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Crafsol is a leading Robotic Process Automation Company in India

Have you thought about deploying your human resources differently post Covid-19?

Let’s suppose you get about 100 business inquiries a day via email, and your CRM database requires constant updating.
Now, have you hired a person to copy-paste data from email to CRM database?

Imagine a Robotic Process Automation software parsing data from e-mail and automatically updating the CRM database within seconds.

What is Robotic Process Automation (RPA)?

The RPA system will mimic almost all human interactions with a digital system. It will log into applications, parse data from documents and emails and write it into relevant database. While humans have around 60% efficiency with a chance of errors, RPA tools will offer 100% accurate and far more efficient results at the cost of a year’s salary of the concerned employee.

Meanwhile, that employee could now focus on turning those email leads into actual sale.

COVID-19 and RPA?

Covid situation has restricted human movement and social distancing has constrained human contact.
Maintaining business continuity despite these limitations means improving the efficiency while working with fewer employees.

RPA can log into your systems, create and organise folders and move files as per pre-defined rules, and also copy-paste data as instructed.

If repetitive tasks are automated, employees could focus on more sophisticated and challenges, adding more value to the business than before.

Why implement RPA?

Up to 90% automation of repetitive process
Processing time reduced by 80%
Zero mistakes and 100% accuracy
Highly scalable and adaptable to changing business environment.

Crafsol’s Robotic Process Automation for SAP

Automation of repetitive business processes is reshaping the role of employees in the industry amidst the Covid-19 crisis. Crafsol has tested and proven Robotic Process Automation solutions that guarantee quick RoI and progressively reduced cost of operations with time.

Crafsol has developed RPA solutions for several processes including:

Accounts receivables/payable
Resume scanning and classifications
Enuqiry logging automation
Service request automation and allocation
Field data scanning and automation

Visit Website – https://crafsol.com/robotic-process-automation-2/

If you believe that embracing RPA tools could put your business on a growth trajectory, we should get in touch.
Write to use at connect@crafsol.com.

4 Reasons to implement facial recognition technology right now

As this article series outlines, risk associated with human touch by the Covid 19 pandemic has underlined the urgent need for touch-free systems at workplace. Businesses are looking to adopt facial recognition system for attendance at workplaces. A facial recognition software is advanced enough today to recognize faces despite a difference in illumination and factors such as ageing. Today the technology is used in a number of sectors ranging from law enforcement and security to retail.

How facial recognition technology works

1- First, the facial recognition software captures your face as a picture or video.

2- Facial recognition algorithm “sees” faces as data. The software maps the geometry of your face. It focuses primarily on the space between your eyes and the distance between your forehead and chin. Next, it identifies landmarks that distinguish your face from others. Now, the software has built your facial signature.

3-Lastly,this ;signature’ of you is compared to a database to identify your face with accuracy.

Why use facial recognition technology?

Security: The technology has proved to be an effective law enforcement tool for governments. Additionally, the technology cannot be hacked, which makes it suitable for locking and unlocking devices such as computers and smartphones.
Faster processing: It takes just one second for face recognition to process. This has proved beneficial for companies as it enables them to quickly verify a person. This, combined with the foolproof nature of the technology makes it a great way for applications such as seamless payments and logging attendance at workplace.
Improved services: Smooth integration offered by facial recognition systems could enable various sectors to improve their services. For instance, facial recognition could replace OTP in banking. In retail, shopping centers that “know” their customers could offer a more seamless shopping experience. This is also true of airports.
Contact-less system: Face recognition provides a non-intrusive way of verifying identities. As the world recovers from Covid-19 pandemic, touch-less systems are poised to become more relevant and mainstream.

Despite its many advantages and successful early adoption by many sectors, some businesses have been reluctant to implement facial recognition technology. High costs of implementation and doubts about the system’s ability to detect changes such as illumination and aging process are points of concern.

Crafsol has been focused on developing solutions that bring cutting edge technologies at affordable price points. Facial recognition technologies developed by us are being used across sectors such as banking, retail, and others. In order to know more about how Crafsol’s facial recognition technology might help your business become better and more reliable, reach out to us at: .

