A Few Words

ABOUT US

Amakshar partners with organizations to develop technology strategies
and solutions that deliver tangible business value.

Our Strategy

Founded in 2015, Amakshar partners with organizations to develop technology strategies and solutions that deliver tangible business value.

An ISO 9001:2001 Certified Company

Work-life balance

People

We care about people and the role of work in their lives. We respect people as individuals, trusting them, supporting them, enabling them to achieve their aims in work and in life.

We help people develop their careers through planning, work, coaching and training.

We recognize everyone’s contribution to our success – our staff, our clients and our candidates. We encourage and reward achievement.

next in the world of work

Knowledge

We share our knowledge, our expertise and our resources, so that everyone understands what is important now and what’s happening next in the world of work – and knows how best to respond.

We actively listen and act upon this information to improve our relationships, solutions and services.

our entrepreneurial spirit

Innovation

Based on our understanding of the world of work, we actively pursue the development and adoption of the best practices worldwide. We lead in the world of work. We dare to innovate, to pioneer and to evolve.

We never accept the status quo. We constantly challenge the norm to find new and better ways of doing things.

We thrive on our entrepreneurial spirit and speed of response; taking risks, knowing that we will not always succeed, but never exposing our clients to risk.

Directors

SHREENIVASSAN KRISHNA RAO SHERTHALAI

PADMINI SUBRAMANIAH IYER

Blog

1 Salesforce:

When an engineer is required to work on the Salesforce Platform, a typical reaction is a mixture of dread and confusion as to what Salesforce actually is.

We have been working for a number of different clients on the platform since (2015) and witnessed firsthand how Salesforce has evolved over the years. I aim to show why Salesforce development can be agile, fast and even fun.

From Product Perspective
From a product perspective, Salesforce is a Customer Relationship Management solution that focuses on areas such as sales, support and marketing. From a developer perspective, Salesforce is all about data.

You create the data structures in the back end, control access to who has access to and can modify that data, build out business logic and integrations, and ensure that end users can interpret the data correctly through UI views, reporting and analytics. Obviously there are a lot more bells and whistles, but that’s the gist.

About Lightening
One of the classic criticisms of Salesforce was that its UI was tired and stuck in the late 2000s. The company responded by launching the Lightning Platform, an app-centric platform that provides typical business flows out-the-box while allowing for the possibility of heavy customization where necessary.

Development has switched from the traditional JSF model (Visualforce) that required multiple round trips to the server to a component-based, lightweight framework (Lightning) that has more in common with React and Angular. And the recent launch of Lightning Web Components makes this even better.

About Coding
Salesforce does a great job at abstracting away some of the boilerplate that’s required in other applications. For those who don’t have a coding background, they can get far just by using point-and-click tools.

However, when the application starts to mature and feature requests build up, this lack of separation of concerns will come back to bite us. Likewise, if attention not paid to governor limits, what works in your sandbox can suddenly start failing in a production org with more data. If we don’t get things right from the beginning, fixing it later requires far more time and resources.

Latest improvements

Earlier we have challenges to wait while Eclipse plugin complied and pushed the code. Even worse was when you were sharing an environment with other developers or admins and had to worry about conflicts or replicating those changes in version control.

That’s why SalesforceDX was such a massive improvement when it launched a couple of years ago. For the first time, we had a CI-friendly, CLI-driven development process that could spin up an environment in seconds, push the code from source control, and run unit tests and UI tests to ensure we haven’t broken anything with changes. Unlocked packages are a great way to modularize code and explicitly specify dependencies.

All in all, developing on the platform has been completely transformed for the better! we can proceed for survey without any hinderance.

2 Machine learning

Machine learning and artificial intelligence promise to be transformative technologies, but despite many businesses rushing to integrate machine learning, they still struggle with setting the proper foundation for these technologies: Controlling the quality and accuracy of their data.

In fact, a recent report found that nearly half of businesses do not have the technology in place to leverage their data effectively. That same report noted that obtaining accurate data was one of the largest challenges businesses face when it comes to data management.

New open source technologies now enable companies of any size to implement advanced analytics, but most companies fail at the basics of collecting and storing their data. It is the old “garbage-in, garbage-out” problem, but now poor data is driving machine learning or artificial intelligence projects.

Relevancy and timeliness of data is critical to effective application of machine learning for business outcomes, both in training and using the model. That said, the timeliness needed depends on the use case. It could be in seconds, minutes, hours or days.

