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Analytics As a Service for the Supply Chain and Logistics Industry

By | Blog

Logistics and supply chains have been the backbone of the world economy for years. The recent digitalization has changed its processes, methodologies and legacy systems to new generation high tech capabilities. With massive digital transformation comes massive data. The supply chain and logistics service providers must benefit from the analytics, from this data to improve services and stay ahead of the competition.

Analytics can be used at each level of the supply chain and logistics. The main thing that makes analytics very usable is the amount of data generated by any logistics and supply chain operation. The decision at each stage is crucial as wrong decisions can hamper the whole chain. Using analytics embedded with AI and ML can help companies in different ways to boost productivity and profits. Below are some of the areas where Celbridge Science has helped its clients achieve higher productivity and profits with the power of analytics as a service.

1. End To End Process Analytics

From sourcing to the last mile logistics, Analytics can help get better insights about end-to-end processes. With data interpretation of real-time data and predictive analytics, one can get accurate predictions related to maintenance of systems, vehicle fleet, and unplanned requirements. Also, Big Data analytics can help mitigate operational risks using diagnostic analytics and provide possible solutions with the help of prescriptive analytics before time.

2. Demand Forecasting

Big data analytics with predictive analytics based on the AI and ML models helps demand forecasting for supply chains. Celbridge has helped its client with its customized data analytics services to forecast demand related to sourcing, inventory and logistics management and more. Our AI programs convert this data into actionable insights, further enhancing decision-makers’ ability to make forecasting decisions. With better demand forecasting, costs are optimized, resource wastage is reduced, which results in higher profits.

3. Optimization

With loads of GPS and routing data, companies can optimize transportation routes with the help of real-time analytics. This helps to reduce transportation distance, saving fuel costs. Our Analytics Solutions are well crafted for providing automated insights for optimization. With defined KPIs for departments like warehousing, transportation, supplier management, and areas like reverse logistics, which is a costly and time-consuming area. For every purpose, we provide all the optimization analytics insights. Hence, you focus on the core business and get rid of high costs, faulty and time-consuming processes with the help of decision-making, aiding optimization.

The future of logistics is digital and many of the current manual processes will be automated with time and generate tons of data that needs to be used wisely. To be ready for this future, supply chain and logistics companies need to incorporate data analytics as a service in their end-to-end operations.

Please check out at Celbridge Science. To connect with us at [email protected] or call us anytime on 636-594-2242.

Hyper Automation in the Healthcare Industry

By | Blog

Many organizations that were falling short of digitized methods struggled during the sudden onset of an epidemic. The number of people who needed proper treatment and healthcare assistance was humongous and was unexpected too. This is one of the scenarios that emphasize having an advanced automation technology in place that can handle increased demand in the healthcare industry and pharmaceutical companies and provide top-notch healthcare to every proper enrollment.

Good-to-know facts about Hyper automation technology

Hyper automation stands at the first position on Gartner’s Top 10 Strategic Technology Trends for 2020. Well, Gartner refers to it by the term Hyper automation, but what exactly does it mean? Hyper automation encompasses advanced automation technologies like RPA, Artificial Intelligence, Machine learning, etc., to discover, design, analyze, monitor, measure, and automate various tools to enhance human augmentation. To cope with the superior intelligence human beings possess, hyper-automation is curated with a combination of specific practical tools like RPA, process management, case management, IBPMS, etc.

Need for hyper-automation in the healthcare industry

In today’s world, where we deal with a pandemic, speedy and effective measures are essential in drug production, handling vast volumes of data, detecting diseases, and performing administrative tasks. Advanced automating software and tools can lead to more significant innovations and developments in less time and is highly cost-effective than outdated computing methodologies. Voice biometrics technology, AI chatbots, Big data analytics, etc., are just a few hyper-automated tools that eliminate some of the severe challenges faced by the medical industry.
A recent study shows that in India, approximately 5.2 million medical errors occur every year. Hyper Automation involves the implementation of robotic process automation (RPA) to get rid of these errors that are often caused by human interventions.

On a positive note, studies show that “The Hyperautomation Market is estimated to grow at a rate of 18.2% in the forecast period and is expected to reach a value of USD 22.84 Billion by 2027.” as per a journal published by Emergen research.

