More than a third of consumers believe AI improves the customer experience. So if you want to enhance the ways customers interact with your business, you’ll want to hire one of the AI companies on our list. Below the list of companies, you’ll find details on how we ranked them, as well as a guide to help you shortlist and hire the best company for your needs.
We follow a comprehensive evaluation methodology to rank the companies on our list. These evaluation criteria cover everything from the company’s track record with clients to the developer team’s expertise in the different aspects of AI.
The first stage of the evaluation process involves a thorough study of each company’s website. We do this for the following reasons:
While we consider the testimonials shared by companies on their websites, we know that these are likely to be overwhelmingly positive in nature. For a more objective view of the company’s past work, we do a thorough survey across third-party review sites like Clutch. If a company’s negative reviews outnumber the positive ones, we remove it from further consideration.
Narrowing things down to the top 100 AI companies, the next critical process we follow is a deeper assessment of each company’s services. An AI development agency needs a team that excels in a vast range of services, and we evaluate these offerings against industry standards and best practices. The service areas we consider most important are described in detail below.
AI can be classified into specific types based on a number of different criteria. The types within one group can match types from another. For example, an AI with a specific business use requires a specific AI solution in terms of independence of task performance. The classifications of AI that these artificial intelligence companies work with are given below:
Also known as “weak” AI, this is programmed to perform a single task. This could be checking the weather, making sense of raw data to write a report, or simply playing chess. ANI systems can’t perform outside the task they’re designed for because they pull information from a specific data-set. All the machine intelligence we see around us today (Siri, Google Assistant, Alexa) falls under this category.
Also known as “strong” AI, it exhibits human intelligence and can perform any intellectual task that a human can. Machines with AGI are conscious, sentient, and driven by emotion and self-awareness, much like what one would have seen in Her and a number of other science-fiction films. In the future, artificial intelligence software companies are expected to develop AGI that will be able to solve complex problems using reasoning and judgment, work under uncertainty, and integrate prior knowledge into the decision-making process.
The most advanced AI, ASI will surpass the cognitive performance of humans in all respects, including creativity, general wisdom, and problem-solving. Consequently, this is also the type of AI that so many people worry about, imagining it taking independent control of the world from the human race.
These machines don’t use past experiences to determine future actions. AI companies use these for basic tasks since they only perceive the world and react to it. An example would be IBM’s Deep Blue, which makes certain chess moves based on the opponent’s play.
These machines can retain data for a short period of time and use it, but they can’t add it to their library of experiences. Some self-driving cars use this technology, navigating the roads by temporarily storing data like the speed of nearby cars, the distance between cars, and the speed limit.
The human mind has thoughts, memories, emotions, and mental models that drive human behavior. Machines based on the theory of mind will be able to imitate human mental models by forming representations of the world and the entities within it. While leading companies in AI have developed computers that work on basic models, a machine that has a “mind” of its own is yet to be developed.
This is the ultimate goal of AI and, in a way, an extension of the theory of mind machines. A self-aware machine will be able to operate as a person does, preserving itself, predicting its own needs and demands, and relating to others as an equal. Whether a machine can become truly self-aware or conscious, however, is still a matter of debate among researchers.
Machine learning powers analytic AI to scan large amounts of data to identify patterns and then produce insights and recommendations. Certain AI companies would use this data-driven decision-making process for tasks like sentiment analysis and supplier risk assessment.
Functional AI is similar to analytic AI in that it scans huge amounts of data for patterns and dependencies. The difference here is that instead of giving recommendations, functional AI takes actions. An example would be an inventory system where a decrease in stock is automatically detected and a robot replaces items to replenish it.
This type of AI, as the name suggests, assists businesses in automating communication processes without compromising on interactivity. Various chatbots have been developed by AI companies with abilities ranging from answering pre-built questions to understanding the conversation’s context.
Unlike the traditional reliance on keyword search, text AI uses semantic search and natural language processing to identify the most relevant answer to a query even if the answer doesn’t fulfill an exact keyword match criterion. This is possible because text AI can build semantic maps and recognize synonyms to understand the query’s context. Companies developing AI would use this type of artificial intelligence in business for text recognition, speech-to-text conversion, machine translation, and content generation.
Visual AI can help businesses identify, classify, or convert images and videos into insights. This type of AI covers fields like computer vision and augmented reality. There are several examples of the use of visual AI in business, including damage estimation by an insurance company based on photos of the damaged vehicle or a facial recognition solution that can help a retailer personalize its customer service.
One company specializing in AI can be very different from another because AI encompasses a wide range of capabilities. Most artificial intelligence companies typically have expertise in one or more of these subsets, and accordingly, the solutions they provide vary. These subsets of AI include the following:
On our list we've included machine learning experts. ML gives machines the ability to automatically learn from experiences without being explicitly programmed. Expertise in ML is primarily concerned with the design and development of algorithms that allow a system to learn from historical data by discovering patterns.
