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The definition of AI today is not limited to Machines transforming into self-aware entities and taking over the human race from fiction novels. Rather, each one of us today has interacted, knowingly or unknowingly with an AI algorithm, which is trying to learn from your behavior and adapting its pathways to curtail to your needs.

AI has not spread so to say, through all industry verticals but it has found niche areas of application in most sectors. Areas which are critical, intricate and involve humanly impossible feats. There are various perspectives based on which AI can be measured; but based on the degree of intelligence AI can be categorized into ANI (Artificial Narrow Intelligence), AGI (Artificial General Intelligence) and ASI (Artificial Super Intelligence). Most AI applications today are ANI in nature. They are dedicated applications destined for a specific purpose and exceed the capabilities of an individual in that domain. Playing chess is a common example for ANI, wherein the program is precisely constructed for the game of chess. The same algorithm if applied to a game of Tennis, may not give the same output as expected.

Many such ANI applications are proving their worth in critical domains of our day to day life.  Be it surgical robot assistants in performing microsurgery procedures, AI assisted diagnosis, Predictive intelligence driven Logistics or a simple natural language driven reactive instrument, AI devices are gradually creeping up into our routines and establishing dependability amongst the generation. Having said that, these are large-scale implementations of AI which involves heavy investment in terms of skilled manpower as well as cost, but how can small and medium scale businesses benefit from these AI revolutions. Can these be embedded in an organization’s processes to improve their efficiency in any manner? Are there any ready-to-use AI solutions which do not involve a major investment in research? Are there any open source AI apps that can be molded as per an organization’s needs?

Recommendation Engines

People are influenced into buying products by learning from shared experiences, common trends or cultivated tendencies. Based on these and many other factors a consumer develops a persona which establishes the inclinations he/she follows.

As the name suggests REs come up with recommendations that a consumer/user might find appealing and useful based on algorithms which evaluate the consumer’s previous search pattern, demographic, or user persona.

The recommendations are performed by information collection, storage and pattern generation based on collaborative filtering algorithms. Amazon, Netflix, Ebay, Walmart are at the forefront of designing Recommendation Engines that help them come up with optimal results for their target clients.

But not all organizations can put-in a similar effort in terms of research, storage, and filtering. For such organizations on the lookout for out-of-the-box or customized Res, there are alternatives in the market. You can either opt for Recommender Systems available as SaaS (Software as a Service) or customize and build upon an open source Recommender system.

Recommendation Engines as SaaS

Azure Machine learning is a designing platform made available over the Cloud for flexible use. Registering with Azure ML, you are provided access to an array of algorithms which can be utilized as per your application scenario. Azure ML facilitates a browser-based design studio.

Amazon SageMaker is a platform, which also hosts a platter of AI services; Recommendation Engine being one of them. It helps you build (or use pre-built), train and deploy ML services for your business.

Episerver Personalization provides a suite of products integrated with AI and Cloud, which uses behavioral analytics over customer activities and individualize recommendations for e-commerce, digital content, and marketing solutions.

Strands Retail is another e-commerce focused system, which provides Product recommendations, Email solutions, customer recommendations, and advance merchandising. 

Open source Recommendation Engines

recommendationRacoon is a module built on top of Node.js and Redis which provides filtering-based recommendation engine. This can provide websites with a standard setup for products/movies providing customers the capability to like/dislike items and receive recommendations.

Apache Mahout is a more mathematically inclined ML system that can be used by mathematicians and data scientists to come-up with their own algorithms and build an RE.

Good Enough Recommendations (GER) is a search engine based on Node.js, hence being scalable and easy to integrate.


With the extensive market of Customer support centers trying to resolve user queries/complaints either on chat, call or email it was about time that most of these repetitive, tedious and templated functions be automated.  The word Chatbot derived from the term Chatterbot defines an application, which tries to replicate a human interaction (verbal, written) based on Natural Language Processing algorithms and self-learning to come up with accurate and human-like responses.