Significant challenges faced by the healthcare industry

The healthcare sector is in constant need of hyper-automation technologies in the following areas:-

  1. Effectively storing and regulating vast volumes of data
  2. To have strong coordination among the advanced tools, implemented
  3. Data privacy and security systems that could be hampered by human interference

Here are some of the critical benefits of hyper-automation in the healthcare industry:-

  1. Efficiently handles the tedious administrative tasks with greater accuracy
  2. Invoice and insurance claims are settled promptly, without delay
  3. Prescriptions, discharge summaries, and follow-ups can be automated
  4. Audit processes that involve days and months of backend works can be minimized
  5. Fully computerized systems leave no room for human errors
  6. Exceeds in the quality of service and patient experience


As the population increases tremendously, the need for technologies that simplify sophisticated medical tasks goes up, and hyper-automation becomes an ultimate solution to the operational difficulties in the healthcare industry. In the upcoming years, machines tend to take over the industry, providing the most proper data management, billing severe, detecting diseases and performing enrollment and appointment tracking that eases human life. We are bound to have the best medical experiences ever.

Please check out at Celbridge Science. To connect with us at [email protected] or call us anytime on 636-594-2242.

Ai As A Service

Where Should AI Be Used & Why?

By | Blog

AI is a savior for businesses. Artificial intelligence has algorithms that mimic human behavior or human thinking that doubles the computing capacity of any machine in terms of real-world relevance. It further enhances the speed, precision and effectiveness of human efforts involved in business processes when AI is extended to Machine Learning and ML is extended to Deep Learning.

Every industry from Pharma, Lifesciences, Automation, Manufacturing to Supply Chain have numerous departments, processes, products, etc. that are data-dependent AI make them more efficient and productive through the following ways:

  1. AI for Data Analytics

It becomes difficult to get useful insights from the analyzed data at scale and quickly with huge and complex data sets. Because what’s the use of insight if it’s late or incorrect due to manual errors. The whole data analytics process can be taken to the next level with machine learning models and AI. It can handle complex data sets and solve any business problem through the insights it develops. The constant analysis that machine learning models with AI do, help to bring scalability, pace and accuracy to data analytics in real-time.

  1. AI for Business Decision Making

A business decision needs descriptions of the problems, their root cause and different solutions that can solve that business challenge. A data analytics approach alone can help solve problems at a very small scale and when the complexity of the problem is low. But as the complexity increases, the probable solutions become complex, and the system needs to take into account all the scenarios, the root causes and compute it at a very large complex scale to provide better decision-making to the businesses. Celbridge science has helped its clients across various industries make better decisions with its AI and ML capabilities.

For example: In a Supply Chain that functions at a very massive scale and faces any problem, our pre-trained Machine learning models can come to the rescue as they are trained to sense, understand, analyze the data continuously and provide solutions in real-time and at pace. Similarly, the AI can itself predict any similar problems that can come in the future and alert the stakeholders in advance due to its pattern recognition and predictive reasoning capabilities that come to it through its machine learning models.

  1. AI for Business Process Optimization

Apart from crucial decision-making, AI can optimize the business processes throughout. Each department can be made more efficient and productive by saving costs through error reduction and process optimization. For example: In Manufacturing and Logistics, AI can optimize inventory management, dispatching, and workforce planning. In Lifesciences, the power of AI and ML helps researchers and clinicians speed up the research process and save millions in dollars.

Similarly, businesses and governments can also optimize their services by custom designing AI & ML algorithms that cater to various government programs and departments.

As businesses want to maximize return on their investments, they have made technologies, processes, and human workforce. AI can enhance the business capabilities to the next level and can make them future-proof.Celbridge Science offers AIaaS for organizations that seek to benefit from AI technologies without a massive investment in People, Process & Technology. Please check out at Celbridge Science. To connect, email us at [email protected] or call us anytime on 1636-594-2242.

How Big Data Analytics is improving customer experience?

By | Blog

Today’s market is a buyer’s market where the customer is GOD. Every company is trying to be customer-centric by providing a rich customer experience throughout the customer journey. Each step in the customer journey generates large chunks of data with volume, variety, and velocity. This big data is being analyzed and processed to further the customer experience in every aspect.

