose obstacles.

Key Components of a Successful Knowledge Management Strategy

A effective knowledge management strategy is built on several critical components, including information accessibility, a people-centric approach, and technological integration across several applications and systems. Gen AI may assist gather and construct data repositories from a variety of sources, but it must be thoroughly tested to assure accuracy, especially in key industries like healthcare and cosmetics. Trust in Gen AI systems will grow as they mature, potentially providing suggestions for healthcare professionals.

Data security and feedback loops are critical to ensuring the integrity and efficacy of AI-driven KMS. Protecting sensitive information involves validating and regulating data access, as well as adopting encryption and other security measures. Regular user input helps to fine-tune AI models, boosting their performance and dependability.

Overcoming Challenges in Knowledge Sharing

Implementing Gen AI-powered knowledge management systems is not without hurdles. Data silos, which separate information between distinct departments or organisations, can restrict the flow of knowledge. Furthermore, human reluctance to share information, which is motivated by variables like as rivalry, fear of losing hierarchical rank, or job security concerns, creates a considerable obstacle.

Addressing these difficulties necessitates a culture shift towards information sharing, spearheaded by leaders. Leaders must set the example by making data-driven choices and encouraging cross-collaboration. Recognising and rewarding employees for their contributions to the knowledge database can help to build an open and collaborative environment.

Adoption by Startups

Startups, too, may profit from AI-powered KMS. It is critical to assess the requirement for such systems in relation to the size and type of the firm. Starting small and focussing on high-impact tasks can help startups progressively incorporate KMS into their operations. Crowdsourcing expertise and utilising real-time data can help improve decision-making and future learning.

The Future of AI in Enterprises

As AI tools such as analytics and machine vision become more widely available, firms must develop high-impact growth plans. Starting small and iterating based on feedback enables businesses to learn and adapt. AI-powered knowledge management solutions for businesses with proprietary and confidential data handle privacy issues by using light models with low hallucination rates.

These systems handle a wide range of data formats, including documents, videos, audio files, and tables, and they enable real-time connections between libraries and sources. These systems improve overall knowledge management efficiency by allowing for real-time changes while also maintaining transparency, dependability, and data security.

Document Intelligence Solutions Gaining Importance

Document intelligence technologies are becoming increasingly vital for managing unstructured data while maintaining high accuracy. These methods provide transparency and dependability via correct document indexing, chunking, hierarchy map generation, and Knowledge Graph building. Data security is prioritised in on-premises or private cloud systems that use role-based access control.

These technologies may be used for smart operations as well as financial and ESG data analytics. In smart operations, AI-powered assistants may handle customer support issues by analysing technical papers and replying to enquiries. Structured data helps financial analysts do research and create reports.

Takeaway:

The integration of AI and Gen AI into Knowledge Management Systems is transforming the way businesses manage and utilize information. By addressing challenges such as data silos and human reluctance to share knowledge, and by fostering a culture of collaboration, companies can harness the full potential of these advanced technologies. As we move forward, the continuous evolution of AI-driven KMS will undoubtedly play a pivotal role in driving innovation, efficiency, and success across various industries.

Contact us at open-innovator@quotients.com to schedule a consultation and explore the transformative potential of this innovative technology.

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Applied Innovation

Using AI to Transform Energy Efficiency and Maximize Resource Utilization

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Applied Innovation

Using AI to Transform Energy Efficiency and Maximize Resource Utilization

Energy efficiency and conservation are important to meet global targets for reducing greenhouse gas emissions, fossil fuel use, grid load, and prices. There are a wide range of additional advantages including reduced Carbon Footprint, decreased power costs, etc. In order to maximize energy efficiency, cut expenses, and pave the road for a sustainable future, AI has emerged to be a potent ally.

Energy Efficiency Revolutionized by Smart Buildings

The use of AI to optimize energy use in office and residential buildings has enormous potential. To predict and improve HVAC systems, machine learning algorithms may be used to analyze data from sensors, weather predictions, occupancy patterns, and past energy consumption. AI systems may learn continuously, adapting to shifting variables like temperature changes and occupancy levels to save energy while maintaining a comfortable interior environment.

Industrial Process Efficiency

AI can reduce energy use in several sectors by carefully monitoring and managing a wide range of operations. Machine learning algorithms can recognize actions that use a lot of energy, find inefficiencies, and suggest changes. For instance, AI may adjust the timing of industrial processes to lower peak and total energy use. The health and performance of equipment may also be monitored by AI-powered systems, opening up options for energy-saving measures like motor speed modifications and process parameter optimizations.