Understanding the approaches to facial recognition technology

As the previous article illustrates, the need for touch-free identity authentication is more serious than ever. As questions surround the finger-touch bio-metric machines, businesses are looking at using facial recognition for attendance. In order to build a better understanding of this technology, let us take a look at different approaches used in building a good facial recognition software.

Approaches to facial recognition technology

Feature-based approach
In this approach, facial recognition software relies on mapping significant features such as eyes, nose etc to generate primary input data for face recognition. This approach works in three stages: in the first stage, the intensity of the image is calculated in terms of features. In the second stage, the image is represented as a series of graphs. Here, the nodes in the graph represent features information while links in the graph represent relation between the features. In the third and final stage, this data is matched with a database to identify the face correctly.

Holistic approach
In the holistic approach to face recognition the whole head is considered for input. While feature-based approach considers face as a flat plain, holistic approach creates a model of the entire head. This technology sees the whole head as a cylindrical volume. Further on, it takes into account the possibility of movement. This approach too marks a number of feature points on the face and then adjusts these points according to the way the face rotates.

Hybrid approach
The hybrid approach to facial recognition technology is simply the combination of the two other approaches in face recognition. This approach helps scientists overcome the shortcomings of other individual approaches. According to some studies, this approach can work in two stages called training and classification.

In the training stage, facial recognition algorithm extracts various feature points from faces using a combination of technologies such as Principle Component Analysis (PCA) and Independent Component Analysis (ICA). After this, these extracted features are trained in parallel and partitioned into different face classes using Back-propagation neural networks (BPNNs) In the classification stage, the images are classified into different face classes using a combination of various algorithms.

While these are the broader approaches to face recognition, there are other specific techniques to design algorithms. These techniques include Eigenface, Neural network, Fisherfaces, Elastic bunch graph matching, and Geomatrical feature matching.

At present, facial recognition techniques used by social media companies are capable of identifying faces with 97% accuracy. However, new challenges now loom on the horizon for facial recognition technology.

The new challenge for facial recognition algorithms

However, with masks becoming ubiquitous, identifying faces wearing masks is the new challenge for facial recognition tech. recently, a company in China has claimed that it has resolved the issue.

There seem to be two approaches to solving this problem. First, making algorithms adapt at guessing what a face could look like without wearing mask. Second, deleting a part of the picture, so that the algorithm is designed to recognize half the face itself accurately. Chinese researchers working on this issue claim that the first approach requires loading into the system, around 6 million pictures of people without masks, and then some pictures without masks.

Despite the new challenges, there is recognition within the industry that the time for use of face recognition technology at workplace has arrived. The recent Covid-19 pandemic has underlined the need for face recognition to replace biometric touch pads and other systems that involve touch.

Crafsol has been working to make advanced facial recognition technology available at affordable prices to businesses across India. Early adaptation of this technology for functions such as logging employee attendance could ensure a hassle-free and safe workplace for all. If you want to explore how this technology could help your business, reach out to us at…..

Is facial recognition system for attendance finally here ahead of its time?

Given how Covid 19 pandemic has rendered human touch a potential health threat, the use of bio-metric machines at workplaces has come under scanner.

Consider this: You have a steel plant which has thousands of workers. The plant has now restarted after the pandemic. the bio-metric machine is somewhat of a risk considering the number of workers. However, a reliable alternative is needed to check impostors entering the plant in place of actual workers.

Could facial recognition system be an alternative to bio-metrics ?

As facial recognition technology adoption by mobile companies demonstrates, facial recognition is emerging as a hands-free way to gain access. However, facial recognition algorithms must still overcome some significant challenges to become mainstream.

Challenges before facial recognition technology

Costs: Market research by consulting giant Gartner outlines how cost hinders the adoption of facial recognition technology. Advanced technology that is used by the likes of government agencies is high in cost as well. On the other hand, cheaper options in this field often do not live up to the expectations of customers. This leads to many customers avoiding buying facial recognition technology.