To improve customer experience big data is analyzed to know more about the customer behavior, motivations, and touchpoints across the buying and retention process. Here are the following areas where big data analytics is improving customer experience.

1. Personalized Recommendations

With the help of big data analytics, companies can build solutions that provide a more personalized customer experience. Predictive analytics methods use historical customer data for analysis, based on that recommendation engines can be built which deliver personalized & accurate recommendations to the customer during the buying journey. This helps to reduce the bounce rate and increases customer engagement.

2. Security and Privacy

The increased flow of data has resulted in various privacy and security issues. Customers are now more aware of their data being kept secure. Big data is a boon but handling it poorly can increase data threats. Big data analytics solutions through AI and Machine Learning helps companies to analyze and classify cyber threats based on historical data and patterns and it helps to build predictive solutions for future security threats. It can be said if the data of your customer is secured, your business is secured.

3. Customer Retention and Support

Whenever a customer faces problems after a purchase, the time and efficiency of supporting the customer determine the chances of retaining the customer and building word-of-mouth publicity. It’s the fact that you can’t guess what problems customers may face and what will be the best solution. But big data analytics can be a savior. It helps you to predict what your customers may face and helps you build solutions to retain them.

For example: Through diagnostic big data analytics, using past events the root causes for any problems can be figured out. Similarly, through churn analysis companies can pinpoint what has gone wrong and how it can be prevented in the future. This helps to prevent customers from facing the problem that can arise and keeps you ahead of the competition.

To be a brand and not just a name, every interaction point with a customer matter and it builds the perception in the customer’s mind. The use of big data analytics solutions helps to better understand customers, their behaviors and helps companies to be perceived as “Best” in the ocean of competition.

To discover the potential of Big Data Analytics in AI, please check out at Celbridge Science. To connect with us at [email protected] or call us anytime on 636-594-2242.

CIO Magazine Machine Learning Company of the Year 2021

By | News

Celbridge Science has been featured in CIO Applications Magazine as The Machine Learning Company of The Year 2021.

Celbridge is proud of being recognized for the immense hard work and the results we have delivered over time for various industries.The feature in the magazine presents a platform for the company to showcase its stories and expertise in AI and Analytics.

The digital version of the magazine is now available for you to read by clicking the button below.

Read the magazine

Big Data Analytics in Pharma Industry

By | Blog

In the current world, where COVID 19 has showcased the rate of transmission of a virus worldwide, there is a clear ask for innovative technology solutions to speed up the processing of drug discoveries. As pharma companies raced towards minimizing timelines for vaccine discovery, which usually takes 5-10 years, it has now been done within a couple of years. What changed exactly? The answer is Data Analytics.

Data has always been the pedestal for drug discoveries to identify the sequences and theories about the efficiency of the treatment. But as more and more factors are introduced in the picture and huge amounts of data are being generated, to an extent where it is humanly impossible to process it within smaller time frames, technology comes into play. The term is coined as ‘‘big data’’, which means large and complex data sets that are difficult to process using traditional database technology. But big data is not just about the size or amount of data, and it comprises 4 dimensions, Volume, Velocity, Veracity, and Variety.

The Four Dimensions of Big Data

Big data unlocks the true potential for data analytics, from accelerating drug discoveries to understanding patient trends and behavior. Big data unlocks the true potential for data analytics, from accelerating drug discoveries to understanding patient trends and behavior.

Here are 4 ways that pharma companies use Data Analytics to drive innovation.

1. Drug Discovery and Development

Applying predictive analytics to the search parameters should help them hone in on the relevant information and also get insight into which avenues are likely to yield the best results. Pharmaceutical Companies like AstraZeneca, Celgene, Bayer, Janssen Research and development, Sanofi, and Memorial Sloan Kettering Cancer Center, started a data-sharing initiative under the name Project Data Sphere to share previous research data on cancer to help experts in their research on treatments against the disease.

2. Targeting specific population segments

Scrutinizing data sets from various sources allows researchers to identify patterns that provide them with key feedback on how infections perform concerning specific population segments.

3. Customized patient care

Companies can leverage data from devices, which provides insights into the current patient behavior through analytics models. According to the formulated insights, the medical practitioner or companies can use this information to design services targeted to different demographics and curate treatments.