There are 2 distinct chatbots operating today, which are rule-based and machine learning based. The rule-based chatbots have a predefined set of questions and agreed upon responses that the bot will be providing, and hence doesn’t involve extensive learning. With the Machine Learning bots, they carry out language interpretation and understanding, calculating the next response based on algorithms.

There are a few open source Chatbot platforms that you can readily embed in your portal or application.

BotKit is marketed as the most extensively used platforms for building chatbots, and in a recent development, it has been acquired by Microsoft. Botkit is a rule-based framework wherein the developer has to integrate interaction logic and a fixed set of responses. BotKit provides limited machine learning capabilities and new pathways have to be defined as we go.

Rasa provides an enterprise-scale open solution that has machine learning embedded within them, which facilitates text and voice-based chatbots and assistants. The entire framework can be on your site keeping all components in-house.

Unlike the other platforms, Pandorabots is a chatbot builder tool that facilitates a custom instance environment and a Flexible API to build custom bots.

Natural Language Processing

An evolution beyond chatbots is the voice-driven chatbots, mostly used in designing Assistants for your personalized experience. Most standard devices like smartphones have introduced applications that react to voice/speech and further process the user’s requests. Explicit devices, which are driven only by voice interfaces have started hitting the market and are on the verge of acting as speech-driven internet-butlers.

The small-scale applications of Assistants are gradually growing, and they can be used for routine tasks like taking notes, marking the calendar, finding a route or answering a query. But in case you have your own idea of how to utilize these assistants for your business, there are open source platforms that are available which will help you do so.

Mycroft is marketed as the world first customizable open source voice assistant, which can run across all platforms – desktop, mobile, or an automobile. They have provided developers with documentation and API on how to customize Mycroft for their needs which runs on Linux and Raspberry Pi.

Jasper has an easy-to-use developer API, which facilitates easy integration with your existing applications. Further, it states to be 100% Open source and can be built with off-the-shelf hardware, and personalized software modules.

Enhanced Business Intelligence

Business intelligence was being used for reporting summarized information using data generated from business and operations. The process of collection, storage, segregation, analysis and presentation in a business relevant format which stimulates relevant suggestions can be termed as Business Intelligence. On a large scale, BI is being used for generating information, which would aid in Business Decision Making. 

This kind of analysis is mostly reactive and known as Descriptive analytics, wherein based on data generated from the previous years we get a descriptive overview of which decisions resulted in what outputs. A step ahead of this, the same data can be used for Predictive analytics, which uses statistics to predict and influence the decisions of the future. The base of these predictions are probability theories and statistics.

With a heightened level of processing power and improved facilities for big-data storage, we have gained the ability to move to the next mode of analytics, which is Prescriptive Analytics. This provides us with an accurate prescription of what decisions to take and what result each decision will result in. These are based on machine learning applications, which can analyze business trends, build patterns based on past experiences, examine future probabilities to come up with opportunities profitable from an expected outcome standpoint. BI empowered with Natural Language Processing and Image Processing helps evaluate even the unstructured data within an organization that influences customer decisions.

Power BI  is a cloud-based BI application, which can be utilized on-the-go which is now elevated with AI capabilities. Driven by Azure cognitive services we get access to pre-trained machine learning models, which help us extract information from a variety of sources like unstructured documents, images, social media posts. Power BI is easy to enroll and feasible to use even for Small organizations.

Sisense is renowned for its easy-to-use dashboard and drag and drop datasets. Hence, a novice with minimal knowledge of a BI system can adopt Sisense with a basic briefing. It provides customized reporting across industries and departments and with Sisense Hunch – AI Engine it facilitates tie-ups with Big data to Edge computing.

Pentaho is an open source BI engine also available in the paid version. It also provides IoT based analytics and facilitates Bigdata integration.

AI has introduced remarkable changes in the way we do business and interface with clients. It is time for SMBs to utilize these nuances and get into the groove with customers getting seasoned to these new methods of interaction. Gradually these conventions will creep into all modes of communication and be a norm for all business processes. To be a part of this revolution, it is mandatory to steadily adopt the market trends with handily available solutions so that you do not wake up in the medieval age one fine day.