Integration of Renewable Energy Leveraging AI

AI plays a crucial part in the smooth integration of renewable energy sources like solar and wind into the electrical grid as they gain in popularity. Artificial intelligence (AI) systems use historical data and weather trends to anticipate energy generation from renewable sources with accuracy. AI systems optimize the dispatch of energy from renewable sources, guaranteeing effective utilization and reducing curtailment. This information is combined with real-time power demand. AI also helps to forecast and manage the need for energy storage, improve grid stability, and balance supply and demand dynamics.

Intelligent Power Delivery

AI can be crucial for managing grid operations and optimizing energy distribution networks. Utilities can anticipate electricity demand with accuracy by utilizing the capabilities of machine learning algorithms, historical data, and real-time information. This enables them to create a more precise balance between supply and demand, optimize energy-generating schedules, and decrease transmission losses. A stable and robust energy infrastructure can further be ensured by AI’s grid management capabilities, including anomaly detection, equipment failure prediction, and optimized maintenance scheduling.

Towards Efficiency in Transportation

AI is essential for maximizing energy use in the transportation sector, which reduces emissions and fuel use. To improve route planning and driving behavior, machine learning algorithms carefully examine elements such as traffic patterns, road conditions, and vehicle attributes. AI systems’ real-time feedback and recommendations help drivers adopt fuel-efficient driving practices including smooth acceleration and braking. It can also help manage the infrastructure for electric car charging by balancing grid energy demand and optimizing charging schedules.

Benefits:

Numerous advantages result from the deployment of AI-driven energy optimization approaches. First of all, it lowers energy expenditures and consumption, which saves companies a lot of money and results in cheaper power bills for customers. Second, AI helps to promote a cleaner and more sustainable energy ecosystem by reducing energy waste and encouraging the integration of renewable energy sources. Furthermore, AI improves grid stability and dependability, enabling utilities to effectively manage energy distribution and grid operations and guaranteeing a consistent and reliable energy supply. As a final benefit, AI increases operational effectiveness by spotting inefficiencies, streamlining procedures, and automating energy management duties, freeing up human resources for more worthwhile projects.

Energy optimization is ready to undergo a revolution thanks to AI’s revolutionary potential, which will increase efficiency, lower prices, and promote sustainability. Businesses, industries, and utilities may reduce their carbon footprint, save a significant amount of energy, and actively contribute to a more sustainable future by adopting AI technology. The road to optimal resource and energy use can be paved with AI as a reliable partner.

If you’re interested in exploring these technologies and their use cases further, don’t hesitate to reach out to us at open-innovator@quotients.com. We are here to assist you and provide additional information.

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Applied Innovation

Leveraging AI, ML, CV, and NLP to transform unstructured data into valuable intelligence

3D Digital Twins: The Key to a More Efficient, Safer, and Sustainable Future
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Applied Innovation

Leveraging AI, ML, CV, and NLP to transform unstructured data into valuable intelligence

In today’s digital era, organizations are swimming in a vast ocean of data, with a significant portion of it residing in unstructured documents. These documents, such as emails, contracts, research papers, and customer feedback, hold a wealth of valuable information waiting to be unlocked. However, extracting meaningful insights from this unstructured data has traditionally been a daunting task. Enter the power of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP). These transformative technologies are revolutionizing the way businesses derive value from the data encapsulated within unstructured documents.

Unstructured documents differ from structured data sources, such as databases or spreadsheets, as they lack a predefined format or organized data model. They contain free-form text, images, tables, and diverse information types, making them challenging to analyze using conventional methods. However, advancements in AI, ML, and NLP have paved the way for extracting valuable insights, patterns, and knowledge from these untapped resources.

By applying intelligent algorithms and techniques, businesses can gain a competitive edge, drive innovation, and make informed decisions based on comprehensive data analysis. NLP techniques enable the classification of unstructured text data, such as categorizing emails, research papers, or customer reviews, leading to automated organization and efficient data retrieval. ML algorithms, both supervised and unsupervised, can be used to recognize patterns, detect anomalies, and make predictions within unstructured documents. By employing computer vision algorithms, organizations can automatically classify images, identify objects, and generate textual descriptions, revolutionizing fields like healthcare, security, and manufacturing.

Deriving value from unstructured data is a significant challenge, but leveraging Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision (CV) technologies can help unlock its potential. Here’s a high-level overview of how these technologies can be used:

Data Preprocessing: Before applying AI and ML algorithms, unstructured data needs to be processed and structured. This involves tasks like data cleaning, normalization, and transforming the data into a suitable format for analysis.

Natural Language Processing (NLP): NLP techniques can be used to classify unstructured text data into predefined categories or topics. This can enable automated categorization and organization of large amounts of textual information. Then by Named Entity Recognition (NER), algorithms can identify and extract entities like names, locations, organizations, and other relevant information from unstructured text. AI models then analyze text sentiment to determine whether it’s positive, negative, or neutral. This can be useful for understanding customer feedback, social media sentiment, or market trends. NLP techniques can also automatically generate summaries of large documents or text datasets, enabling quick extraction of key information.