4. Streamlining Clinical Trials

Big data can assist the appropriate candidates for clinical trials by analyzing demographics and historical data, remote patient monitoring, due diligence of the previous trails track record, and even predicting outcomes of the trial. In addition, big data analytics can further narrow the patient funnel by considering more factors such as genetic information that help streamline clinical trials and drive down costs.

As the pharma companies are looking to drive maximum ROI before their patents expire, data analytics which substantially reduces timelines plays a key role in driving value. Not only does technology derive enhanced results, but it also provides a competitive advantage.

Artificial Intelligence Trends in Biotechnology

By | Blog

‘Data is the new oil’, we can hear this term being thrown around. Generating large amounts of data was never a problem. The difficulty was in making that data organized and available in an actionable format – in shorter time duration. However, the real challenge has been driving meaningful insights from that data – which is being addressed with the help of AI and Machine Learning.

The application of AI has moved past the general applications as we can see it making groundbreaking advances in the pharmaceutical and healthcare industry.

1. Drug Discovery

The COVID pandemic highlighted the need for faster drug development procedures, and the pharma companies delivered. In a global setting where disease transmission is quick, the need to expedite drug discovery procedures is important. Pharma and Biotech companies are leveraging the abilities of AI for rapid drug development. Once the genes and proteins associated with diseases are identified, the cognitive ability of AI allows thousands of chemicals to be screened to recognize potential drugs that affect the disease. The vaccine or drug development that would previously take on an average of 5-10 years has been reduced to 2-3 years with the help of AI and Data Analytics.

2. Digital Biomarkers

Digital biomarkers are making it easier to analyze data and predict or influence healthcare outcomes. As patient data is being increasingly harvested digitally, the opportunities to implement artificial intelligence (AI), especially machine learning, are increasing exponentially. Recently, the FDA issued the emergency use of the first machine learning-based COVID-19 non-diagnostic screening device called the Tiger Tech COVID Plus Monitor. The device recognizes certain biomarkers that may be indicative of SARS-CoV-2 infection as well as other hypercoagulable conditions or hyper-inflammatory states in asymptomatic individuals over the age of 5.

3. Gene Editing

Gene editing has made it increasingly possible to alter DNA sequences in living organisms in turn customizing the gene expression. The main applications of AI in gene editing are the identification of harmful genes and the treatment of diseases. AI reduces concerns regarding human errors with gene editing and is recognized to improve the procedure accuracy and yield better results.

While there are drawbacks to gene editing, AI and Machine Learning have tremendous potential to make gene editing more efficient and accurate. This will influence pharmacogenomics, genetic screening tools for newborns, enhancements to agriculture, and more.

4. Precision Medicine

he role of AI in Precision Medicine development is promising a new future of personalized healthcare. AI-enabled reinforcement learning allows sophisticated computation to generate insights, and empower clinicians to make well-informed decisions. On the other hand ‘precision therapy’ can be coined as a term as well, where we can see AI and ML algorithms being trained according to certain common health conditions, which enable caregivers to outline a therapy for patients suffering from mental health issues based on insights.

Data is critical for drawing insights, and technology is bridging the gap between this data and actionable intel. While the above-given trends are only the tip of the iceberg, AI & ML are expected to dramatically improve health outcomes – whether in the research of new drugs or the delivery of clinical care.

Artificial Intelligence in Bioinformatics

By | Blog

Alan McCarthy, also known as the father of Artificial Intelligence coined the term back in 1955. Since then AI has seen steady growth until recent years. In the past few years, businesses have started incorporating AI more and more in their operations for ease of functioning. We can see it being used in a myriad of business activities such as building personalized customer experiences, cyber-security, predictive service and maintenance, and smart assistants, to name a few. Machine Learning and Deep Learning, which form the subsets of Artificial Intelligence, have also opened several avenues in the research community.

Let’s Focus On Bioinformatics – Connoting our niche focus area of Bioinformatics and its aspect of Clinical Research and Drug Development, let’s look at AI applications that have reshaped the sector.

AI, Computing & COVID 19

Integration of AI models into supercomputers is enabling them to run complex calculations in epidemiology, bioinformatics, and molecular modeling. We have seen IBM leverage this with IBM Summit to enable the experts at Oak Ridge National Laboratory and the University of Tennessee to screen 8,000 compounds to bind the main spike protein of COVID and come up with 77 possible drug compounds which could be experimentally tested. These procedures, if carried out in the traditional ways, could have taken months or even years.