AI technology companies may also have particular experience in machine learning’s subtypes. These include supervised learning (learning from known datasets and predicting the output), unsupervised learning (training algorithms using data that’s neither labeled nor classified), and reinforcement learning (training an AI agent based on feedback received when the agent performs an action).
Another important type of machine learning is deep learning, which works on deep neural networks and provides the ability for machines to perform human-like tasks without human involvement. Deep learning can use both supervised and unsupervised learning to train an AI agent.
NLP is the part of AI that allows a machine to understand and process human language. With the help of NLP, AI companies can instruct systems in a conversational language instead of having to use computer language code. This is how you can ask Siri to check the day’s weather or Alexa to play a particular song.
These AI systems are programmed using the knowledge of human experts. In other words, expert systems try to copy the decision-making ability of human experts. These systems solve complex problems using a body of knowledge rather than conventional procedural code. A common example is a spelling error suggestion while typing in the Google search box.
Machine vision helps a machine recognize an object by capturing and analyzing visual information using one or more video cameras, analog-to-digital conversations, and digital signal processing. AI companies would employ machine vision to program narrowly defined tasks such as reading serial numbers or counting items.
This AI technology makes it possible for a machine to understand spoken language and translate it into a machine-readable format. Speech recognition technology can be used to perform an action based on the instructions defined by the human. The human needs to train the speech recognition system by storing speech patterns and vocabulary into the system. By doing so, they can essentially train the system to understand them when they speak. This might seem similar to NLP, and, indeed, both speech recognition and NLP are used together in voice assistants and speech analytics tools.
Robots are programmed machines that can perform a series of actions automatically or semi-automatically. Artificial intelligence companies can enable robots to perform more complex tasks using machine intelligence backed by algorithms. Machine learning is commonly used these days to manufacture robots that can even interact socially like humans, e.g., the humanoid Sophia developed by Hanson robotics.
The development work for AI requires teams to have expertise in the use of a range of different kinds of tools. We’re looking for companies only working with the best of each kind of tool—it’s best when they have certifications in the use of these tools. Each category below covers the most prominent tools used by leading AI companies:
Belonging to the Google family, TensorFlow is a robust open-source framework with easy programming options, great support for deep learning, and accessibility even from a mobile phone. It’s particularly popular for statistic program development. It offers distributed training, through which machine models can be effectively trained at any level of abstraction required by the user. TensorFlow uses Python and is capable of high computational power, making it usable on any CPU or GPU.
This is a faster, more versatile open-source framework based on neural networks that support text, message, and video remodeling. This framework has integrations with major datasets, making it the top choice for AI companies with products like Skype and Cortana. It supports both Python and C++ and is highly optimized for efficiency, scalability, and speed.
Based on Python, Theano uses GPUs in place of CPUs to support deep learning research and deliver accuracy for networks that need high computational power. With Theano, the evaluation of expressions is faster thanks to its dynamic code generation, excellent accuracy ratios, and efficient data-intensive applications. Unit testing is another feature here, allowing the AI companies using it to detect and diagnose errors easily.
A powerful deep learning framework, Caffe comes with a preloaded set of neural networks, making it ideal for projects with tight deadlines. Other advantages include its image processing capabilities; extended support for MATLAB; high speed and efficiency; interlinking of C, C++, and Python; and support for CNN (convolutional neural networks) modeling.
Torch is an open-source machine learning library for scientific and numerical operations. It’s based on a language called Lua, though there’s also a Python-based version called PyTorch. By providing a large number of algorithms, Torch facilitates easier deep learning research and improved efficiency and speed. Its powerful N-dimensional array—which helps with operations such as slicing and indexing—and linear algebra routines and neural network models are all advantageous to these AI development companies.
Other popular frameworks and libraries include Amazon Machine Learning, Accord.Net, Apache Mahout, Spark MLib, Keras, Sci-kit Learn, and MLPack.
Just as Amazon S3 dominates the cloud storage industry, its AI offering has been racing ahead with its useful features. These include Amazon Comprehend (which makes sense of large amounts of textual, unstructured data), Amazon Forecast (which turns existing time series data into accurate forecasts), Amazon Lex (which builds conversational interfaces into your applications), and Amazon Personalize (which uses data sets to create recommendations). While these services are targeted at developers, they aren’t difficult to get the hang of. Moreover, you can sample most of these for free, and then pay for only what you use.
Its services support building enterprise-level AI applications. These allow AI services companies to make use of big data to find the patterns and meanings that humans miss. Among its possible applications are the detection of fraudulent credit card applications, the prediction of customer churn, and the forecasting of demand. This is a relatively high-end platform, requiring a large amount of customer data to be really productive. On the plus side, it’s easy to use, with no complex server setup required and minimal coding thanks to a drag-and-drop designer.