Not all data needs to be refreshed in real time. Historically, data collection and curation have been batch-oriented. The increasing corporate appetite for real time analytics is changing that, and the abundance of elastic computing and storage is making the change possible.

Once the sole province of some companies, various proprietary and open source technologies are now available to help organizations of any size tackle these challenges. Data pipelines, asynchronous messaging, micro batches, stream processing, time series and concurrent model iterations are representative techniques that are being deployed successfully.

Apache Streamsets, Kafka, Spark, Time series databases, and Tensorflow are some of the foundational open source tools and technologies in the forefront of this shift to real time data collection and curation.

But no matter how sophisticated the technology, it still comes down to the relevance and timeliness of the data. It is the foundation of any digital transformation effort, and companies must take a disciplined and structured approach to managing their data if they want to properly leverage machine learning and AI. This involves:

Business objectives. What are the business goals and objectives? What data are relevant to achieving understanding if those goals are to be met? What level of timeliness is needed? Without understanding the answers to these questions, any effort to leverage data will likely fail to reach its full potential.

Data inventors. This includes structured data from internal transactional databases; external sources, such as credit scores, to augment the internal data; and then open source and internal, unstructured data on user behavior and social media. Many companies think their internal structured data is enough, but the unstructured and third-party data can be just as critical.

Data Storage. For many companies, important data is distributed in silos across the enterprise. Implementing a data lake will help pool these different data sources into a single view across the enterprise. In addition, groups across the enterprise will make decisions based on the same source of data, eliminating redundant and inconsistent actions.

Data visualization. Once the basics of data collection are established, then companies can move towards using the data for visualization, where reports and dashboards enable people to make decisions and take actions based on the data. This is the first step in providing meaningful interpretation of data in a form that is actionable.

Automated process. With clean, timely, and relevant data – and a solid understanding of how the data can be used to make decisions – it now becomes possible to forecast and predict in real time. Rather than having to conduct interpretation of the data manually, companies can let machines use the data to automate some of the decision making. Additionally, unsupervised machine learning also enables to uncover insights which previously have not been hypothesized.

Data governance. It’s important to establish policies and processes that maintain a high level of data consistency and cleanliness, otherwise companies will find the quality of their analytics will degrade as the quality of their data degrades. When that happens, it opens the door to a sub-optimal decision making process and has an adverse impact on clients

There is no silver bullet for this, nor should companies expect to implement a comprehensive data strategy in one fell swoop. Rather, this is a long, slow process, assessing where the company is today in its maturity curve and what it needs to do to get to the next step.

If there are (50+) data sources that ultimately need to be integrated, don’t try to incorporate all of them at the same time. Instead, focus on the two or three that will have the greatest impact on the business outcomes and work those through the full end-to-end process of data assessment, enrichment, visualization, and ultimately machine learning

3 Digital Transformation

Digital transformation is changing how companies operate from the ground up – every part of the organization is impacted. Whether it is building new products or services, creating new customer experiences, or using data to create new marketing strategies technology is the enabler in how they are delivered. Technology has been a core component of business for decades and digital transformation is the latest chapter in its evolution. Although companies may experience some headwinds, digital transformation will have a lasting benefit on a company’s competitiveness and bottom-line results.

Modern, advanced technologies today such as AI, deep learning, IOT, data sciences and so on are putting intense pressure on companies to adapt to the new mandates of digital transformation. However, balancing between the established and new ways of doing things can be a daunting task and become an inhibitor in moving forward. Companies are grappling with how to create a smooth transition plan and integrate a digital transformation without disruption to its business.

Challenges

While most companies have already realized that digital transformation will lead to a nimbler and more flexible way of delivering products and services in the marketplace, established behemoths face significant headwinds from legacy system constraints, having the right skills with current employees, or struggle with a culture of innovation. The latter of these tends to be more difficult because innovation challenges the status quo – why fix something that isn’t broken? Of course, startups don’t face these same issues because they can start with a clean page and build a technology ecosystem using the latest tools.

Initiatives

Therefore, despite tangible obstacles companies are choosing to forge ahead with their own digital transformation initiatives. Most realize they don’t have a choice. Either adapt or fall further behind the competition, lose market share, or worse, risk extinction. Failure to evolve will lead to higher operating costs, slower responsiveness, and losing talent to more “cutting edge” companies. Real risks exist for those companies unwilling or unable to adapt yet embracing change will also be fraught with its own set of challenges.