Machine Learning (ML): ML algorithms can be trained on labeled data to recognize patterns and make predictions. For example, ML models can learn to classify images, identify objects, or recognize patterns in unstructured data. Through unsupervised learning, these algorithms can identify hidden patterns or clusters in unstructured data without any predefined labels. This can help in data exploration, segmentation, or anomaly detection. ML algorithms can also analyze user behavior, preferences, and unstructured data such as product reviews or browsing history to make personalized recommendations. Along with things, ML models can learn patterns from normal data and identify outliers or anomalies in unstructured data, which is particularly useful for fraud detection or cybersecurity.

Computer Vision (CV): CV techniques can classify and categorize images or videos based on their content, enabling automated analysis and organization of visual data. These algorithms can identify and locate specific objects within photos or videos. This can be useful in various applications, such as self-driving cars or surveillance systems. Such AI models can also generate textual descriptions or captions for images, enabling better understanding and indexing of visual data.

Use Cases

By combining these technologies, organizations can extract valuable insights, automate manual processes, improve decision-making, enhance customer experiences, and gain a competitive edge by making the most of unstructured data.These technologies can be used to analyze customer feedback from social media posts, reviews, or customer support interactions to understand the sentiment, identify emerging trends, and improve products or services. it can help organizations to automatically categorize customer queries or complaints to prioritize and route them to the appropriate departments for faster resolution. These algorithms can mine unstructured data from customer surveys or feedback forms to extract actionable insights and identify areas for improvement.

Analyzing unstructured data, such as transaction logs, emails, or support tickets, can help identify patterns indicative of fraudulent activities or cybersecurity threats. By applying NLP techniques it can be used to detect suspicious text patterns or anomalies in financial reports, insurance claims, or legal documents. By combining unstructured data sources like social media posts, news articles, and public records to assess reputation or compliance risks associated with individuals or organizations.

Using CV algorithms for facial recognition and object detection in surveillance videos to enhance security measures and identify potential threats or suspicious activities. Analyzing images from medical scans or remote sensing data can be used to assist in diagnosis, detect anomalies, or monitor environmental changes. ML and CV techniques can also be applied to monitor manufacturing processes, detect defects in products or equipment, and ensure quality control.

Extracting structured data from unstructured documents like invoices, contracts, or financial reports to automate data entry, streamline workflows, and improve operational efficiency. Automatically generating summaries or key insights from lengthy reports, research papers, or legal documents to aid in information retrieval and decision-making.

These use cases highlight the diverse applications of AI, ML, NLP, and CV in deriving value from unstructured data across various industries, including finance, healthcare, retail, manufacturing, and more. By harnessing the power of these technologies, organizations can unlock valuable insights, drive innovation, and gain a competitive edge in today’s data-driven landscape.

If you’re interested in exploring these technologies and their use cases further, don’t hesitate to reach out to us at open-innovator@quotients.com. We are here to assist you and provide additional information.

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Applied Innovation

On-Premise AI Solutions for Businesses: Balancing Control, Flexibility, and Investment

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Applied Innovation

On-Premise AI Solutions for Businesses: Balancing Control, Flexibility, and Investment

In the rapidly evolving landscape of artificial intelligence (AI) technologies, businesses face the critical decision of choosing between on-premise and cloud-based AI solutions. On-premise AI solutions refer to applications that are installed and operated on the businesses’ own servers or computers, offering enhanced control over data, intellectual property, flexibility, and security. While the advantages of on-premise AI solutions are compelling, they come at a cost, requiring substantial upfront investment, ongoing maintenance, and expertise.

Some potential users of On-Premise AI Solutions

A fintech company that wants to offer 24/7 customer support via chat, email, and phone using AI agents that can handle queries related to payments, transactions, security, and more.

For a fintech company aiming to provide round-the-clock customer support, on-premise AI solutions can prove invaluable. By deploying AI agents on their own servers or computers, the company gains greater control over sensitive financial data while ensuring seamless customer interactions. The AI agents can handle a wide range of customer queries related to payments, transactions, account security, and more, offering prompt and accurate responses.

On-premise AI solutions enable the fintech company to customize the AI agents to align with their specific business requirements. The AI agents can be trained on vast datasets of financial information, allowing them to understand complex queries and provide tailored solutions. With real-time access to customer data stored on internal servers, the AI agents can offer personalized assistance, addressing individual customer concerns and providing relevant information about account balances, transaction histories, or security protocols.

