corporateSupporting knowledge utilization issues with AI. No-code report builder feature also available

Cinnamon AI is an AI equipped with a no-code report builder function that allows companies to easily collect corporate knowledge accumulated in various data formats, such as audio, images, and videos, and use it to develop new services and improve business efficiency. We have developed the product ``Knowledge Collector'' and will begin providing a beta version for companies today, July 6th.The beta version is free and applications are accepted from our website.

Download the “Knowledge Collector” introduction materials Click here / Apply for the free beta version Click here

"Knowledge Collector" is a knowledge collection tool that combines AI technologies such as AI voice recognition, AI-OCR, and large-scale language models, and is characterized by the ability to easily collect (input) information from your smartphone or PC at any time. Data (knowledge) can be collected in a variety of ways, including not only text input, but also voice text input using easier AI voice recognition, automatic extraction of text information from images such as handwriting and printed text using AI-OCR technology, and more. Supports photo and video recording, barcode input, etc. as evidence.

In addition, the types of knowledge information and reporting formats vary depending on department, team, and job type within a company, but Knowledge Collector's no-code report builder function allows you to easily and freely create templates, so it can be used in a wide range of industries. It can be implemented in any industry.

In the future, by utilizing large-scale language models on data converted into text by AI voice recognition, we aim to organize information on the content and implement a suggestion function.
*Please note that some functions may not be available in the free beta version.

■Development background: Utilize AI to minimize the burden of knowledge collection and maximize its utilization
The knowledge accumulated by Japanese companies, many of which have been around for 100 years, is vast. On the other hand, many companies have an urgent need to efficiently pass on and utilize knowledge due to factors such as aging workers, declining population, and delays in introducing DX. In addition, in a variety of occupations, skills and know-how are dependent on the individual, and there is a current situation where valuable knowledge cannot be absorbed. Another barrier to knowledge collection for companies is that the act of "writing and reporting" itself, whether by handwriting or using a keyboard, is a burden on the person in charge, and because of this, there are cases in which knowledge cannot be collected effectively. there is.

In order to solve these issues, Cinnamon AI has developed a "Knowledge Collector" that allows you to easily input information using various methods. "Knowledge Collector" reduces the input burden on staff, and the ability to input voice input on the spot is expected to improve the quantity and quality of information. In addition, it eliminates paper records that are often found in labor-intensive industries, leading to paperless and digitalization.

■ Introduction image of “Knowledge Collector” by industry
[Insurance/Finance/Pharmaceuticals]
Daily business report / Adjuster business report / Information sharing

[Manufacturing industry]
Trouble report / Inspection record / Daily work report / Near miss report / Daily awareness
Maintenance patrol records / submission / manual creation / research records / sensory evaluation

[Logistics industry]
Business report/transfer/manual creation/near miss report

[Maintenance business]
Maintenance work record

[Retail]
Customer opinions/daily business report

■ Examples of knowledge utilization
New service planning / Succession of know-how / Detection of human error / Unification of judgment criteria / Early detection / Productivity improvement /
Sophistication of planning / load reduction / sharing of information / improvement of communication / resolving human resource shortages through task shifting, correcting long working hours, and improving the quality of work

■ UI image of “Knowledge Collector”

■ Implementation of large-scale language model (LLM) collaboration function
By using a large-scale language model, it is possible to organize and suggest data that has been converted into text using AI speech recognition. For example, it can point out missing items from the contents of a daily business report, suggest the next action based on the business negotiation, or use the contents of a near-miss report to identify "when," "where," "what," "what happened," and "what happened". We aim to implement a function that organizes information by classifying it into categories such as "cause".

▽Function example
-Optional provision of preset LLM collaboration templates
-Functions for business daily report templates
Correcting typos / Summary / Bullet points / Next action proposal / Positive/negative judgment / Pointing out missing elements in the daily business report
-Function for near-miss report templates
Correction of typos/summary/classification (when, where, what happened, what happened, cause)
-Function for business report/application templates
Typo correction/summary/bullet points