There are many good reasons to hire an offshore software development company, and the chances are that if you’re here already, you’re already sold on why you might want to use one.
And so, with that out of the way, let’s go ahead and jump straight into the list of the best offshore software development companies in the world.
Top 10 Offshore Software Development Companies in the World
Zfort Group is an innovative software specialist that prides itself on its ability to solve tangible business problems through the smart use of technology. They say that it’s not what they do that makes them unique, but rather how they do it – using their proven methodology that can then be tailored for each of the sixteen different industries they serve.
The good thing about Zfort Group is that company has a proven reputation for delivering projects on time and budget, without the usual inaccurate estimates and project delays. Founded back in 2000, they’ve got over a thousand completed projects under their belt, more than 50 dedicated teams, and over 200 highly qualified professionals on board. As a result, it provides the best treatment for their clients.
N-IX has over 1,000 employees, which means that they’re a pretty good pick if you’re a large company that needs an extensive development team. They’ve worked with many Fortune 500 companies and have a pretty good track record, having helped over 100 clients create software solutions across various industries. N-IX use techniques including big data, data science, cloud technology, and more.
They’ve been in business for over eighteen years, not just weathering changes but moving with them, acting as fast movers for new tech. They’re based in Ukraine, with additional offices in Poland, Sweden, Bulgaria, Malta, and the US.
Your Team in India
The clue is in the name, here! Formed eleven years ago in India, they’ve established a decent reputation for full-service development, working across a range of different industries and technology types. Throughout the years since their formation, they’ve averaged nearly 100 projects per year to leave them with over 1,000 completed projects. The result is a pretty decent outsourcing company at prices that almost every company can afford.
This Panama-based company has a motto: Pa’ lante, which means “always moving forward” in Panamanian. It’s a pretty good motto for them because they pride themselves on their ability to hit the ground running, powering through roadblocks as though they’re not even there.
Based in Lviv in Ukraine and founded an impressive thirty years ago, back in 1991, Eleks has over 1,000 employees and works with high-profile clients like Aramex, Autodesk, ESET, TAIT, and Havas. They also have additional offices and research centers worldwide and sales offices in the United States and Japan.
Their offering covers a little bit of everything, from QA and testing to development, technological consultancy, and more. They have a whole heap of experience from their thirty years in business, including artificial intelligence and sector experience in finance and retail.
This company started back in 2013, intending to help companies with their lack of technological solutions. They won their first major client at the end of their first year when they developed a mobile app for a non-profit called Teleton 20-30. This led to them winning new business from Adidas and Ralph Lauren.
Hypernova later started working with clients in the banking sector and other key industries, and have continued to grow throughout the years while specialising in outsourced development. As of 2011, they have just over 50 employees, making them one of the smaller companies on this list, though that hasn’t stopped them from working with some global giants.
Based in Poland, this company was formed nearly 20 years ago, back in 2004, and has grown to have over 1,000 employees at the time of writing. On top of their headquarters in Gliwice, Euvic has additional offices in Germany, the Netherlands, and the USA and offers different services that include custom software development, mobile app development, application testing, and more.
Clients including Ardent, FileHold Systems, and JetAvation can work with clients of all different shapes and sizes. Still, they’re mostly known for working with mid-size companies across a range of different industries.
Codal is built on transparency, and they pride themselves on the fact that they partner with clients and understand what they need to do and why they need to do it. They then create a bespoke proposal that outlines exactly what you’re paying for and why you’re paying for it.
Codal has a particularly good reputation for their employees, who have a great pedigree and an impressive work ethic. They’ve got excellent feedback from their dozens of clients and have the award wins to prove it. They also have millions of end-users for their successful builds.
Founded by Hitesh Agarwal, this offshore development company aims to offer a mixture of value for money and consistent peace of mind throughout the development process. Agarwal founded the company because he was unhappy with the way that the companies he’d worked for approached development, failing to focus on the software’s real-world applications.
Since its inception, the company has grown quickly in a couple of years, serving hundreds of clients from around the globe and growing at over 100% YoY. They might not have the same pedigree as some of the others, but they’re definitely a rising star.
MobiDev is all about making it as easy as possible for founders and entrepreneurs to create software that makes their lives easier The idea is to plug straight into the company as a development arm so that the leadership team can focus on building the business and servicing the clients.
The company prides itself on its ability to take care of the minor but still important details, taking away the headache of software development. They reinvest 30% of their cash into research and development. So far, they still have an impressive 100% project success rate.
Can we use Laravel for big projects?
Yes, of course, you can!
The good thing about Laravel is that it can be used for any project of any size. The sky’s the limit, and one of the advantages of using it is that you’re able to start off small and scale up your Laravel build as and when it’s needed.
Laravel is like most other content management systems in that it essentially underpins your site and provides you with a base that you can build from. It’s no more or less suitable for big projects than something like WordPress or WooCommerce, and so it’s really a case of choosing the best platform for you. If Laravel ticks all of your boxes, it’s a no-brainer.