Additionally, on-premise AI solutions ensure the security and confidentiality of customer data. As financial information is highly sensitive, the company can implement robust security measures and encryption protocols within its own network. By avoiding the use of external cloud platforms, the fintech company minimizes the risk of data breaches and unauthorized access.

An e-commerce company that wants to increase conversions and retention by using AI agents that can provide personalized product recommendations, upsell and cross-sell offers, and feedback collection.

For an e-commerce company aiming to enhance customer engagement and boost sales, on-premise AI solutions offer a powerful toolset. By utilizing AI agents installed on their own servers or computers, the company can leverage the benefits of personalized product recommendations, upselling, cross-selling, and efficient feedback collection.

On-premise AI solutions enable the e-commerce company to process vast amounts of customer data and analyze browsing patterns, purchase histories, and customer preferences within their secure internal environment. The AI agents can then utilize this information to deliver highly personalized product recommendations, suggesting items that align with individual customer tastes and interests. This level of customization significantly improves the customer experience, leading to increased conversions and customer satisfaction.

Moreover, on-premise AI solutions allow the e-commerce company to implement targeted upselling and cross-selling strategies. The AI agents can identify relevant products or services that complement customers’ existing purchases, enabling the company to increase order values and drive additional sales. By suggesting upgrades, accessories, or related items, the AI agents facilitate a seamless and personalized shopping experience.

Furthermore, on-premise AI solutions facilitate effective feedback collection and analysis. The AI agents can interact with customers through chatbots or email surveys, collecting valuable insights and opinions regarding product quality, user experience, and customer service. This feedback can then be utilized to refine marketing strategies, improve product offerings, and enhance overall customer satisfaction.

A healthcare company that wants to improve patient engagement and satisfaction by using AI agents that can provide health information, appointment scheduling, medication reminders, and symptom checking.

In the healthcare industry, on-premise AI solutions can revolutionize patient engagement and satisfaction by deploying AI agents capable of delivering a wide range of services. By using on-premise AI solutions, healthcare companies can ensure the privacy and security of patient information while offering personalized and convenient healthcare experiences.

AI agents installed on the healthcare company’s own servers or computers can provide patients with instant access to essential health information. From general medical advice to specific queries about symptoms, medications, or treatments, AI agents can offer accurate and reliable responses, helping patients make informed decisions about their health.

Appointment scheduling becomes streamlined and efficient with AI agents handling the task. Patients can interact with the AI agents through various channels such as chatbots or voice assistants, allowing them to conveniently book appointments, reschedule, or receive notifications

Benefits and challenges of on-premise AI solutions

One of the primary advantages of on-premise AI solutions is the level of control they afford to businesses. By housing AI applications on internal servers or computers, organizations maintain complete authority over their data and intellectual property. This is particularly crucial for industries dealing with sensitive information, such as healthcare, finance, or defense, where compliance and regulatory requirements demand stringent data protection measures. With on-premise AI, businesses can enforce their own security protocols and ensure that sensitive data remains within their network boundaries, minimizing the risk of data breaches or unauthorized access.

Flexibility is another key advantage of on-premise AI solutions. By having direct access to their AI infrastructure, businesses can tailor the system to their specific requirements and integrate it seamlessly with existing on-site applications and processes. This level of customization enables organizations to leverage the full potential of AI technologies in a manner that aligns with their unique business goals. Additionally, on-premise solutions offer greater flexibility in terms of data storage and processing. Businesses can choose to keep sensitive or critical data on local servers, allowing faster access and reducing dependence on external networks.

Despite the advantages, implementing on-premise AI solutions can pose challenges for businesses. The foremost consideration is the upfront investment required. Setting up and maintaining an on-premise AI infrastructure involves significant costs, including hardware procurement, software licensing, and skilled personnel. Businesses must carefully evaluate their budgetary constraints and long-term AI strategy to ensure that the investment is justified by the benefits and potential returns.

Another challenge is the need for ongoing maintenance and expertise. With on-premise AI solutions, organizations assume the responsibility of managing the system’s performance, updates, and troubleshooting. This demands a dedicated team of IT professionals with the necessary knowledge and skills to handle AI infrastructure effectively. The recruitment or training of personnel can present additional expenses and time commitments. Moreover, the rapid pace of AI advancements requires continuous learning and adaptation to stay up-to-date, placing further demands on businesses to cultivate an AI-savvy workforce.

Security is a critical concern for any AI deployment, and on-premise solutions offer a certain degree of control in this regard. However, they also require businesses to bear the responsibility of implementing robust security measures. This involves ensuring the physical security of servers, implementing firewalls and intrusion detection systems, conducting regular security audits, and maintaining compliance with relevant regulations. Failure to meet these requirements can expose organizations to various risks, such as data breaches, unauthorized access, or reputational damage.