This sophisticated platform allows businesses to combine AI into their applications and manage cloud data easily. Its PowerAI feature provides deep learning capabilities, while other modules allow the processing of any kind of data to extract meaning and look for patterns. Personality insights and tone analysis help you extract personality characteristics from text, as well as understand emotions and intent. Leading artificial intelligence companies can also take advantage of Watson’s conversion of audio or voice input into plain text capabilities, as well as tools that create intelligent chatbots without the need for coding. It’s designed for professional users, so you’ll likely need its documentation to understand the platform. However, most of its services can be tried out for free.
Google’s cloud platform offers services like machine learning and data analytics, along with the popular open-source library TensorFlow. Like Amazon’s AI services, Google has a suite of services offering a range of AI features. These include Cloud Vision API (for image recognition and classification), Cloud Translation API (for language detection and translation), Cloud Text-to-Speech API (for the conversion of text to speech), and DialogFlow (to build conversational interfaces).
Other popular platforms used by AI technology companies include SAP Leonardo Machine Learning, Intel Nervana Platform, Salesforce Einstein Suite, HPE C3 AI Suite, and Infosys Nia.
The most popular choice among AI development languages because of its simplicity, Python has a simple syntax that makes implementing AI algorithms easy. It supports object-oriented, functional, and procedure-oriented styles of programming. Python developers have plenty of libraries available—like Numpy and Pybrain—which makes using this language for different purposes very convenient.
R makes it easy to produce well-designed, publication-quality plots, including mathematical symbols and formulae. It’s the most effective language for analyzing and manipulating data for statistical purposes. R also boasts numerous packages like RODBC and Gmodels, which make it easy to implement machine learning algorithms.
One of the oldest languages used by companies developing AI, Lisp can effectively process symbolic information. Its key advantages include its excellent prototyping capabilities, the easy dynamic creation of new objects (with automatic garbage collection), and a development cycle that allows for the interactive evaluation of expressions and recompilation of functions while the program is running.
Prolog has features like efficient pattern matching, tree-based data structuring, and automatic backtracking, which give it a powerful and flexible programming framework. Prolog is widely used in medical projects and in the design of expert systems.
Apart from these, C, C++, and Java—which find extensive use in most kinds of software development—are also commonly employed in the field of AI.
The best AI companies follow a step-by-step process to ensure top-notch execution. We assess the companies on our list and evaluate how closely their development processes adhere to industry standards. They should typically include the following steps:
The problems that AI is used to solve are typically complex. Given this, development companies need to take a consultation-based approach at the beginning of each project:
AI software companies then have to design the initial solution architecture, laying out all the necessary requirements and specifications. This is crucial in defining how the projects are deployed, regardless of their nature. The documentation at this stage contains details regarding the data sourcing and processing pipeline, the project’s monitoring, and the actual best-fitting solution architecture. There’s a wide range of tools that can be used at this stage, depending upon the project’s requirements. Here are the most notable:
Other tools commonly used by companies developing artificial intelligence include Azure Machine Learning Studio, Dataiku, DIANNE, and TensorBoard.
This is the time to collect any open-source data and anonymize or depersonalize any sensitive data. It’s followed by data clean-up and adjustment for further analysis, as well as data labeling. The latter-most can either be done in-house or in collaboration with big data experts.
This is when the machine learning pipeline is developed, starting with a baseline model to verify the initial results and then building on it by following the optimal architecture. Once that’s done, the training and parameters tuning cycle is launched. The model is then tested in inference mode, and the graphs are optimized to achieve the defined metrics for accuracy and speed.
This is where artificial intelligence companies merge the data pipeline with machine learning and develop the APIs, services, or packages that will be used in production. Once that’s done, the pipelines are “packed” and deployed to the required destination such as the cloud, dedicated servers, or mobile or embedded devices.
To be efficient and effective, this process has to combine monitoring, performance testing, and CI/CD systems. This is a key stage that grants insights into key metrics and any data anomalies, which is crucial for detecting any data distribution and processing changes that can either impair or improve performance.
Artificial intelligence companies can only call their job done once they train the client’s team, leaving everyone involved capable and knowledgeable around the new technology they’ll be working with. While most good vendors provide ongoing services, the daily maintenance of an AI system must be in-house, which makes this stage as important as the system’s development.
Although we followed the detailed evaluation methodology described above to come up with our list of artificial intelligence companies, it’s important to understand that not all of these companies are likely to satisfy your specific needs. Here’s how you can narrow your search down to one or two companies that will align perfectly with your goals:
Artificial intelligence is an exciting field, with new uses being discovered almost every day. The far-reaching possibilities for transforming businesses using artificial intelligence are uncontestable. We’re sure that this detailed article will assist you in finding the right partner among the many AI companies out there, ensuring that your business’s AI-led transformation will be effective and hassle-free.