Software Engineering Insights

Vulnerabilities in software applications are common. 85% of security incidents happen at the application layer. With the right security measures, the operations and development team can discover these vulnerabilities and fix them at an early stage in an SDLC. 

Organizations irrespective of industry accumulate data across categories such as purchase history, customer contacts, lead journey statistics, buyer behavior, and so on. The most preferred mechanism to track and leverage this data today is through Customer Relationship Management (CRM) software such as Salesforce CRM. What makes Salesforce a highly viable CRM solution is the suite of low code application development tools it provides.

There are 400 million active websites on the internet and all these websites are susceptible to vulnerabilities of one kind or the other. Even the microscale misconfigurations, such as improper validation, disclosure of server versions, and using vulnerable software libraries can lead to drastic security issues. To avoid the consequences, there is Application Security Testing. 

An organization’s data is its most essential resource and its day-to-day operations produce data in abundance. But to derive actionable insights that help make data-driven decisions, it is important to remove inefficiencies in the way the data is consumed and visualized. Business Intelligence, or BI, is a group of services that empower businesses to leverage their data for driving enterprise excellence.

Salesforce has been knocking its competition in the CRM world. Despite the existence of several industry-specific CRM solutions, Salesforce has been standing strong. One of the prime reasons for its success is its flexible integration services. 

Automation is driving the decline of mundane and repetitive tasks. And this holds absolutely true for a software development cycle. 

There is a provision to automate almost every aspect of a software development cycle- coding, testing, and even deployment.

Over the last half-decade, Artificial Intelligence solutions has made incredible advances in pivotal areas such as vision, video generation, linguistic processing, trend prediction, and so on. While AI attempts to make giant leaps toward matching human intelligence, industry insiders forecast the emergence of Artificial General Intelligence (AGI) as the next obvious step.

The global technology ecosystem has an insatiable demand for AI today but without the right training data and efficient AI model development, this domain cannot be utilized to its full potential. With fully actualized AI models that arise from high-quality build lifecycles, continuous innovation in AI fields such as Natural Language Processing (NLP) or predictive analytics can be ensured.

If you’re leading the marketing campaigns of an organization, you could relate to this! The prospect data received from website forms, surveys, web browsing, a list of event attendees, or advertising is not enough to create opportunities and convert a lead. 

Taking a business to a global scale involves navigation, not just through untraversed markets but also across language-based hurdles. Natural Language Processing (NLP) offers the utility of Machine Translation, wherein a source language is converted to a target language automatically with significant accuracy. It is the most viable solution to a fast and effective translation process without human intervention.