Despite the challenges, the benefits of on-premise AI solutions are undeniable, making them a viable option for businesses seeking enhanced control and flexibility. By investing in on-premise infrastructure, organizations gain the ability to protect sensitive data, customize AI systems to their specific needs, and integrate AI seamlessly with existing processes. However, businesses must carefully consider their resources, both financial and human, and evaluate the long-term implications of managing an on-premise AI infrastructure.

In conclusion, on-premise AI solutions for businesses offer a range of advantages, including increased control over data and intellectual property, flexibility in customization, and heightened security. However, the decision to adopt an on-premise AI infrastructure requires careful consideration.

We offer on-premise AI solutions through our extensive partner networks. If you would like to learn more about how these solutions can benefit your business, please reach out to us at open-innovator@quotients.com. Our team will be happy to provide you with detailed information and answer any questions you may have. Don’t miss the opportunity to explore the advantages of on-premise AI solutions for your organization. Contact us to start the conversation and discover how AI can transform your business.

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Applied Innovation

How Digitization and Automation with NLP and AI Can Revolutionize Care Programs

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Applied Innovation

How Digitization and Automation with NLP and AI Can Revolutionize Care Programs

Digitization and automation of care programs including remote patient monitoring, symptom management, patient education, and clinical process automation have become a must to increase effectiveness, and accessibility, and improve patient outcomes. These innovations enhance care delivery and resource use in the healthcare industry by enabling real-time monitoring, personalized symptom treatment, accessible patient education, and faster procedures.

Artificial intelligence is being employed by platforms to prioritize patients, offer personalized messages, gather patient-reported outcomes, and monitor care plan adherence. To simplify data gathering and reporting, the platform also connects with current electronic health records and other platforms.

For healthcare platforms to comprehend patient wants and preferences and produce personalized replies, natural language processing (NLP) and machine learning (ML) are essential components. NLP approaches examine language produced by patients and extract pertinent data, including symptoms, worries, and preferences. Sentiment analysis is performed by ML algorithms in conjunction with NLP to evaluate patients’ emotional states and satisfaction levels and provide the necessary assistance. While entity identification derives from medical conditions and lifestyle characteristics, intent recognition models classify patient inquiries to appropriately understand their demands.

Healthcare practitioners may deploy resources efficiently thanks to the platform’s AI algorithms that triage patients according to their symptoms, medical history, and urgency. Sending reminders and messages that are specifically targeted to the treatment goals and preferences of each patient, also makes personalized communication possible. It makes it easier to get patient feedback by giving real-time information on symptoms, medication adherence, and quality of life. It checks patients’ compliance with care programs, sends out reminders, and enables proactive interventions to enhance results.

As a result of the platform’s seamless integration with current healthcare systems, human data entry is not necessary, and data flow is uninterrupted. Comprehensive reporting, analytics, and population health management are made possible by this combination. Enhanced patient interaction, better care coordination, data-driven decision-making, effective resource allocation, and simplified reporting and compliance are just a few advantages of the platform.

Consequently, the platform transforms healthcare organizations by utilizing AI, digitizing and automating procedures, and fostering patient-centered care. It gives patients and healthcare professionals more influence, which improves patient outcomes, efficiency, and experiences overall.

To discover more about the various evolving use cases in different industries, kindly reach out to us at open-innovator@quotients.com.

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Healthtech Startup Stories

Precision medicine and AI together might completely transform the medical industry

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Healthtech Startup Stories

Precision medicine and AI together might completely transform the medical industry

Precision medicine or precision health, also called personalized medicine, helps medical professionals find an individual’s unique disease risks and treatments based on his unique biology and life circumstances. Precision Health focuses on predicting, preventing, and curing disease before it strikes.

This approach is a fundamental shift to care that empowers people and allows doctors and researchers across medical disciplines to determine the best care for each individual patient; identify disease mutations in patients with undiagnosed conditions. It also helps avoid serious side effects from medications and takes into consideration genetic risk factors.

To provide personal, comprehensive, and effective care, precision health starts with a comprehensive consultation where the current health status, medical and family history, personal health objectives, etc are discussed. With this biometrics, and other functional and physical examination is done to diagnose any diseases or conditions, and to identify their root causes. Comprehensive lab tests are also done and detailed results are studied to gain an even better understanding of the patient’s health profile.

Precision healthcare providers then develop an exhaustive plan customized to the patient’s unique needs. The medical plan addresses the current disease or illness, the root cause of the disease, and reversing any factors that give rise to the disease. Drivers of imbalances in the body’s biological systems, physiological processes, assimilation of nutrients, inflammation or energy production, etc are also taken care of. Multimodal strategies concerning lifestyle factors like diet, nutrition, exercise, and stress management are also adopted to optimize health and improve a patient’s health Regular monitoring is also done to assess progress and evaluate the effectiveness of the care plan. A full reassessment of each patient is also done yearly like a physical and functional exam, biometric measurements, and lab tests.