Lack of standards: There is no set of specific standards which would enable customers to evaluate the solutions that vendors have to offer. Hence, decision-making becomes difficult. Additionally, vendors might throw in terms such as deep learning, artificial intelligence etc and further confuse the customer.

Lack of awareness: Facial recognition technology is often seen to intrude into privacy. At this point, an educated debate with the customers around the issues of privacy often becomes difficult.

Despite the challenges, early adoption of facial recognition technology has already begun…

Despite these challenges, facial recognition softwares are emerging as a useful tool. Rajiv Gandhi International Airport at Hyderabad recently became the first airport in India to initiate facial recognition. After enrolling into the programme, passengers can now pass seamlessly through all touchpoints at the airport for further departures.

Elsewhere, medical colleges in Japan are already using facial recognition technology to log attendance in classes.

In the face of recent circumstances, businesses across India are considering deploying facial recognition for logging attendance. Resolving the affordability factor of facial recognition technology has been a key priority at Crafsol. The company has a decade of experience in providing smart facial recognition systems to sectors such as banking and retail. To know more about how Crafsol’s facial recognition technology can help you build a better and safer workplace, reach out to us at:

https://crafsol.com/contact-us/
Or give us a call at +91-20 65427680

Artificial Intelligence (AI) in Healthcare

Dr. AI – How it is impacting healthcare? and Covid-19 in China?

Technological change is rapidly sweeping the world today. On one hand there is the digitalisation of many services, and on the other hand, there is Artificial Intelligence which is redefining various aspects of life. From education to production and logistics, AI is transforming the way we work, play and live. The field of medicine has not remained untouched by technology. Digitalization is already reshaping the age-old doctor-patient relationship, and now, Artificial Intelligence is poised to bring tectonic shifts in medicine and healthcare.

More than 32,000 potential cases of Covid-19 have been detected by using AI software to expedite diagnosis.

Chinese hospitals are deploying artificial intelligence to detect visual signs of the pneumonia associated with Covid-19 on images from lung CT scans. As of February, around 34 hospitals in China were using software to diagnose Covid-19 cases. The software used data fro over 5000 previous diagnosis to successfully detect Coroavirus differences in CT scans with an accuracy of 96% within 20 seconds.

Here’s a look at how AI is changing medicine

Digital Diagnosis

With systematic integration of AI, Deep Learning, and chat-bots, consultation has really advanced to the next level. AI enabled chat-bots are able to ask relevant questions and help doctors with correct and detailed information to provide accurate diagnosis in the shortest possible time. With Deep Learning processes even complicated and unstructured inputs can be transformed into accurate information and utilised to achieve a quick resolution. On a lighter note, AI can save doctors from tasks such as writing notes and reading scans, allowing them more time to connect with the patient.

Medicine Selection

After consultation, AI further helps doctors to quickly and efficiently select medicine to prescribe. It could offer pointers about whether the medicine is available near the patient’s location. AI could help quickly diagnose and treat common childhood conditions. It’s expertise in recognising patterns could also help predict whether an individual could develop Alzheimer’s. Similarly, AI could play a vital role in the treatment of potentially terminal illnesses such as cancer.

Robotic Operators

The use of tools such as robotic arms for surgeries goes to show how robotics has impacted medicine. The entry of AI into the operation theatre could further improve the efficiency and accuracy of surgeries. It could help doctors choose the right tools, and provide real-time data and risk-assessment while the surgery is in progress

Follow-up

Many a time, the follow-up visit to the doctor becomes more of a hassle for the patient than the actual treatment itself. Doctors find it difficult to devote enough time to follow-up and patients struggle to get an appointment. AI-enable chat-bots could help resolve many such issues with follow-up. As data about treatment is stored digitally and could be accessed easily, AI-enabled bots could instantly solve the patient’s problem. This could help the follow-up checkups hassle-free for both patients as well as doctors.

These are just a few areas of opportunity where AI could revolutionize medicine. There is a lot of ground AI expected to cover over a period of time. More accuracy is to be achieved while ensuring operational efficiency. Being a veteran player in the healthcare sector, Crafsol is committed to working towards the betterment of the healthcare industry in every way. The company’s leadership has deep expertise in healthcare and pharmaceutical domains and aims to play a pivotal role in driving innovation in this field.

