JangIT.ai Coming Soon
About JangIT
The Problem
One of the largest problems facing the modern world is that there is more and more information available to all of us at our fingertips. The problem is that all of us are faced with more and more time constraints. One of the most valuable tools that can be provided is for a person to have the ability to quickly and accurately sift, sort, and consolidate information into an amount that is consumable and usable for the individual’s particular purpose.
Our Solution
The beauty of Jangit is the licensed patented “media summarizer” process. The licensed algorithm can be customized by the user for their specific purpose. For example, a scholarly article on the latest techniques in brain surgery would have interest and utility to varying degrees with different parties. A brain surgeon would want to read the article in its entirety. A general medical doctor would probably want to have the article 23 consolidated by about 50 or 60%. Someone who is interested in science in general may want to have it consolidated by 80%. And someone who is only passively interested would likely only want to capture the main points so they could have it consolidated by 95%.
A Jangit subscriber will be able to synthesize anything of interest to varying degrees. One version has been tested and deployed on a limited release in beta version. There are five additional products that utilize patented algorithm that are in production. The media summarizer is currently 80% complete. The remaining steps to have a commercial product include placing the product on a completed webpage and finalizing the programming of the links and key word search features. This product will be able to be completed with an infusion of $3,000,000 and six months of programming. After we complete these steps, we will create an app and begin our marketing efforts of the product and associated app to college students and professionals.
Products
The JangIT engine is designed to gather data in multiple formats, standardize that data in a singular searchable format, and summarize data using custom rules. JangIT involves a three-step process which includes (1) Translation, (2) Mapping, and (3) Shaping.
During translation, data will be uploaded or scraped from documents, URLs or website. Data may consist of any readable source online or in digital form, including images. The JangIT Engine will then take that information and turn it into a standard language that can be valued based on its type, content, styles, font, punctuation and wording. Those values are assigned a standard set of binary rules for future use. Finally, the data is stored on the database for current and future need.
Mapping begins as the translated data is recalled and analyzed to the specification of the engine. The data is then valuated based on the parameters given in the engine to a set of standards. Each piece of data is given a value that can be qualified. The final data is then ready for shaping.
Now that the engine has accomplished a task of standardizing all of your data, you can now apply standard filters to the documents. You can apply your own parameters and focal points on each document or the bundle of documents as a whole. The final product is a set of documents or data that is fine tuned to your specification. The entire research model can be exported in one final draft.
Our Team & Partners
Kelly Kirchhoff - Chief Executive Officer & Chairman of the Board
Mr. Kirchhoff has had a varied career in leadership since his graduation with an Associate’s Degree from Iowa Western Community College in 1987. Most recently, from 2014 to 2020, he served as a sales and software manager for Digital Research Solutions. He has been the CEO of Digital Research Solutions, Inc. and still serves in that capacity. Before that, he was the Chief Executive Officer of Smarbee, Inc., an international software company. Before 2005, he worked as a financial advisor, broker, marketer, and farm manager.
Mr. Kirchhoff has had a varied career in leadership since his graduation with an Associate’s Degree from Iowa Western Community College in 1987. Most recently, from 2014 to 2020, he served as a sales and software manager for Digital Research Solutions. He has been the CEO of Digital Research Solutions, Inc. and still serves in that capacity. Before that, he was the Chief Executive Officer of Smarbee, Inc., an international software company. Before 2005, he worked as a financial advisor, broker, marketer, and farm manager.
Scott Reid Chief - Chief Technology Officer & Board Member
Scott Reid is a Data Engineer, Software Engineer, and Business Leader with over 20 years of experience in the technology industry. He is passionate about data structures, AI, and software algorithms to solve real-world problems and drive business growth, with a strong foundation in Software Engineering, Data Science, and Business Analytics. He is proficient in 16 software languages and has vast experience in various data science tools and techniques, including machine learning, natural language processing, and data visualization.
Scott has been the lead on a variety of projects in the Technology Industry, including the development of Point-of-Sale Systems, Automated AI inventory control systems, Electronic Medical Record Systems, Insurance Electronic Data Interchange Systems (EDI), Merchant Gateway systems and Terminals, Automated Video Production Systems, Full Data Center control systems, and Infrastructure Security Systems. His knowledge extends into many industries, including the Healthcare, Dental, Automotive, Finance (FinTech), Data Center, Retail, and Entertainment industries.