Precision medicine and artificial intelligence (AI) working together might completely transform the medical industry. Precision medicine techniques isolate patient phenotypes with less frequent responses to therapy or particular medical requirements. Through the use of complex computing and inference, AI helps to develop insights, allows the system to reason and learn, and enhances clinical decision-making. Recent literature suggests that translational research examining this convergence will aid in resolving the most challenging issues facing precision medicine, particularly those where nongenomic and genomic determinants will facilitate personalized diagnosis and prognostication along with data from patient symptoms, clinical history, and lifestyles.

Verily, a subsidiary of Alphabet is using a data-driven approach to change the way people manage their health and the way healthcare is delivered. Launched from X in 2015, Verily relies on the increasing ability to use the power of technology to create new tools to generate evidence, new infrastructure to handle data, and new business models that can deliver on the promise of precision health. It generates and activates data from a wide variety of sources, including clinical, social, behavioral, and the real world, to arrive at the best solutions for a person based on a comprehensive view of the evidence. The company for this uses its recognized expertise and capabilities in technology, data science, and healthcare to enable the entire healthcare ecosystem to drive better health outcomes.

Reach out to open-innovator@quotients.com to know more about open innovation updates, programs, and collaboration opportunities.

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Industry 4.0

AI-powered Computer Vision Revolutionizing Multiple Industries

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Industry 4.0

AI-powered Computer Vision Revolutionizing Multiple Industries

Inspections are critical for attaining manufacturing excellence. Inspection of processes and products determine the business success and customer confidence in the brand. Companies are increasingly relying on next-generation inspection solutions to improve quality control and deliver defect-free products. 

AI-powered Computer Vision

Digitization and machine vision based on AI algorithms can identify manufacturing anomalies much faster than human inspectors and improve quality and reduce costs. It employs computer vision, a field of artificial intelligence (AI) that enables computers and systems to obtain useful insight from digital images, videos, and other visual inputs — and take actions or make recommendations.

AI-backed computer vision is finding applications in industries ranging from manufacturing to automotive – and the market is expanding rapidly. It can perform the functions like inspection and identification in much less time. By the use of cameras, data, and algorithms, a system can be trained to inspect products or watch a production asset and analyze a very wide range of products or processes and detect invisible defects at a rate exceeding human capabilities.

Deep learning, an aspect of machine learning technology, trains machines by feeding a neural network with examples of labeled data. This is used to identify common patterns based on these examples and then convert it into a ‘math equation’ that mimics a human visual inspection classifies forthcoming information and performs tasks like differentiating parts, abnormalities, and texture.

Use Cases:

Automatic Counting: Computer Vision can be used for counting applications in industries where small parts are manufactured in large numbers like in metal parts, foods, pharmaceuticals, food, rubber pieces, wooden products, jewelry, etc.

Detect absence/presence: Computer vision can also detect the absence and presence of something such as date print, tags, brand logos, codes, stamps, etc, and automatically confirm the completeness of the product.

Sorting: Vision systems powered by AI algorithms can identify the right and defective product types by imaging them at high speeds. For example, separating pills in the pharma industry and segregating broken and damaged items in jewelry. This can be followed by sorting the identified items into chosen categories.

Surface Inspection: Computer Vision can identify surface anomalies for example scratches, dents, and pits accurately and at a high speed. Defects in some products like fabrics or automobile bodies are very small and undistinguishable, which can be detected only by monitoring the variation in intensity using deep learning algorithms.

Application:

Machine Vision is powering Industrial Automation. Using the latest 2D, 3D, and Artificial Intelligence solutions inspection systems are used across various industries like the pharmaceutical industry, automotive industry, printing and packaging industry, food and beverage industry, and textile industry. It offers huge benefits in eliminating human interventions and errors thus cutting down heavily on inspection cost and time.

There are startups working on this solution helping the above-mentioned as well as other sectors to greatly enhance their functioning through the acceptance and integration of new technologies into their existing systems. To know more about these and for collaboration and partnership opportunities please write to us at open-innovator@quotients.com

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Applied Innovation Enterprise Innovation

Blockchain based Anti-counterfeit solution

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Applied Innovation Enterprise Innovation

Blockchain based Anti-counterfeit solution

Surge in Online Shopping

The growth in online shopping has seen a spectacular increase in last few years. The retail e commerce sales worldwide is expected to climb a further 16.8% this year, to $4.921 trillion. This rapid acceleration of online buying was fueled by the pandemic, several countries like India, Brazil, Russia, and Argentina are witnessing significant expansion in e commerce.