Integrating Machine Learning to your SAP HANA Platform

Artificial Intelligence is one of biggest drivers of today’s digital world. AI has already become the part of our everyday life.

Machine Learning is a subset of AI that empowers the system to automatically learn and improve from experience without being explicitly programmed. The success of Machine Learning depends on the quantum of data. It requires large sets of data augmented with a whole range of algorithms.

As technology advances, enterprises are migrating or have already moved to SAP ERPs, so for better success it is best to sync Machine Learning with your SAP database.

Running Machine Learning algorithms where your data resides—in the database—can help reduce latency and alleviate other delays that arise when copying data to another server.

The tango between Machine Learning and SAP

SAP HANA Predictive Analytics

SAP Hana’s Predictive Analytics provides machine learning capabilities through in-memory database. SAP Hana predictive analytics library (part of AFL) provides data analysts and developers with automated, wizard-driven machine-learning capabilities and it can create algorithms to build predictive models and provide better controls for the data scientist.

PAL also has several algorithms that ML learns and continuously updates for dynamic predictions.

Open-Source Machine Learning

SAP Hana also facilitates open source machine learning frameworks, R integration is one of them. R is an open-source programming language designed for statistical computing and advanced analysis. Users can write R code in HANA and then mix and match those programs with PAL Algorithms.

Main advantage here is that businesses can utilize the power of SAP HANA and user-expertise for scalability as well as performance through R scripts

HANA inbuilt Machine Learning Integration

SAP’s Extended Machine Learning Library (EML) provides machine learning inference on data at rest or in motion. ML models can be built in tens or flow using python packages to build complex deep learning integration.

Deep learning brings in ability to solve complex inspection and build algorithms, Identifies every defect outside if the set tolerance and processes prediction at very fast pace.

Crafsol Technology Solutions has in-depth experience in SAP implementation and has also worked on some of the challenging Machine Learning projects, If you wish to take best advantages of ML from SAP platform, get in touch with our experts.

How SMEs are benefiting from Data in ‘Big’ way?

Data has a pivotal role to play in today’s technologically advancing world. Data is deriving valuable insights and speeding up decision making for businesses. Efficient use of big data can thus change the way businesses operate.

Estimates suggest that by the end of 2020, there will more than 40 times more data as compared 2015. Multinationals and large conglomerates are already taking advantages of big data. However, small and medium enterprise are also making efforts to utilize the potential of big data.

Big Data and SMEs

Due to their infrastructure, processes and systems, large organisations are better placed to generate and utilize big data to gain better insights. However, it is a struggle for the SMEs. As the volume of their business is small, the volume of data they generate also tends to be small. Big Data can still offer a number of benefits to SMEs. It can help SME make their businesses more agile, analyze and predict in consumer’s behavior and plan their product accordingly.

What could SMEs do using Big Data?

Analyze markets and forecast its trends
Predict price fluctuations
Define buyer personas and profile customers for marketing
Run and manage marketing campaigns
Improve and develop product design

Challenges in choosing Big Data

When it comes to SMEs, biggest challenge is uncertainty about return on investment. Many a times, SME go for average IT infrastructure which reduces their efficiency. Even in cases where SMEs can invest they may not know how to choose the right technology. In such matters, two points need to be considered.

Selecting the right tools
Selecting the right implementation partner.

A knowledgeable and experienced implementation partner plays key role in the success of Big Data. A partner who has experience in leading the projects at scale of SME can understand the challenges faced by SMEs and create meaningful difference with desired end-results for small businesses.

Talking about tools, open source tools like Hadoop, or Spark can act as boon to SMEs. Hadoop can process large sets of structured as well as unstructured in nature. processes in real-time for creating delightful insights for SMEs. Crafsol helps businesses to create easy and efficient digital insights engines to gain an competitive edge.

Get in touch with our expert team to avail our services.