The Benefits of Offshoring Development
We could write a full post on the benefits of offshore development because there are so many different advantages available. Different companies are looking for different things from their offshore development companies, but just a few of the main benefits for people to tap into include:
Scalability: One of the biggest advantages of working with an offshore development company is that they can bring in new resources at speed to scale up and down with your company. And you don’t even have to worry about paying them a salary!
Lower costs: Because they’re typically located in cities and countries with lower costs of living, offshore companies can usually save money on their operating costs and pass those savings on to their customers.
Specialist expertise: Many offshore development companies specialize in working on a certain kind of build, whether that’s ultra-secure builds or whether they pride themselves on speed or their ability to work in certain industries.
Offshore Software Development Costs: Rate Comparison
Offshore development companies tend to have a variety of different rates, just as you’ll find if you look at domestic companies and compare their prices. Still, as a general rule, you should be wary of any company charging less than $10-15 per hour, while once you get to $40+/hour, you’re getting pretty close to what you’d pay for a local agency. And of course, remember that you get what you pay for. So sometimes it can be worth paying a little more if it’s going to have a positive effect on the service you receive.
How to Choose an Offshore Software Outsourcing Company
There is a range of different factors that you’ll want to consider when choosing an offshore development company. Of course, most people immediately think of the potential cost savings, because outsourcing to an offshore company can mean that you can work with companies where the cost of living is lower, but cost isn’t the only factor that you’ll want to think about.
For a pretty good starting point, you can take a look at the list of benefits that we shared and identify which of those benefits are the most important for you. If scalability is a factor, for example, then, cost might take a backseat so that you can focus instead on those companies that can bring in the most additional resources to work with you.
One final thing to note is that you should never underestimate how important it is to find a company that can offer stellar customer service because that’s often the first thing to go when they’re trying to keep their costs down. The problem is that you can’t put a price on customer service, and so it’s worth finding an outsourcing company that offers reasonably good support as a bare minimum.
Why choose Zfort group as a software development company?
Zfort Group is an obvious choice when you’re looking to outsource your software development because they tick all of the boxes, which is why we ranked them first on this list. They have a strong history of delivering software builds on time and budget, and they also offer pretty good customer service.
In fact, unless you have some specific need that calls for a specialist agency, they probably offer the best overall mixture of price versus quality, and they have the testimonials to back that up. Check out their website or reach out to them to find out more.
The world is connected today in more ways than it ever has been before, as billions of objects are now capable of connecting to the internet or interfacing with devices that are already online. The new “Internet of Everything” generates a deluge of data, which is increasingly directed to the cloud for processing and storage. Meanwhile, Artificial intelligence is increasingly utilized to analyze and derive value from these enormous stores of data. In industries such as healthcare, transportation, industrial manufacturing, and financial services, AI algorithms are now being applied to increasingly difficult tasks, including critical decision-making processes.
What differentiates human from machine is the quality of judgement, creativity, and critical thinking. Humans still have the edge, but intelligent machines are slowing progressing in their ability to replicate the human decision-making process. Deep learning algorithms utilize artificial neural networks inspired by the human brain, performing a task repeatedly with small variations to find an optimal outcome.
The key to success in Machine Learning and ultimately Artificial Intelligence is data. Copious amounts of data along with rapidly advancing computing power allow machines to solve increasingly complex problems. Data not only needs to be plentiful but it also needs to be clean, representative, and balanced. If training data is not wholly representative of the diversity of a general population, then the results will undoubtedly be subject to bias. Such biases, whether intended or unintended, can manifest in subtle ways or via colossal and public failures such as the recent examples of age, gender and racial bias found in the ML offerings of some of the world’s largest software companies.
The issue of bias is well documented in sociology, psychology, and other disciplines. Our society has implemented many different safeguards to ensure that bias, and its more offensive derivatives prejudice and discrimination, are kept in check across situations as varied as employment, creditworthiness, education, and social club membership. Because algorithms are increasingly being used to guide important decisions that affect large groups of people, it is critical that similar safeguards are enacted to identify and correct issues of bias in machine learning and AI. This bias is often unintended and can also go unnoticed for a long time, so it is important to carefully evaluate the prediction results from a model to look specifically for instances of bias.
Machine learning models are entirely reliant on the underlying data that they were trained on. If this training data is biased, limited, unbalanced, or flawed in some fashion then the model will inevitably end up producing biased outputs. Data Scientists must exercise care and caution in their data collection and data labeling phases. Data should be balanced and diverse and ideally cover corner cases. If related to populations of humans in some way, such as in face recognition or sentiment analysis, it is important to achieve balanced and representative training data from a global pool of subjects if the model will potentially be applied to a global pool of actual data.
