FAQ
Frequently Asked Questions
With whatever we can. Preparing cost analysis, establishing team roles, creating roadmaps. Defining business goals, key business values, target users, main features, prioritising. We can find you similar projects that are already active on the market to benchmark against. We can assist you with creating user personas, mock-ups, user stories, time frame, establishing project status and preparing project estimation. We'll be happy to outline project specification, prepare wireframes, details concerning integrations with external services, API documentation, required features list. In terms of project development we prepare server-side architecture, front end, back end, graphic and UX design, and create back-office consoles. We are also happy to advise our customers in terms of budgeting, scheduling, risk management, and business model creation.
We thoroughly monitor our developers and control how much time they spend on each task down to the exact minute. We use Harvest software and Redmine to track time spent on your project.
One of the key applications of AI is to combine these technologies — speech recognition, natural language understanding, dialogue management and so on — to create Intelligent Assistants. Intelligent Assistants are interactive systems that can communicate naturally with humans and assist them in accessing information and completing tasks. Most of the Intelligent Assistants that people are familiar with today are consumer facing and somewhat general in purpose. Siri, Cortana, and Google Now, for example, are Intelligent Assistants that make it easier for a consumer using a phone, tablet, or other device to get things done. The majority of what you can do through these assistants was already possible through the graphical interface of the device, but the Intelligent Assistant enables a single point of entry to perform a broad range of different tasks. Much the same argument applies to enterprise care. Customers need to interact with a business to fulfill numerous different types of requests such as account access, billing, sales, troubleshooting, and so on. An enterprise Intelligent Assistant powered by AI technologies can provide a simple and consistent point of entry to access numerous different services that get lost in a complex graphical interface or the structured voice menus that are typical of interactive voice response systems (IVRs). An enterprise Intelligent Assistant could be something you deploy in the voice/telephony channel, or through web chat, text, mobile applications, or to support social media customer care. The ideal is to have all of these channels supported by the same underlying Conversational AI in order to offer the customer a seamless omnichannel experience. For example, when a customer reaches out through the mobile channel, the Conversational AI should be aware of any recent customer service calls in the telephony channel: “So sorry you were having trouble with your internet service yesterday, what can I help you with today?”.
This question cannot be easily answered absolutely. Based on the infrastructure on the market the lower threshold is at about 1 to 3 terabytes. But using Big Data technologies can be sensible for smaller databases as well, for example if complex mathematiccal or statistical analyses are run against a database. Netezza offers about 200 built in functions and computer languages like Revolution R or Phyton which can be used in such cases.
From cloud companies like Amazon to healthcare companies to financial firms, it seems as if everyone is developing a strategy to use big data. For example, every mobile phone user has a monthly bill which catalogs every call and every text; processing the sheer volume of that data can be challenging. Software logs, remote sensing technologies, information-sensing mobile devices all pose a challenge in terms of the volumes of data created. The size of Big Data can be relative to the size of the enterprise. For some, it may be hundreds of gigabytes, for others, tens or hundreds of terabytes to cause consideration.
Public cloud services are breaking down into three broad categories: software-as-a-service, infrastructure-as-a-service, and platform-as-a-service. SaaS is well known and consists of software applications delivered over the Web. Infrastructure-as-a-service refers to remotely accessible server and storage capacity, while platform-as-a-service is a compute-and-software platform that lets developers build and deploy Web applications on a hosted infrastructure.
Yes, but that doesn't mean it will be easy. Services have popped up to move applications from one cloud platform to another (such as from Amazon to GoGrid) and from internal data centers to the cloud. But going forward, cloud vendors will have to adopt standards-based technologies in order to ensure true interoperability, according to several industry groups. The recently released "Open Cloud Manifesto" supports interoperability of data and applications, while the Open Cloud Consortium is promoting open frameworks that will let clouds operated by different entities work seamlessly together. The goal is to move applications from one cloud to another without having to rewrite them.
Expertise across various technologies End to end project responsibility SLA defined support to ensure high availability of data 24X7 monitoring for data security and recovery Proactive problem management Efficient data processing for informed decision making
To put it simply, Robotic Process Automation (RPA) is the technology that allows organizations to use software robots to perform specific tasks in an automated way. Using a combination of user interface interaction, descriptor technologies, and cognitive processes, The RPA BOTs can be used to mimic or emulate selected tasks within an overall business or IT process. These may include manipulating data, passing data to and from different applications, triggering responses, or executing transactions. The bots can work through one or more software applications.