Machine Learning & AI for Cyber Security

In last decade we have seen great development is Machine Learning, Artificial Intelligence and its impact in everyday lift of Human ecosystem.

AI and ML typically helps machines or systems to automatically learn and improve from experience without being explicitly programmed. ML can build algorithms on data that is classified or unclassified, with partial information or no information been provided.

Cyber security also is one of the area where AI and ML can play pivotal role. lets take an example, if your computer or machine is made able to to decide what is good for it and what is bad for it while accessing, this way one machine itself finds new Malware or Anomalies when it faces for the first time.

AI and ML can be used to build this capability, it can identify and safeguard the systems from ever changing cyber world. Traditional security tools may run short to point out the control the cyber attacks.

Building the system that can fight threats

Malware or Cyber attacks always evolve themselves, hacker constantly build upon their previous works, adding new features, upgrading abilities to crack current systems that are blocking its attacks.
AI coupled with ML learns from the information available from previous attacks (data) and build algorithms to predict, identify and control future ones which can be similar category or style

Layered Defence

Basic principle of Cyber security is creating a defence that layered and is in depth. Keeping everything updated such as constant updates, scanning computers every time and everything user accesses is one thing.

Quickly scanning the content user is trying to access, identifying if it is good or bad is one element that every cyber security tool has in build right now, however time involved in doing it is quite time consuming.

this is exactly AI and ML can make the difference, A properly-trained AI/ML model can deliver decision on good or bad files in just few milliseconds.

Resource optimization

Applying machine learning and artificial intelligence to improve cyber security saves an organisation a considerable amount of time and money that would have otherwise been spent by cyber security experts.

Machine Learning quickly excess large pool of data instantly and learn and analyze from it. Systems generate lot of alerts and unautomated attention can buildup lot of work security team. ML can learn from historical data and create resolution on its own.

Artificial intelligence and machine learning is all set to become one of the front runner of next-generation security, enabling elevated degrees of cyber security.

Crafsol ML and AI services builds highly advanced and customized solutions. We are focused on improvisation of the algorithms after the machines have undergone some experience in identifying objects based on defined attributes which makes our approach unique.

EHS – Disruptive Technologies that changing the way it operates!

Awareness about Environment, Health and Safety has been incremental over last couple of decades and lot has been invested proving EHS issues. Tracking injuries, transportation mishaps, leading indicators, and related environmental incidents are not easy.

Identifying the reason behind occurrence of particular incident, developing preventive measure is one of the key challenge for the companies.

However, the age we live in is poised to take that transformation several notches up, Huge investments in technologies like AI, ML and Big Data are being made by companies to achieve operational efficiency, these can very well important role in EHS as well.

In this blog, we will look at how ML, AI and Big data helps companies to improve their EHS department and which areas it can positively impact.

Artificial Intelligence:

AI is practically automating many tasks, and execute these task in effective and reliable model. This has released EHS executive to more valuable tasks.
Alongside, advanced AI systems are capable of integrating company’s data monitoring system, understand regulatory norms, and generate suggestions, prediction and actions to take for better decision making.

Big Data:

EHS has huge data, large amount of data is generated from sensors, control and monitoring systems and interpreting the same was big challenge in earlier days. With Big Data, companies get deeper insights and understanding of their employees, their behavior, operations, incident occurrence patterns, etc based on which EHS departments within a company can take preventive action, optimize reporting to improve performance of EHS department.

Machine Learning:

Machine Learning is everywhere in the businesses now, playing an important role all over different departments, in EHS as well, ML has great role to play for example: Industrial Hygiene, ML can offer a practical method to process data to build a predictive modelling resource, delivering a significant improvement in efficiency.

Let’s look at some of the factors where these technologies can be of great help

Occupational Safety

Big data & machine learning offers lot of insights when it comes ti identifying several risk factors connected to accidents, mishaps and incidents. Based intensity of risk a corrective actions can be planned. This proactive outlook helps companies to improve safety as well as productivity.