Areas of Concern

Although online shopping has many factors of concern like data security, customer loyalty, unexpected price changes, economic uncertainty, inventory overload etc but counterfeited products sold online to unsuspecting consumers is one of the major concerns. Phone chargers, pharmaceuticals, and cosmetics are common items whose counterfeit products are often sold online at a considerably lower price.

Counterfeiting Impacting Brand Reputation

Counterfeiting is not only impacting not just sales and profits but also brand reputation. Brands and organizations strive to improve their brand identity and expanding their market share by constantly working on developing unique and innovative products but if the end consumers do not get access to these features because of infiltration of fake products results in the brands suffering massive losses.

There are also certain risks health risks associated with these counterfeits products, like electrocution or exposure to harmful chemicals that can cause serious damage to customers health. It has thus become pertinent that brands and organizations guarantee the authenticity of their products as the customers move towards more online purchases.

Groundbreaking Technology

To address this need of ensuring that authentic products reach the end consumer, Quotients has been working with different startups to come up with a solution. In this quest, we have come across a groundbreaking technology that employs AI, cryptography, and blockchain to add a cryptographic signature to the parcels. This signature can be used to verify the authenticity of products by a user’s mobile device through an app. Also, there is no need to change the existing package design and printing process by the client.

How it works

Blockchain is used with IoT and NFC tags to provides stakeholders with the visibility a product’s entire history. Blockchain creates immutable digital records, as a permanent ledger, any data stored stays for all the time. As each piece of information is encrypted individually to make a change rest of the nodes in the network need to agree which makes it impossible for the fraudsters and instances of fraud can be easily detected.

NFC, or near field communication, a popular wireless technology that allows transfer of data between two devices that are in close proximity, is a tamper-proof tag that is used to give each product a unique identifier.

As the product goes through the distribution chain, operators scan the NFC tag and upload information about its current state to the blockchain. Because all signatures are cryptographic, the system can provide complete security and transparency. The blockchain serves as a permanent data repository and while NFC tags prevents tampering with real objects.

Application

Application of anti counterfeit solutions depends primarily on the needs of industry and respective supply chains. In agriculture, this solution can be used to check illegally-produced goods that may pose a real risk to human health. The pharmaceutical industry is another popular target where false drugs are counterfeited as genuine by refilling empty containers or using fake labels. Here such solution can be used to combat such activities and save life and prevent harmful effects on patients.

These innovations also find application ensure premium beverages, food and tobacco products. With the globalization of food products and producers working in different areas better opportunities these solutions can help tracking and monitoring of supply chain and ensuring delivery of genuine products.

Conclusion

By ensuring the delivery of authentic product, the solution leads to brand loyalty and along with this it generates rich data on interactions in supply chain, checks cross border sales, helps in warranty management and in reducing carbon footprint by constant monitoring of supply chain.

To know more of such innovative solutions and evolving use cases in different domains along with collaboration and partnership opportunities please write us at open-innovator@quotients.com

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Innovator's Vista

How AI and Digital Technologies are Enhancing Policing

Team Adivid
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Innovator's Vista

How AI and Digital Technologies are Enhancing Policing

National Crime Records Bureau (NCRB) recently released its report on crime in India. The report states that a total of 66,01,285 cognizable crimes were registered in 2020. India reported an average of 80 murders daily in 2020, totaling 29,193 fatalities over the year. The crime records have thus been very unimpressive in the country and call for some strategic shifts in their policing.

Law Enforcement Agencies in developed countries utilize technology a lot and there has been a strong association between technology and their policing strategy. However, Police departments in India have traditionally been slow adopters of technology. It is primarily because of the highly prevalent bureaucratic practices and legacy systems still in use in police departments.

The impact of AI and related technologies on jobs, the economy, and the future of work have much been discussed but its usage in surveillance and predictive policing is an area that needs to be explored. AI can enable innovative police services by connecting police forces to citizens, building trust, and strengthening associations with communities. Smart solutions such as biometrics, facial recognition, smart cameras, and video surveillance systems coupled with analytics can greatly improve policing. A recent study suggests that smart technologies can reduce crime in cities by 30 to 40 percent and reduce response times for emergency services by 20 to 35 percent.

Adivid Technologies, based in Kota, Rajasthan is a startup working on this usecase. The company is providing police departments with such a platform called Third I. Having spent around five years working extensively with Police Departments, Adivid team has identified the existing challenges and opportunities.

Third I form Adivid

As per its experience, a majority of Police force is involved in reporting, paperwork, collecting data through multiple channels, information transferring, maintaining and retrieving it. Though it is an essential practice, it involves a lot of monotonous effort to compile hundreds of reports on a daily basis, ultimately leading to lesser number of personnel on field, for their core service to detect and prevent crimes.