BasicAI provides a comprehensive solution for your data collection and annotation needs. We often assist clients seeking to improve diversity in training data by offering a spectrum of regions from which data can be collected. We utilize our global network of partners and affiliates to collect samples from Asia, Africa, Europe, and the Middle East. Meanwhile our proprietary annotation platform ensures highly accurate and cost-efficient data labeling in the cloud or on premises. With a focus on accuracy and effectiveness, BasicAI is committed to providing world-class annotation solutions across industry sectors.
To learn more, contact us at [email protected]
Everything should be stated as simply as possible, but not simpler.To make the game entertaining and interesting, it is not necessary to make computer-controlled opponents smarter. In the end, the player must win. However, letting him win only because the manager of the opponents of AI is badly designed, is also unacceptable. Interest in the game can be increased if the mistakes made by the enemy are intentional. Carefully adjusting the mistakes of opponents, making them intentional, but believable, programmers will allow opponents to look smart and at the same time ensure the victory of the player. In addition, by monitoring AI systems and appropriately controlling them, you can turn situations in which opponents look silly into an interesting gameplay.
- Albert Einstein
A common mistake in the development and implementation of AI systems in computer games is in too complex a design. The AI developer can easily get carried away by creating an intelligent game character and losing sight of the ultimate goal, namely, creating an entertaining game. If a player has the illusion that a computer opponent is doing something clever, then it does not matter how the AI (if any) creates this illusion. A sign of a good AI programmer is the ability to resist the temptation to add intelligence to where it is not needed, and to recognize situations in which more "cheap" and simple solutions are enough. Programming AI is often more like art than science. The ability to distinguish between moments in which cheap tricks are enough, and those where more complicated AI is required, is not easy. For example, a programmer, having full access to all structures of game data, can easily cheat by making the NPC omniscient. NPCs can know where the enemies are, where the weapons or ammunition lies, without seeing them. However, players often recognize such cheap stunts. Even if they can not determine the very nature of cheating, they may have a feeling that the behavior of the NPC is not like the natural.
When I was young a long time ago, I did not have any friends. I needed to communicate, I dreamed to have someone close, but I could not find the understanding among other people, therefore I found salvation only in books and computer. When on the market came the first CD-drive, I got my first CDs with the games. You probably remember, such games as three hundred games, five hundred, seven hundred ... I had a program “Dial” (an interactive companion) on one of my CDs in addition to the arcade games and shooters. No one can think of more boring pastime than communicating with the chat-robot, but I liked it. I began to realize that in order to have a true friendship is not required a physical contact, it takes only some warm and sincere words to be understood.
I was growing up and getting higher, the bigger I was the more I read, because I was able to reach for the higher shelf in the bookcase every year. One day, when I was ten years old, I grew up to the shelf with the science fiction authors: Azimov, Sheckley, Bradbury ... I liked the Soviet book "Can a machine think?" more than any foreign Sci-Fi. I loved re-reading this book, as well as the textbooks for BASIC and Pascal. You may believe it or not, but once while I was reading this book, my subconscious had decided everything for me: I need to create the artificial intelligence. It does not matter that I did not know how to do it. It does not matter that I did not know how to program. It does not matter that I did not have any idea what a computer friend should become.
I have recently read an article where its author states that the computer will never be able to understand the text as it is understood by the human. He cites a number of impossible tasks to machines as proof with an emphasis on the lack of efficient algorithms and modeling impossibility of a complete system, which would take into account all the possible alternatives of the text. However, is it really that bad? Is it true that for the solution of such tasks is needed special processing power? What is a situation of natural language text processing?
What does it mean to "understand"?
The first thing I was confused is the question itself. Could a computer be able ever to understand the text as it understood by the human? What exactly does it mean to "understand as the human"? Generally, what does it mean to "understand"? In the book “Data Mining: Practical Machine Learning Tools and Techniques” authors asked themselves a similar question. What does it mean to "get trained"? Let us assume that we have applied to the "interpreter" some training technique. How do we check whether or not a student is learning? If a student attended all the lectures on the subject, it does not mean that the student has learned and understood it. In order to test this, teachers hold examinations, where student is asked to complete some tests on the subject. Same thing is with the computer, we want to know whether it has learned (whether it has understood the text). In order to find out that we have to check, as it solves the specific applications, translates the text, highlights the facts, gives concrete meaning of a polysemantic word, etc. In this perspective, the meaning misses the importance at all. The meaning can be assumed as a certain state of the interpreter in accordance with which it handles text.
Google has a secret laboratory that even many employees do not know about, where the new projects are being developed, and their description sounds like a sci-fi movie. The New York Times tells about this in its article.
The laboratory was located somewhere in the area of San Francisco Bay, where the brightest Google’s engineers are working on dozens of the projects. This is the place where your refrigerator could be connected to the Internet, and it will order the food, when it ends. Your plate could message in a social network what you eat, and your PA robot could go to the office while you are in your pajamas at home.