Step 5: Will my tech stack scale? Choosing a tech stack is a commitment that’s not easily reversed, so concerns related to future circumstances like scalability and performance are common. However, trying to address these concerns at the MVP stage would be premature. Many performance issues are a product of application design, not technology choice. […]
4. Consider the landscape of tools in your industry One of the keys to a successful MVP is reducing time to market. Capitalizing on existing tools can dramatically reduce the scope of work and effort required to launch your product. When selecting a programming language and other back-end technologies, first identify the best open source […]
3. Who is going to build this thing? Shortage of engineering talent is a major challenge for early stage startups. As a result, the availability of engineering team members will play a role in determining the right tech stack for your company. Certain tech stacks attract different types of candidates and will influence your company […]
An MVP is all about getting a product into the hands of your customers quickly and learning from their feedback. Importantly, it also serves as a foundation for your engineering team to build on as your company grows. Before any code gets written, you will need to select the tech stack that will power your application.
An MVP is all about getting a product into the hands of your customers quickly and learning from their feedback. Importantly, it also serves as a foundation for your engineering team to build on as your company grows. Before any code gets written, you will need to select the tech stack that will power your application.
An MVP is all about getting a product into the hands of your customers quickly and learning from their feedback. Importantly, it also serves as a foundation for your engineering team to build on as your company grows. Before any code gets written, you will need to select the tech stack that will power your application.
The following post is an interview with SVSG CTO and Financial Services Practice Manager Carter Smyth. Carter is an executive with over 25 years experience building technology teams to solve enterprise challenges in the financial services industry. He has extensive experience leading software development teams, business process reengineering, and global expansion projects. Carter joined SVSG in March 2017.
There’s no longer a debate as to whether companies should invest in machine learning (ML); rather, the question is, “Do you have a valid reason not to invest in ML now?”
In the last post, we highlighted the disruption that chatbot technologies are poised to make in call centers. To recap, we are seeing the trend that Generation X and Y have now shown a preference for text-based communication over voice. This results in consumers increasingly wanting to talk with brands via messaging platforms like Whatsapp and Facebook Messenger. Simultaneously, there has been an explosion of conversational A.I. technology tools and frameworks in which natural language processing can be used to automate customer support inquiries. As the last installment discussed, this trend provides a compelling opportunity for companies to drastically reduce the costs of running their call centers.
In 2014, Facebook acquired WhatsApp for $19 billion. That astronomical number set off waves of speculation as to what value Facebook could possibly see in a company with just 55 employees and roughly $20 million in revenue, although it had 500 million users. At last week’s F8 conference, that vision became a lot clearer, and it’s big. Chatbots will cause a near-term disruption in how businesses interact with consumers, and a long term paradigm shift in how people will interact with machines.