Machine Learning And Bio-sciences

Machine Learning can be leveraged in decrypting large datasets such as DNA sequencing, Protein classification, and Gene expression.

For example, AI ingestion allows easy analysis of voluminous patient data for DNA sequencing, to predict and personalize medicines. Researchers are also extracting protein data with advanced techniques in Natural Language Processing (NLP). Neural network applications such as Bidirectional Long Short-Term Memory (BLSTM) is helping in the study of critical features of protein sequences. Deep learning tools such as ProDec BLSTM, DeepSea, and Basset are proving to be useful.

Data Collation & Analysis For Drug

Data collection is a crucial part of drug development, which can be done with the help of AI tools. Natural Language Processing (NLP) uses machine learning to analyze large data sets and recognize biologically applicable terms. It helps scrutinize the copious amount of data and identify previously missed patterns or relationships concerned with a disease and its drugs. The ability of NLP-mined data can be enhanced by compiling results from multiple sources. We can see pharmaceutical companies identifying trends in diseases and compiling data sets that can garner better and quicker results in drug discovery.

Enterprises Are Looking For Activation Partners

While the future holds a lot of scope for advances in the bioinformatics industry, with AI, ML, and Analytics, stakeholders still need experienced implementation partners. What also matters is their industry know-how and the ability to streamline the research timelines and in many ways make it cost-efficient as well.

DNA analysis, new drug development, artificial organ generation are picking up the pace. Here, the right AI solution provider can be a game-changer providing insights that would have taken a lot of years or to generate if done the traditional way.

If you’re looking to develop perfect drug discovery, correct analytical research, and accurate methods for integrating medical science bioinformatics, please check out at Celbridge Science. To connect with us at [email protected] or call us anytime on 1636-594-2242.

Advances In Using AI In Drug Development

By | Blog

Artificial Intelligence has transformed biomedical research in terms of Drug Discovery and Design. It has made the process more efficient and has paved a path for the discovery and development of inexpensive and efficient drugs and rational structure-based drug design, with reduced time and costs. Pharmaceutical companies are using Machine Learning Models and Advanced AI Insights to analyze and use complex datasets as a solution to the increased cost of new drug development.

Drug discovery using AI

On average, a new drug coming to the market has to go through phases that involve testing the drug on sample groups for years, and with costs involved up to billions. It also involves the standard approaches of testing how different molecules of the drug composition interact with different targets. The preclinical development phase of drug discovery tests potential drug targets on animal samples.

During this phase, trials can be accelerated with minimal errors using AI models, enabling researchers to easily find the permutations and combinations of the drug interaction with the animal test sample model. AI can also facilitate participant monitoring during trials, generating a larger set of data more quickly, and aid in participant retention by personalizing the trial experience. With advancements in AI, the automatic feature extraction ability of deep learning supports models with better accuracy and delivers more reliable results. Secondly, deep learning models’ generative ability can be utilized to speed up the drug discovery process with better prediction capability and a low failure rate.

Drug design using AI

Drug design is the process of finding new medicinal drugs based on the knowledge of biological targets. How strongly a molecule can bind to a target is the main goal of a drug design phase. With the computational machine learning models, the computational affinity of a compound for synthesis can be predicted, and hence only one compound needs to be synthesized saving enormous time and cost.
Artificial intelligence (AI) proves very efficient in various fields of drug design that include virtual screening, activity scoring, quantitative structure-activity relationship (QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion, and toxicity (ADME/T) properties.

Many pharmaceutical companies are riding the wave of Drug Design and Discovery using AI. For example, AstraZeneca using AI is researching new drugs on chronic kidney diseases. Bristol-Myers Squibb (BMS) has deployed machine learning models in finding data patterns that are associated with CYP450 inhibitors, these patterns help to reduce adverse side effects and interactions of the drug-in-development.

GSK Pharma has its in-house AI unit which has developed a machine-learning algorithm to identify protein crystals. Not only the big pharma players but the mid-sized pharmaceutical companies and startups are also taking advantage of these advancements of AI in drug discovery and design.