Risks analysis

Predictive analytics coupled by artificial intelligence, ML helps companies in identifying areas of risks, typical behavioral issues, typicality in mishaps etc. Utilizing predictive technologies enables management to be more proactive and opportunistic in the way they manage their operations. ML provides insights on the past data, and build realistic assumptions about when is it likely to happen.

Waste Management

Managing wastes at factories is a laborious job, ML helps to identify when a high quantity of waste is generated during the production day. Alongside, it also helps manufacturing units to visualize the location, quality, type of waste that will be generated

These just few benefits listed here, while the list can long and long like predictive maintenance, EHS Audits etc. At Crafsol we strongly believe these disruptive technologies are changing the way EHS can operate.

Interested in learning more? Get in touch with our experts to know more.

Optimising Customer Support Services with Machine Learning

Enterprises have been using Machines to improve efficiency and productivity for long, though it may be advance machinery or shifting to robotics or cobots to further improvement, the need to increase efficiency is consistent. Machine Learning is the next thing which is helping enterprises to use the machines to improve and work more efficiently.

Customer Support is undoubtedly one of the biggest cost centers in modern-day operations, for big or medium scale businesses.

And Customer Support is also considered as one of the most complex parts as well.
Well does it really need to be one? the straight answer is ‘No“

Machine Learning is already set to bring in a major role in resolving challenges in a customer support function.

What is Machine Learning (ML)

ML is one of the parts of artificial intelligence (AI), it is a set of techniques that provides systems or computers an ability to automatically learn without being actually programmed. It can read the data whether it may be structured or unstructured and discover insights.

How does it help in Customer Support

When it comes to Customer support ML provides a higher level of convenience to the customers and efficiency to the support staff.
CS means a lot of data, of which the major part is an unstructured one. This data that is gathered through everyday conversion and communication which contains deep insights, ML when used intelligently can be helpful in understanding the customers, their thinking, wishes and so on.

How is Machine Learning used in the CS environment?

Here are quick top picks

Understanding the Intent

Most of the time your agent does not know why is customer contacting you or where probably he is asking for help.

Machine Learning can help CS agents predict the same. Machine learning collects data about a customer’s previous browsing, geo-events, contact pattern, and other online behaviors. That allows agents to plan and prioritize the customers need. In a way your agent provides personalized services to the connecting customer, assuring elevated user experience.

Timesaving – a win-win situation

Well, what is mostly observed by the researchers, people prefer getting in touch with customer support is through chat or messaging app option. that’s where chatbot comes in the picture.

While, chatbots are something that is equally disliked by people, when used intelligently it can have a greater impact. for instance, if a chatbot can extract what exactly the customer is looking from the text types, further tag it correctly, to direct it right customer support specialist, can bring in a win-win situation for both.

it saves time for the customer, saves time for an agent, reaches the right SME and improves the overall experience

Predictive Service

With IoT, companies are now able to practically track their devices even while the customer is using it. For example, a Smart Machine connected to the internet can send back a signal to the manufacturer when a fault or unusual operation takes place. The manufacturer can get back to owning the company to inform arising issues with the required solution.

Virtual Assistance

Virtual assistance is helping customers during their journey, providing insights to customers as well as feeding the analytic program at the back-end for future reference.

ML can help a enterprises to virtually assist the customer right from buying stage to finding out resolution when he is facing any issue. ML can also help in pushing a product which customer has checked or liked based on his online behavior, it can trigger alerts or updates to the customers.

While these are only a few things listed here, there is a long list where ML can help in optimizing Customer support.
To list a few – customer data capture, classification, proactive emailing, perform root-cause analysis of repetitive instances, reduce interaction time.
Crafsol with its ML and AI services has strong experience of building custom solutions for enterprises.

Get in touch to know more how we can help you in your fasten your customer support services.

https://www.zendesk.com/blog/machine-learning-used-customer-service/
https://freshdesk.com/customer-support/machine-learning-optimizing-customer-support-blog/
https://www.forbes.com/sites/forbestechcouncil/2019/04/24/using-machine-learning-to-improve-customer-service/#298ccf985892
https://www.cognizant.com/perspectives/how-machine-learning-can-optimize-customer-support