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iLearningEngines (iLearningEngines) Short-Term Debt : $10.7 Mil (As of Jun. 2023)


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What is iLearningEngines Short-Term Debt?

iLearningEngines's Short-Term Debt for the quarter that ended in Jun. 2023 was $10.7 Mil.

iLearningEngines's quarterly Short-Term Debt increased from Jun. 2022 ($0.0 Mil) to Dec. 2022 ($8.1 Mil) and increased from Dec. 2022 ($8.1 Mil) to Jun. 2023 ($10.7 Mil).

iLearningEngines's annual Short-Term Debt increased from Dec. 2020 ($0.0 Mil) to Dec. 2021 ($3.5 Mil) and increased from Dec. 2021 ($3.5 Mil) to Dec. 2022 ($8.1 Mil).


iLearningEngines Short-Term Debt Historical Data

The historical data trend for iLearningEngines's Short-Term Debt can be seen below:

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.

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iLearningEngines Short-Term Debt Chart

iLearningEngines Annual Data
Trend Dec20 Dec21 Dec22
Short-Term Debt
- 3.45 8.14

iLearningEngines Semi-Annual Data
Dec20 Dec21 Jun22 Dec22 Jun23
Short-Term Debt - 3.45 - 8.14 10.69

iLearningEngines Short-Term Debt Explanation

Short-Term Debt represents the total amount of Long-Term Debt such as bank loans and commercial paper, which is due within one year.


iLearningEngines Short-Term Debt Related Terms

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iLearningEngines (iLearningEngines) Business Description

Comparable Companies
Traded in Other Exchanges
N/A
Address
6701 Democracy Boulevard, Suite 300, Bethesda, MD, USA, 20817
iLearningEngines Inc is an AI and automation platform that empowers its customers to productize their institutional knowledge by transforming it into actionable intellectual property that enhances outcomes for employees, customers and other stakeholders. Its platform enables enterprises to build intelligent Knowledge Clouds that incorporate large volumes of structured and unstructured information across disparate internal and external systems and to automate organizational processes that leverage these Knowledge Clouds to improve performance. The company combines its offerings with vertically focused capabilities and data models to operationalize AI and automation to effectively and efficiently address critical challenges facing its customers.