The next decade will see a transition from traditional drug design and discovery methodologies to highly intelligent AI-based algorithms that will deal with the new scale and complexity of the data used in drug design and discovery.

please check out at Celbridge Science. To connect with us at [email protected] or call us anytime on 636-594-2242.

A Learning Path Towards Data Science Future

By | Blog

As many industries are utilizing analytical data to improve business practices, big data and data science career opportunities are exploding in parallel. The U.S. Bureau of Labor Statistics (BLS) reports that the demand for data science skills will drive a 27.9% rise in employment by 2026 and data science-related career is estimated to grow 11% from 2014 to 2024. The good news is that there are several paths that a data science career can take. The challenge is to understand how these careers differ and what kinds of skill sets are required individually for them.

The question now is, where and how to start?

J.P. Morgan –
“The first step towards getting somewhere is to decide you’re not going to stay where you are.”

But have you ever wondered what Data Science is? “Data Science is used by computing professionals having skills for collecting, shaping, storing, managing and analyzing data for organizations to leverage accurate data-driven decisions generating values”. Almost every interaction with technology includes data. Amazon purchases, Facebook feed, Netflix recommendations, and even the facial recognition required to sign in to your phone.

      1. First Step – The 3WWhere can your data science skills take your career? What job title is offered? Which path is right for you? A Data Scientist has many hats in his/her workplace. Not only are Data Scientists responsible for business analytics, but they are also involved in building data products along with developing visualizations and ML algorithms. Finding answers to these questions should be the first step in your data science journey. We can’t cover every potential job title, but we can surely find some leading roles in the data science universe and how they differ with the progression of a career in the field if you’re starting.
      2. Second StepChoosing a Tool / Programming Language. The most probably question asked by the beginners. Straightforward answer: Any of the mainstream tool/languages. Tools are just a means for implementation; understanding the concept puts you a step ahead. But then also to give you an idea, start with the simplest or the most familiar or GUI based tools. Also, you should look into attributes like availability, cost, ease of learning, advancements in tools, support for data handling, etc. Currently, the two programming languages used widely are Python and R. In case if you are familiar with these languages you can prefer to skip this step.
      3. Third Step – Communicate with databases. If data science is the art, a database is an artist. Data is the heart of data science. Whenever you work on a data science project, you need to have data that can be analyzed, visualized to build a valid database. To do so and communicate with a database, you need to speak its language where SQL plays its part. A Structured Query Language used to communicate with a database. If you could design a simple database, then this will take you to the next level.
      4. Fourth Step – Understanding the Math. Math is the core of data science, without math no data processing can be taken forward. You need to understand the basics of probability theory, statistics, and linear algebra to comprehend data science. You not only need to understand how it works but also you need to know how and when to use it. Here are some useful courses you can enroll in and get your math solved. Data Science Math SkillsMathematics for Data Science Specialization.
      5. Fifth Step – Collaboration & Interaction. Controlling versions across systems to track changes in the source code during the development and testing process. Git is used as a version control system to harmonize the group of programmers, developers, and testers. It allows you to coordinate among teams without needing to interact much with the command line via GitHub or GitLab.
      6. Sixth Step – Exploring Machine Learning. The fun part starts here. Relate what you have learned so far, discover and implement different Python and R packages using SciPy and NumPy. Here you’ll not only get to experience how data science works but also the impact it has on our daily lives.
      7. Seventh Step – Deep Learning. It is a subset of machine learning concerned with artificial neural networks, inspired by the structure and function of the human brain to learn from large amounts of data. Deep learning clarifies its actual usage when it comes to your daily life via Virtual assistants, Chatbots and service bots, Facial recognition, personalized shopping, and entertainment and to solve complex problems without human intervention.
        Now you are almost at the finish line.
      8. Eighth Step- Natural language Processing (NPL). A branch of artificial intelligence that helps computers understands, interpret, and manipulate human language and processes via software. NPL includes Speech Recognition, Text To Speech Application, Sentiment Analysis, and Virtual Assistance like Google, Siri, Alexa, and other different kinds of conversation bots.

So here we reached our destination. You being a data scientist always need to be in a continuous stage of development and learning. This field is rapidly growing with new techniques, algorithms and research. The most important thing about this journey to know is that you can do it. You just need to dedicate yourself enough time, efforts and be open-minded to achieve your goals.