Build Smart Apps

Increase efficiency in your organization by building apps that automate labor intensive processes that are intuitive for users to use and that put information on their users’ fingertips on any device anytime anywhere.

  • Great User Experience
  • Time Saving App design
  • Top Notch Code
  • Quality
  • Enterprise Grade App Security
  • Timely Delivery
  • Continuous Improvement
  • Satisfaction Guarantee

Our App Development Process

When it comes to developing a new app design, the process can simply begin with a hand-drawn sketch. This helps to get a visual representation of the app before any coding or design work begins. But how do you go about creating a hand-drawn sketch for an app? Here are the steps you need to follow:

  • We start by getting to know you and your team.
  • We try to quickly go to design and prototyping phase.
  • As soon as an interactive prototype is finalized, we start developing the features.
  • Because our designs are exactly like the app functions, we find that development and deployment phases go smoothly.
  • Once the product goes through all the sprints and is mature, it goes in maintenance mode.

People

We start with people side of the business and develop an understanding of your team structure, user roles, and team skill sets.

Process

Next we focus on business processes that are target of any custom development and how the choices we make will affect workflow.

Technology

Then, we look at your existing technology assets that will include on-premise, cloud and hybrid. We will evaluate and help you prioritize projects.

Technology Platforms and Tools We Work With

Hosting

  • Microsoft Azure
  • Amazon AWS
  • On-Premise

Databases

  • Microsoft SQL Server
  • MySQL
  • PostgreSQL

Frontend

  • React Native
  • Angular / JavaScript
  • ASP.Net

Backend

  • Microsoft .Net / C#
  • Python / PHP
  • Web Services / APIs

DevOps

  • Azure DevOps
  • GitHub
  • BitBucket

Tools

  • FIGMA
  • Visual Studio
  • Jira

Mobile Apps

Most software is built in a way that allows the software company to sell to a large number of users. Features are built for mass appeal. That process leaves a lot of gaps and screens are full of options that are rarely used. We start with what you need and only develop features that your users need.

Why consider a web app?

  • iOS App Development: When you develop a native iOS app, you can create a custom experience for your users. Developing an iOS app can help promote your business and drive traffic to your website or storefront.
  • Android App Development: Android has become the world’s most popular mobile operating system (OS) with over 2 billion active users. The Android SDK comes with a rich library that is supported by a large community..
  • Windows App Development: If you’re a business that’s already using Microsoft 365 platform and need to provide a better mobile user experience to your users, Microsoft tools make it easy to build powerful apps with much less total cost of ownership.
  • Hybrid App Development: Hybrid app development has emerged as a popular solution for businesses looking to build mobile apps. Some companies are hesitant to develop a hybrid app because they believe that it won’t perform as well as a native app. However, there are several benefits of hybrid app development that can outweigh the negatives.

FAQs

The list may include Educational Apps, Lifestyle Apps, Social Media Apps, B2B Apps and Gaming Apps. The list is long.

We have an Excel template that we use to quickly give you an idea about how much to budget for an app. Please ask us to walk you through that sheet.

It can take a few months to a year depending on the scope.

Web Apps

Web apps are designed to help you get the most out of your online experience by providing a wide range of features. Some advantages of web apps include increased efficiency, cost savings, and a single codebase for each of feature enhancement and total cost of ownership. In most situations, responsive web apps provide a great user experience.

Why consider a web app?

  • Speed: If you need to build something fast, consider starting with a web app. Let users experience it. As the adoption rate increases, evaluate if a hybrid or native app is needed or not.
  • Reach: Web apps do not need to be published on any app store. All users need is a URL. Anyone with access to a browser will be able to use your web app.
  • Changes: It’s easy to make changes in a web app since the tech stack has fewer layers.
  • Costs: Since there are fewer tech components involved, the cost to build a web app can be much lower.
  • Device Support: We build apps that work across any device with a browser.
  • Upgrades: The underlying tech stack for a web app is a web server, a database and some middleware framework. The whole stack can be upgraded with very little additional effort in codebase changes.

FAQs

Web Apps have more backend and front end features than a typical website which may have some landing pages and forms.

If users mainly use desktops, web apps can be built with robust UIs including grids, searches with advanced filtering and sorting features.

There is one additional step that can turn a web site or a web app to a PWA. 

Software Integrations

Many companies are finding that ‘do it all’ business systems have their own issues and replacing them with “best of breed systems” is much better. This creates the challenge of syncing data between multiple systems. Without integration, a company ends up with disconnected islands of data and critical information. Integration not only reduces data entry and unified reporting, but also creates new insights and opportunities for the business. We believe integration is a cost effective way to connect various systems together for long term business success.

