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iLearningEngines (iLearningEngines) Debt-to-Revenue : 0.08 (As of Jun. 2023)


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What is iLearningEngines Debt-to-Revenue?

Debt-to-Revenue measures a company's ability to pay off its debt.

iLearningEngines's Short-Term Debt & Capital Lease Obligation for the quarter that ended in Jun. 2023 was $10.7 Mil. iLearningEngines's Long-Term Debt & Capital Lease Obligation for the quarter that ended in Jun. 2023 was $21.2 Mil. iLearningEngines's annualized Revenue for the quarter that ended in Jun. 2023 was $390.5 Mil. iLearningEngines's annualized Debt-to-Revenue for the quarter that ended in Jun. 2023 was 0.08.

iLearningEngines Debt-to-Revenue Historical Data

The historical data trend for iLearningEngines's Debt-to-Revenue 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 Debt-to-Revenue Chart

iLearningEngines Annual Data
Trend Dec20 Dec21 Dec22
Debt-to-Revenue
- 0.06 0.06

iLearningEngines Semi-Annual Data
Dec20 Dec21 Jun22 Dec22 Jun23
Debt-to-Revenue N/A N/A - 0.05 0.08

Competitive Comparison of iLearningEngines's Debt-to-Revenue

For the Software - Infrastructure subindustry, iLearningEngines's Debt-to-Revenue, along with its competitors' market caps and Debt-to-Revenue data, can be viewed below:

* Competitive companies are chosen from companies within the same industry, with headquarter located in same country, with closest market capitalization; x-axis shows the market cap, and y-axis shows the term value; the bigger the dot, the larger the market cap. Note that "N/A" values will not show up in the chart.

iLearningEngines's Debt-to-Revenue Distribution in the Software Industry

For the Software industry and Technology sector, iLearningEngines's Debt-to-Revenue distribution charts can be found below:

* The bar in red indicates where iLearningEngines's Debt-to-Revenue falls into.


iLearningEngines Debt-to-Revenue Calculation

Debt-to-Revenue measures a company's ability to pay off its debt.

iLearningEngines's Debt-to-Revenue for the fiscal year that ended in Dec. 2022 is calculated as

Debt-to-Revenue=Total Debt / Revenue
=(Short-Term Debt & Capital Lease Obligation + Long-Term Debt & Capital Lease Obligation) / Revenue
=(8.138 + 9.713) / 309.17
=0.06

iLearningEngines's annualized Debt-to-Revenue for the quarter that ended in Jun. 2023 is calculated as

Debt-to-Revenue=Total Debt / Revenue
=(Short-Term Debt & Capital Lease Obligation + Long-Term Debt & Capital Lease Obligation) / Revenue
=(10.686 + 21.202) / 390.45
=0.08

* 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.

In the calculation of annual Debt-to-Revenue, the Revenue of the last fiscal year is used. In calculating the annualized quarterly data, the Revenue data used here is two times the quarterly (Jun. 2023) Revenue data.

iLearningEngines Debt-to-Revenue Related Terms

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iLearningEngines (iLearningEngines) Business Description

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GURUFOCUS.COM » STOCK LIST » Technology » Software » iLearningEngines Inc (NAS:AILE) » Definitions » Debt-to-Revenue
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Traded in Other Exchanges
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Address
6701 Democracy Boulevard, Suite 300, Bethesda, MD, USA, 20817
Website
https://www.ilearningengines.com
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.