Change management

Taking on new platform, data, and upskilling initiatives while maintaining existing systems is not easy. Typically, companies will fail because they try to boil the ocean and are overwhelmed by an insurmountable influx of change. Trying to do too much at one time often leads to operational complexities and skyrocketing costs that become prohibitively expensive. The best approach to take in order to mitigate these risks are:

Develop a safe test environment designed to envision, prototype and deliver ideas with speed and agility through collaboration between technology and business teams.
Take an incremental approach for end-to-end development and cross functional integration creating a culture of friction-free innovation and decision making.
Evolve the digital enterprise ecosystem built on seamless integration of engineering, DevOps, and data analysis through the use of plug and play, reusable microservices and APIs.

This approach greatly increases the likelihood of success and provides for a smoother transition. It is a friction-free transition to a modern platform by eliminating the risks of a “do it all at the same time” approach. A second benefit is that components are reusable and therefore can be easily adopted in other parts of the enterprise. Third, it represents an orderly take down of inflexible legacy systems in stages with minimal disruption to the end customer.

Digital transformation will create a culture where speed, agility, and innovation thrive leading to higher employee satisfaction, improved customer experience, and better bottom line results.

4 Key Challenges

Digital Data Transformation in any organization is an ongoing journey. It is a process of evolution that needs to be addressed from multiple angles to reduce risk of failure and minimize resistance to change.

Most organizations today are grappling with a similar set of challenges – relevant skill shortage, undefined and ambiguous processes, and highly complex and inflexible legacy IT infrastructures. Although there’s a plethora of tools, technologies and methods available in the market, there is a need for organizations to identify business and technology partners who can highly contextualize and configure the end-to-end transformation process to help the organization get closer to the goals with speed and agility.

Amakshar works as an equal partner with organizations, sharing common goals of success. We approach this as a catalyst to change and not as a typical outsourcing vendor. Some of the key differentiators in our approach include:

Business Value

Working with business and technology teams we understand the strategic imperatives in order to help define phases that provide tangible business value as soon as possible.

Attracting Talent

We bring top talent of Architects and Analysts to partner with customer’s Product Owners and Architects in assessing and recommending a long-term architecture that it is resilient through changes of tools and evolution of strategy. Our experts are well versed in regional clustering of data not only from technology scaling perspective but also incorporate business, political and regulatory aspects. All individuals are highly effective collaborators exerting influence for rapid and right decision making in order to achieve speed and agility.

Cross Functional Teams

Cohesive scrum teams comprising skilled and energetic engineers are the staple of our delivery. They collaboratively work with customer’s business and technology teams to achieve iterative milestones every sprint (every two weeks).

Delivery Measures

DevOps and DataOps principles are applied to ensure continuous integration, security readiness and quality release ready code every two-week sprint. Four to five of these sprints are bundled in a release train delivering specific business outcomes defined by an MVP. We define and deliver series of agile MVP releases rather than big bang releases.

Learnings

Our way of working provides flexibility for incorporating learnings and changes as the MVPs are released and feedback received. Scrum teams can morph and flex as the program, business, and IT needs evolve.

Sakshat

We use our Amakshar Sakshat to assemble the top talent scrum teams. It is also used to foster engineering innovation throughout the project for the teams to come up with the best possible solves. Additionally, a ‘Run Ahead’ Innovation team works in parallel to uncover potential roadblocks and get the scrum teams ‘unstuck’ to remain optimally productive with maximum speed and agility.

Constant updates to stakholders

Our teams are proficient in jump starting with a set of accelerators that we bring to the table in addition to relevant open source technology recommendations. Additionally, the scrum teams strive to identify and create / consume reusable assets and scrutinize new open source packages as the implementation progresses. This approach creates long-term benefits for our customers by increasing speed, quality, and compliance.

Preplanning

Our architects and engineers are always on the lookout for adversities and challenges as is the case in a complex system. We articulate various mitigating options laying out business and technical trade-offs without compromising architecture and security. Migration challenges are addressed using parallel pro-forma runs, rollback strategies, A/B throttling.

Checking parameters

Business and technical KPIs are core to our delivery model. These range from scrum team level metrics such as story points, burn rate to business level KPIs in terms of number of customer onboarding abandonments, and response time for a business report aggregating multiple data points.

Our mission is to put customers in complete control of their technology transformation and empower them to go to market quickly. If we would like to partner with us as digital talent or a business under transformation, do drop us a note here!

