Intelligent Content Processing for Chatbots

robot, content, AI, android

Scientia potentia est or Knowledge is Power.”

Whenever an enterprise is working with a large pool of data, the requirement arises for Intelligent Content Processing. Often, enterprise-sized organisations will have thousands of documents shared on Sharepoint or similar data hubs. But what good is data and content that is not in a usable format?

This is where Intelligent Content Processing comes into play. 

What is Intelligent Content Processing?

Intelligent Content Processing via a Document Cognition engine is a way to process unstructured textual data. Document cognition leverages artificial intelligence, machine learning, cognitive science, and natural language processing to index structured, semi-structured, and unstructured data.

When is Intelligent Content Processing via Document Cognition leveraged by a virtual assistant?

When an enterprise is dealing with a massive quantity of documents containing mostly (>70%) text, it incredibly inefficient – and at times impossible – to convert this data into a structured format that can be fed into a system, or a chatbot. In such cases, you can still access the information by feeding it to a document cognition engine, so that a model can take your search phrase and identify the most relevant responses from within these documents. In such cases, a chatbot can take us to the right page/paragraph with ~75-85% accuracy.

How does Intelligent Content Processing via Document Cognition work?

Document Cognition processing via a chatbot can be divided into 3 steps:


The first step involves mapping all the documents from the different sources and the various formats, such as pdf, doc file, system depository, Excel. The automation then indexes each document with a unique identifier code in real-time and maps this unique identifier code to the unique ID of the knowledge management system.


Post mapping, the bot needs to read and understand what the document is talking about. For this, a parser is placed in the bot. The parser identifies every document and its content (even different sections inside the document) in real-time. 

This is similar to how when you search for any keyword on Google, it shows all the documents containing the keyword.


Thirdly, the parsed documents are grouped into entities and then passed through multiple Machine Learning models. 

Benefits of Intelligent Content Processing via Document Cognition

Knowledge Management

A virtual assistant indexes all the unstructured documents and analyses them. This helps enterprises in managing their data and accessing the documents by providing a conversational interface, a chatbot, rather than going through multiple documents to find the right solution. 

Save Time and Effort

Agents and HR staff can spend several hours browsing many documents to answer a customer or an employee. An FAQ chatbot integrated with document cognition can help save a lot of time and effort. This enables the human staff member to focus on more crucial questions.

Reduced risk of regulatory breaches

As the whole knowledge base is saved and automated, it reduces the chances of data being lost or being misfiled.

Two Key players facilitating Intelligent Content Processing via Document Cognition for Enterprises

Yellow Messenger

Yellow Messenger is a leading company in providing cognitive engagement cloud solutions for very big players globally like Indigo, Sephora, Airtel, MG Motor, to name a few. Yellow Messenger is based in Bangalore, India.

Yellow Messenger’s Document Cognition solution is integrated with their AI-enabled Intelligent Virtual agent’s solution. They’re leaders in integrating this with a Chatbot interface, which is very exciting and different from other players, as unlike other solutions in the market, users can engage with the chatbot in a very conversational way and get the answer or document they are looking for. And also most Virtual Assistant providers in the market are leveraging keyword search for document search, whereas Yellow Messenger is leveraging their robust NLP engine for this.

With Yellow Messenger’s Conversational AI Platform, enterprises can connect their data hubs and the platform’s document cognition engine reads through all the data and turns it into Questions and Answers, which can be asked and delivered on a conversational layer: web or pwa app or any messaging channel.

IBM Watson

Another key player in Intelligent Processing is IBM. Their product, IBM Watson, along with Yellow Messenger has been recognised in Gartner Report 2021 Strategic Roadmap for Enterprise AI: Natural Language Architecture for its Natural Language Intelligence. IBM Watson is a question-answering computer system capable of answering questions posed in natural language, developed in IBM’s DeepQA project by a research team led by principal investigator David Ferrucci. IBM Watson performs analytics on vast repositories of data that it processes to answer human-posed questions, often in a fraction of a second.


We live in an instant world, where we expect an instant resolution to all our questions. Even for setting our alarm, we don’t really go to an application and set it manually. We just ask Alexa – to set it for us. Then why should finding documentation be any different? It isn’t anymore, now that chatbots can be harnessed to Intelligent Content Processing.

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