{"componentChunkName":"component---src-templates-blog-post-js","path":"/conversational-ai/state-of-chatbots-in-2020/","result":{"data":{"site":{"siteMetadata":{"title":"Senseforth: AI Powered Chatbot Solutions Provider","author":"@senseforth"}},"markdownRemark":{"id":"d602d3cf-f3f0-5866-bd2e-01e0a106c6bc","excerpt":"Open-sourcing of any product/service is often a subject of contesting dissension. By making the…","html":"

Open-sourcing of any product/service is often a subject of contesting dissension. By making the source code or design documents or blueprints or even the raw content of the product publicly available, providers deliberately endow users to use, modify, study or distribute it – for personal or commercial purposes. So why go for such a model? The reason sometimes lies in the operational productivity of the solution that reaches new heights and attractiveness. The resultant effect can be seen by the popularity of the likes of Mozilla Firefox, Linux, WordPress, VLC, Apache, and Ubuntu.

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Decentralizing the software platform of chatbots, per se, is a felicity of the next level. It not only encourages open collaboration but assists the users to customize their solutions to specific needs. Chatbots can be deployed for a range of use cases in a plethora of industries and their attributes, design, framework, look and feel differ for each combination. Open-sourcing untethers the restraints of not only chatbot developers but also the business owners, empowering them with unprecedented freedom and flexibility. Open source chatbots are thus billowing solutions desired and demanded throughout the market. Specialists can roll their imagination when it comes to managing responsiveness and training the bots in different domains.

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If you are seeking to understand the concept and market landscape of open-source chatbots, read on!

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Building chatbots – Is open-sourcing an easy way out?

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Textual or auditory conversation assistants can be deployed in any medium - website, app, messenger, slack, IM, etc. for a wide-set of diversified business reasons. From customer service, employee enablement, lead generation to site navigation or even something as rudimentary as creating meaningful interactions – chatbots solve a multitude of digital age problems. If you go by a 2018 Gartner report, apart from increased customer satisfaction, organizations also see a decrease of ~70% in inquiries and ~33% saving per voice engagement by implementing virtual customer assistants like chatbots.

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Although chatbots are sure to stream in revenues when deployed efficiently, they are still seen as a risky venture placed on the downward slope of the Peak of Inflated Expectations on Hype Cycle 2019. When diving into the deep realm of chatbots – be it AI or rule-based – businesses look for secure investments, simple building platforms, less complexity, ease of training, and most importantly cost-effectiveness. The volume and complexity of data that the chatbot can handle and the degree of ad hoc, unpredictable judgments it can make are also important decision factors.

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Ideally, there are only two ways of chatbot deployment – either by using the state-of-art platforms such as A.ware by Senseforth.ai or independently building them from scratch using any of the open-source platforms such as Google’s Dialogflow or Amazon’s Lex. They are typically designed and used in dialogue systems for customer care applications or information-acquisition and knowledge-discovery. Open-sourcing serves as a third mid-way to easily implement the technology. Open source bots live on the internet, use databases and API’s to send and receive messages, read and write files, and perform regular tasks.

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How does it work?

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Interactions in chatbots are classified as structured and unstructured. Structured conversations follow a logical flow of information, such as menus or choices; while the unstructured conversation is more freestyle, such as the informal texts exchanged between friends. Be it open sourced or not, every chatbot needs to be fed training data for both types of conversations.

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For efficiency, bots are trained using past information and developers use history logs to upgrade the chatbot’s capability. With a blend of machine learning tools and models, developers analyze client inquiries and reply with the most appropriate answer. Even while developing the script for messages, it is important to keep the conversation topics close to the purpose served by the chatbot and develop scripts for a conversational user interface.

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The conversation design has both structured and unstructured types - close-ended conversations that are easy to handle and open-ended conversations that allow customers to communicate naturally.

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Following functionalities are also pre-built in chatbots:

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Open-sourced chatbots consist of few other components:

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Therefore, chatbots (open sourced or not) have an app layer, a database (knowledge base), and APIs to call other external administrators.

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When you open source a chatbot, the type of platform plays a pivotal role in the success of your business strategies. Open source chatbots have a steep learning curve. The open-source communities tend to be developer-heavy, without a focus on design. As a result, non-developers struggle in handling the technology with minimal support and few guiding manuals. Bot framework in such cases can be too much to handle. Also, open-source chatbots are not a standalone service, so the total cost of ownership (TCO) covers other ancillary costs like servers, training, hardware, and implementation.

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Using proprietary platforms with industry-specific pre-trained algorithms, significantly reduces training and testing times. A lot of these platforms provide a non-coding environment as well. Non-coding platforms are an online ecosystem where beginners or non-technical users can develop bots without coding. A zero-coding platform is therefore highly suitable since it allows one to easily deploy a chatbot for specific business needs and customize the end-product with minimal technical knowledge.

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Most popular platforms and frameworks for building open-source chatbots

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Any Device. Any Platform. Any Channel. Any Backend - A.ware

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Building your own solution using an open source conversational AI platform is not always a good fit when you are investing nearly as much time and resources to train and deploy the chatbot after development. An AI platform with built-in domain knowledge, therefore, comes handy for implementing enterprise-grade enterprise intelligent bots. Versatility, flexibility, scalability, and ease of adaptation are some of the added features that can be the cherry on top.

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Senseforth.ai checks all the boxes with the proprietary A.ware platform, which offers multiple deployment options - cloud, on-premise or even a hybrid of the two. It is embedded with various pre-built connectors to different channels and allows you to build generative model smart bots with deep learning capabilities that minimize the need for human management over time.

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A truly equipped platform with specific analytics modules to track user conversations, customer profiles, agent efficiencies, quality of response, and numerous other parameters – A.ware transforms you into a conversational enterprise capable of deploying several bots across multiple touchpoints in just a few weeks.

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