  • How many integration points are required?
  • What objects or records need to be integrated?
  • What are the trigger points for each integration in your business process?
  • What platforms and programming languages are involved across all trigger points?
  • If there are multiple teams, how are they going to interact and remove obstacles for each other?
  • Do systems to be integrated have any concurrency limits that might become a bottleneck?
  • Would this integration require a two-way sync or one-way sync?

FAQs

Software integration involves connecting two or more software programs to sync information based on pre-defined triggers. Software integrations help businesses automate tasks and improve efficiency. By connecting two or more software applications, businesses can streamline processes and reduce the need for manual data entry. For example, if you have an ecommerce store, you may have integrations with credit card companies or shipping companies.

The list is long. Some examples are Zappier, Dell Boomi, Automate.io, Jitterbit and many more.

There are a few different ways to integrate software applications. The most common method is to use an application programming interface (API). An API is a set of programming code that defines how two applications can communicate with each other. APIs can be used to automatically send data back and forth between applications, making it possible to quickly connect different software systems. Another way to integrate software applications is through embedded integration. This approach involves embedding a piece of code from one application into another. For example, a social media button on a website is an example of embedded integration. Rather than having to manually enter data into multiple software systems, businesses can use integrations to automate tasks and improve efficiency.

Machine Learning Apps

The key question to ask is if a machine learning app can unlock some value that is not possible without this effort. If a business has historical data that may guide their business forecast or point to insights that may help business make strategic decisions in optimizing their operations, ML or AI can be a great tool to accomplish it. We demystify AI/ML and our team of data scientists can help you assess if ML/AI can be leveraged for meeting the goals you have.

Machine Learning Use Cases by Industry

  • Healthcare: Machine learning is being used to develop better diagnostic tools, predict patient outcomes, and personalize treatments.
  • Marketing: Used properly, machine learning can be a game-changer for sales and marketing teams.
  • Logistics: Logistics is becoming increasingly complex and dynamic, making it difficult for traditional methods to keep up. Machine learning holds great promise for the industry, as it can help to automate many of the tasks involved in logistics operations.
  • Advertising: By leveraging the power of data, machine learning can help brands create more personalized and effective ad campaigns. By understanding consumer behavior and preferences, machine learning can also help brands optimize their marketing strategies for better results.

FAQs

Machine learning is a branch of artificial intelligence that deals with the design and development of algorithms that can learn from data. Machine learning algorithms are used to automatically detect patterns in data and to make predictions about future data.

There are two main classification: supervised and unsupervised. Supervised algorithms are trained on a dataset that includes both input data and labels. The labels are used to teach the algorithm to generalize from the training data to new data. Unsupervised algorithms, on the other hand, are only given input data. They are not given any labels. They must discover the patterns in the data on their own.

Machine learning algorithms can be used for a variety of tasks. Some of the most popular applications of machine learning include:

Classification: Machine learning algorithms can be used to automatically classify data into different categories. For example, a machine learning algorithm could be used to automatically classify images as containing either a dog or a cat.

Regression: Machine learning algorithms can be used to predict continuous values. For example, a machine learning algorithm could be used to predict the price of a stock based on historical data.

Clustering: Machine learning algorithms can be used to group data points together into clusters. Clustering is often used for exploratory data analysis. It can help you to find groups of similar data points.

Anomaly detection: Machine learning algorithms can be used to detect anomalies in data. Anomalies can be things like fraudulent transactions or unusual patterns of behavior.

Recommender systems: Machine learning algorithms can be used to build recommender systems. Recommender systems are used to suggest items to users based on their past behavior. For example, a recommender system might suggest a new movie to you based on the movies you have watched in the past.

Machine learning is a powerful tool that can be used to solve many real-world problems.

  • Do you have historical data of your departments like inventory, sales, operations etc.?
  • Do you struggle with forecasting ? 
  • Does your business have to consider seasonal adjustments that need to be forecasted ?
  • Do you want to automate some key business functions to allow you to scale your business without continually hiring people ?
  • Do you want to automate some processes through AI based chat or voice bot ?

Process Steps

  • Data understanding
  • Data quality assessment
  • Use case feasibility
  • Initial Analysis and findings

Key Outcomes

  • Problem statement
  • Viability for use case
  • Analysis and recommendations
  • High level estimation

Ready to Work With US?

We want to work with like-minded clients on difficult problems that technology can help solve. Reach out and share some basic details of what you are struggling with. Let’s see what we can build together.