Discover strategies for navigating the evolving landscape of specialty chemical expertise.
Sales AI
March 5, 2024
We live in a world where Google fetches us millions of preference-based search results in milliseconds. So does it really make sense to deal with the typical, naive enterprise search tools at your workplaces? Or worse — depend on Ctrl+F to open docs one-by-one and find information in your company records.
Enterprise data comprises numerous files, folders, presentations, videos & other documents. If you go on & sum up the time spent searching for technical information through these documents, the number will be tremendous. Studies by McKinsey show employees spend over 1.8 hours every day on search; this means 9+ hours in a week, i.e., more than one complete workday. And it doesn’t end there. You need to scrutinize your private data regularly & compare it with your competitor’s facts & figures available on the web to make insightful decisions.
Technical searches at workplaces need precise answers, which our conventional enterprise search tools fail to provide.
From hand-written records to sophisticated software like IBM Watson for organization data storing & management, we’ve come a long way. But even so, we have no software that could simplify manual work to search, filter, gather & analyze information.
The enterprise search software can store & search through millions of an organization’s files & folders, but with no context. Be it technical or non-technical search, the tool will treat every query the same. Thus, most of the results it presents are generic & not accurate, and users have to search through all of them to find the information they are looking for. Here, big data backfires and what we truly need is the small data to personalize our search experience. Below are some challenges that conventional enterprise search tools can not solve.
You and your enterprise search tool are always on two different frequencies. The technical language you use makes little sense to it; no matter how much you try, it is always going to get you generic answers to your technical questions. The best they can do is generate synonyms and use semantic parsing, but they don’t have any domain understanding. This is because enterprise search tools are designed to be generic, not specific. They cannot understand your domain & language, or provide you with a personalized experience.
Browsing through company data using enterprise search software can be like finding a needle in a haystack. As it cannot understand your technical question, it will fetch you all the documents based on your keyword. But most of them will not be closely related to your search intent. Google is able to generate millions of results for a question and still have the desired answer on the first page because of the billions of data points it gathers and the extensive knowledge graph it maintains. Enterprise search engines don’t have that scale, so they can’t provide a similar experience. And organization data can be finite yet huge. So, if you still have to browse through thousands of links and files to find the desired information, is your enterprise search engine really helping?
Most enterprise search tools are limited to the organization’s private information, whereas your work is not. You need to keep a track of your competitor’s information, including their profits, CSR initiatives & more, to develop a winning business strategy. For this purpose, you will have to rely on some other tool to search through the publicly available data.
Lastly, a conventional enterprise search tool can not interact like humans. If you ask your co-worker for information, they might counter you with a question to explain your requirement precisely. Thus, you often require the expertise of analysts, engineers, and data scientists who have the knowledge and ability to process your question. They might also be able to analyze the project you’re working on and accordingly generate a report that is accurate & relevant to the context.
But what if your enterprise search can provide you with human-like assistance? What if you can have a virtual subject matter expert at your fingertips throughout your day to make your work easy?
Nesh is a one-of-its-kind application that is here to revolutionize your search experience. You can think of Nesh as Google with an Engineering and Business degree. She is a smart & self-learning conversational AI assistant for Search and Analytics. She can save your time by extracting meaningful data on a click and analyzing it to facilitate insightful decision-making. Let’s explore how.
Nesh understands the domain language spoken in the industries like Chemical, Power, Oil and Gas, Mining, Banking, and more. Nesh learns about the domain by reading books, glossaries, existing ontologies, reference literature, and other training material. She uses this domain understanding to find rich and useful answers within the data without the user having to tag and train the model.
Nesh is a conversational AI that processes your question and responds with suitable options to understand your requirement efficiently. She narrows down the results instead of providing anything & everything related to your search. Many times, we are not able to pinpoint the exact information we need. Nesh fills in this gap between vague questions & the right answer.
She will ask you questions to filter out all the irrelevant information and find exactly what you are looking for. For instance, if you ask her for information about a company, a conventional enterprise search engine will get all the related information available. But Nesh comes back with possible options like accounts payable, acquisitions, business risks & more.
Nesh is continuously improving & getting better. The more you interact with her, the smarter she gets. She learns from your questions, your reinforcements, your data, and has the ability to connect the dots between various data points through logical reasoning.
Typically, employees have to rely on two different tools to gather private & public information. Nesh simplifies this process through a user-defined data universe. She can not only connect to private data like internal documents, powerpoints, etc. but can also include public data like websites, market intel reports. You can define Nesh’s data universe to make her search for the data that you care about. This eliminates the generic big data and simplifies your work life with the precision of small data. With Nesh, you can find all you need in one place.
Discover strategies for navigating the evolving landscape of specialty chemical expertise.
Successful AI implementation boils down to answering a handful of key questions.
In their basic format, search engines operate on Information Retrieval (IR) principles and are commonly used for search.