Scott has brought many software products and companies from the startup stage to the maturity stage and even facilitated the sale of many products and companies. He has a strong insight into business analytics and tying those metrics to opportunities that have led to business growth..
Identifies as JangIT.ai
JangIT is the mastermind behind the JangIT AI Technology. JangIT has big data analytic algorithms (BDAA) and tools and technology like "Artificial Intelligence (AI) running through its titanium & carbon fiber shell. JangIT's "Machine Learning (ML) technology experience is like no other. JangIT has outperformed its human counterpart in every possible area.
JangtIT's favorite quote:
I fear the day when technology overlaps with our humanity.
The world will only have a generation of idiots.
"Albert Einstein"
The JangIT Solution
Search Summarization
Search Summarization
Search Summarization
- Search summarization, also known as query-based or search result summarization, is a natural language processing (NLP) task that generates concise and coherent summaries of search engine results in response to a user's query. The goal is to provide users with a brief and informative overview of the most relevant documents or web pages ret
- Search summarization, also known as query-based or search result summarization, is a natural language processing (NLP) task that generates concise and coherent summaries of search engine results in response to a user's query. The goal is to provide users with a brief and informative overview of the most relevant documents or web pages retrieved by the search engine, allowing them to quickly assess the content's relevance without clicking on individual links.
- Search summarization aims to save users time and improve their search experience by providing them with a snapshot of the content within each search result. It helps users make informed decisions about which links to click on for more detailed information. Additionally, search engines may use various techniques, including NLP and machine learning, to improve the quality and relevance of these summaries.
Process
Search Summarization
Search Summarization
Here's how search summarization typically works:
- User Query: A user enters a query or search term into a search engine, such as Google or Bing.
- Retrieval: The search engine retrieves a list of documents or web pages that are potentially relevant to the query.
- Summarization: Search summarization algorithms analyze the content of these search
Here's how search summarization typically works:
- User Query: A user enters a query or search term into a search engine, such as Google or Bing.
- Retrieval: The search engine retrieves a list of documents or web pages that are potentially relevant to the query.
- Summarization: Search summarization algorithms analyze the content of these search results and generate concise summaries for each result. These summaries usually consist of a title, snippet, or key sentences from the document.
- Presentation: The search engine presents the summarized search results to the user, often in a list format. Users can quickly scan these summaries to determine which results are most likely to contain the information they need.
JangIT Step 1
Search Summarization
JangIT Step 1
At JangIT, we envision a world where technology is used to solve the most pressing challenges facing humanity.
JangIT Step 2
JangIT Step 3
JangIT Step 1
Our team has expertise in a range of technical areas including artificial intelligence, robotics, and environmental science.
JangIT Step 3
JangIT Step 3
JangIT Step 3
We take a collaborative approach to every project, working closely with our clients to ensure their needs are met and their expectations are exceeded.
JangIT Step 4
JangIT Step 3
JangIT Step 3
We measure our success not only by the quality of our work but also by the positive impact it has on our clients and the wider community.
Our Growth Strategy “Freemium”
Our Growth Strategy “Freemium”
Our focus groups have shown that consumers respond well to a "freemium" pay model where the essential services are "free," 1, and the consumer can either expand the service with a pay-per-use or subscription fee model. Each month, the consumer would have up to two summaries at no cost to the consumer. If the user needs more outlines, they can elect to use a pay-per-use model or a subscription-based model at a more significant cost savings to the user.
Our focus groups have further shown that consumers will continue to use the service with up to three (3) free summarizations per month and are willing to pay up to $0.99 per additional summarization. Finally, our study shows that tiered subscription models are preferred for consumers, beginning at .99, then $4.99, and finally $9.99 per month, depending on usage.
Competition
Publishers of textbooks and other academic resources have always struggled with the amount of content they've been able to curate for the end user. Often, publishers and content creators will publish summaries and abstracts of their work; the most popular of these summaries are the infamous "Cliff Notes," which is ubiquitous in academia.
The content creator and consumer are limited to the content the creator is willing to summarize. To our knowledge, no competition allows the content creator and the content consumer to create a custom summary that can summarize virtually any content and customize it to the user's specific needs.
Our Partners