Adivid team also delved deeper into 3 major aspect of policing which are as first, Process Automation, Second Enhanced Surveillance & Monitoring and Third Predictive Policing. The team found that in current scenario, Police Control Room dedicates 15-20 people to continuously coordinate and track the location of all the police station vehicles, beat marshals, Nirbhaya mobiles etc. over the wireless device on hourly basis and whenever any incident occurs, all the nearby vehicles are ordered to reach at the venue. The underlying problem with this method is unnecessary noise over wireless, no way to authenticate accurate location of vehicles or any other information, usage of rudimentary technologies with no way to customize.

With its Third I and other modules (20+), Adivid Technologies has worked towards solving the redundancies in regular day-to-day proceedings of police departments, the Information & Technological gaps present, and the lack of Data Centralization & Analytics which subsequently hamper administration’s ability to make real time informed decisions. Currently, more than 35000 Police Personnel across 14 cities and districts and 25000+ citizen users are using Third I and other modules developed by Adivid Technologies.

Powered by Automation, ML & AI, Third I enable Real-Time Information Management, Data Visualization and Improved Communication. Explaining the viability of Adivid solutions, Kovid Sawla, CEO of the company says,” All our modules require very minimal infrastructure (only smartphone & desktop mostly); e.g., instead of RFID, or tracking device, we provide alternative such as QR Codes, Mobile Apps etc. Thus, our solutions are viable and feasible for Police Departments.”

Adivid Technologies has incorporated multiple techniques and algorithms for various analytics, prediction and recommendation based on past crime data to generate various Charts, Statistics and assessment, generate heatmaps, identify Crime/Accident Hotspots, Patterns, Patrolling Recommendations, auto alerts system etc. which can greatly help decision makers to take directed and insightful action.

Adivid Technologies has given it’s solutions to more than 20+ large organizations including Maharashtra State Police, Mumbai Police, Delhi Police, Childline India etc.

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Innovator's Vista

Innovators’ Meet held at GAVS Campus in Chennai

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Innovator's Vista

Innovators’ Meet held at GAVS Campus in Chennai

This week, the third edition of Innovators’ Meet was held at GAVS Campus in Chennai.

The meet had presence of investors, industry partners, business professionals and innovators from IoT, AI, ML, Robotics and other deep tech domains. 

The theme of the foregathering was “Meet the Innovators and the Innovation,” and the motive was to bring the three I’s of the ecosystem. i.e. the Industry leaders, investors, and innovators together to stimulate technology-driven innovation (Artificial intelligence (AI) and the Internet of things (IoT) and unleash cocreation & entrepreneurial creativity.

The Startups, Humors, Vyli, Reprosci, Nautilus, Heaps.ai, Aikenist, RHEMOS, Arficus, Dnome, Medtel, Neodocs, TeraLumen, MedloTek Health, eVitalz, Mocero Health, Vulhunt, Inaluz, zMed, Luecine Rich Bio, Frinks, Tagbox, Neurostellar, Punar, Padmaseetha, pwens, and Nura, pitched their products before the investors. Most of the 25 startups that participated were from Healthtech industry, few startups were from Robotics and Cybersecurity.

Innovators’ Meet held at GAVS Campus in Chennai

The Meet hosted speakers such Mr. Chandra Mouleshwaran S from GAVS, Prof. Nandan; Robert Bosch Centre for Data Science and Artificial Intelligence, Mr. Chandramouli; Ex-CIO of Sankar Nethralaya, Mr. Balaji Upili;  CEO of GS Lab and GAVS, Mrs. Kushboo Goel; Senior Consultant Healthcare  and  Navaratan Kataria; Director, Startup Engagement Innovation & International partnerships, NASSCOM COE.

Attendees from startups included Ankur Jaiswal, Pooja H S,  T Udaya Raga Kiran, Apurva Sule, Ashwin Amrapuram, Aamod Wagh, Sandeep Singh, Divya Sriram, Dr Lalit Ranjan, Pratik Lodha, Dr Jyoti Dash, Sharmila Devadoss, Nanda Kumar, Palaniappan N, Akash, Deepak Gupta, Jayakanth, Kumar Aditya Agarwal, Adarsh Kumar, Dhanushya, Charmi, Gowri, Joseph Jegan, and Dr Tausif. 

Some of the notable attendees include Muthu Singaram; CEO, IIT Madras HTIC Incubator,  Akhilesh Agarwal from Pi Ventures, Adarsh B N from Derbi Foundation, Sanjay Selvan; speciale Invest and Himanshu Sikka from IPE Global.

There are plans to organise Innovators’ Meet every month at a different city so as to bring entrepreneurs and investors together in promoting emerging innovation and technologies that are transforming our future.

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