Global AI, Inc.: 10-Q filed 2023-08-08

This filing

Form

10-Q (quarterly report)

Filed

2023-08-08

Fiscal period

fiscal Q3 2023

Accession

0001493152-23-027128 on SEC EDGAR

Values are as tagged in this filing. A later filing can restate them; each measure links to its history page, which shows the latest-filed value per period. All Global AI, Inc. filings.

Reported measures

Inspect the source

Entity

Global AI, Inc. / CIK 0001473490

Captured

2026-09-20T09:07:52.351Z

SEC response SHA-256

b3c8233dbad8337f1890ab8018c7722ce5fea933dbc84059d62162eb896c7fe5

Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON

Every published concept a filing tagged, with the periods it covered, as reported in that filing at capture time. Forms 10-K, 10-K/A, 10-Q, 10-Q/A, 20-F, 20-F/A, 40-F, 40-F/A. A filing page needs at least 8 published concepts. Later filings can restate these values; the company history pages show the latest-filed value per period.

Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.

Use this in research

A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.

These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.

Research methodology · Execution and cost assumptions · Check backtest overfitting

Build with the open-source tools

Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.

• Get an API key and run your first validation
• Connect the MCP server to your coding assistant
• Inspect the ALPHAC engine on GitHub
• Read the MCP server source and integration examples

import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0001473490.json") as response:
    record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
    print(concept["tag"], next(iter(concept["observations"])))