5 Artificial Intelligence

We have more AI around us than we even know! Start-ups today, have brought in new applications and innovations in every sector. Looking at the investment figures, AI will be the top most applied sectors according to a leading research firm

Given the impact, it’s interesting to study how quickly and aggressively AI has been making its impact on the impervious Financial Services industry felt worldwide.

It was just about a decade ago, when high-speed trading was introduced making the markets more efficient and fast-paced. Today, although the AI technology itself is in a native stage, we can see its application in basic services like scrapping paper work, streamlining operations, customer interaction or even complicated tasks like hedging risks. AI is no doubt helping the financial services industry streamline itself faster and with deep insights, tap into a growing customer market. AI will no doubt be the most important factor for driving revenue growth and profitability.

So here’s a quick study on some key areas of application that we can see:

Artificial Intelligence is a combination of cognition, manipulation and interaction. Cognition – Ability to perceive, understand, plan and navigate; Manipulation – Precise control and ability to manipulate objects in the given environment & Interaction – The ability to learn from and collaborate with humans. The current capabilities for AI is not limited to but include deep learning, predictive and prescriptive analytics, text mining, natural language generation, machine learning systems, and recommendation engines.

Network Security

One of the greatest areas of concern in the financial services industry is network security. There have been multiple cases of hacking and data-theft that have occurred in recent times, making it a top priority to be watchful about and take steps to prevent the same.

In order to make sure they reap the best benefits of this technology, some of the biggest banks and financial institutions around the world have started investing in AI- Bank of America Corporation made a bold push into AI technology. JP Morgan Chase introduced a Contract Intelligence (COiN) platform designed to analyse legal documents and extract important data points and clauses, HDFC Bank has developed an AI-based chat bot, “Eva” and many more such examples exist in the sector.

AI is already being used to spot abnormal activities and behaviour to detect rogue trading and market abuse, ensure compliance with principles and guidelines and set up accountability in case of fraud detection. Companies are also using AI to utilize its powerful image recognition capabilities. Through image-tech, companies can scan through hundreds of thousands of pages of contracts, documents and data to find the necessary piece of information being sought after. This helps financial firms find lapses in their own systems and create penetration-tested solutions.

AI also enables assessing risk management to also be automated. Human error may create incorrect statistics and trend models, but AI provides a much higher accuracy ratio. Also assessment of credit worthiness, as well as quantifying the risk that an investment carries can be calculated on a real time basis.

Credit card/ fraud detection, anti-money laundering, investigation optimization, transaction surveillance, and regulatory mapping are just a few of the varied applications for the financial services industry.

Chat bots

We can chat with a chat-bot and not realize that we are communicating with an AI bot. That’s the power of communication in the AI space. With some of the largest AI industry participants investing heavily into chat-bots, the financial services industry has seen long-strides in quality communications. With regards to scale, AI bots can simultaneously communicate with millions of customers at a time, saving cost and resources for these firms.

Financial services companies also have incentives to implement these AI chat bots as communication gets more complex for many service representatives. As language barriers and service wait-times increase, AI offers a more robust and scalable solution in chat-bots.

There are implications that go globally, when it comes to AI-based communication. However, in the financial services industry that picture is much clearer with sensitive information being shared over communication. That’s why it is safer to opt for AI-based communication for many of these firms. There is also a data analysis component to these communications, making it easier for customer service managers to review customer satisfaction in real-time.

Efficient Process

AI has made processes streamlined in the financial services industry. It has created new avenues of growth for financial firms, banking institutions and fund managers by making it much easier to process paperwork, documents, and large quantities of information. Even something as preliminary as hiring the right people, can be optimized via AI’s advanced mapping and process-efficient technologies.

When it comes to processes within customer relations, vendor mapping, and fund transactions, AI can analyse that data and produce good and reliable insights for managers. Through increased process efficiencies, and advanced data management systems, AI aids not only in enabling better decision making but also enabling them to be much faster and in real time. This increase in efficiencies makes it easier for them to manage various portfolios and design new products as well as new features for existing products.

AI is able to run millions of data points through rigorous testing and analyses to find the loopholes in their security systems. It can handle billions of documents within hours, and find patterns, trends and analysis that a human eye may miss. AI can also run simulations to find the best approach and manage the data better on a 99% uptime.

Increasing efficiencies and delayed decision making has allowed many of the top financial services companies to invest in newer technologies and become increasingly competitive. This has opened up the market for top-tiered customer-oriented services and innovation thereby providing greater flexibility in services provided.