AI Financial Corporation: gross profit

At a glance

AI Financial reported gross profit of $10.2 million for the year ending 2025-12-27, up 80.4% from $5.65 million a year earlier. Across the last five reported years it ranged from $0 (2022-12-31) to $10.2 million (2025-12-27). That was 41.0% of revenue ($24.8 million) for the same year.

Computed from the values in the table below, in USD; each links to the filing it came from.

What this measure means

Revenue less the costs directly attributed to the goods or services sold. It precedes other operating expenses and is not net income.

Exact concept: us-gaap:GrossProfit. Each value covers an annual-duration reporting interval, shown with both start and end dates. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2010-01-03 to 2025-12-27. The SEC response was captured on 2026-09-20.

Selected filing history

Related AI FINANCIAL CORPORATION histories

• Revenue
• Contract revenue excluding tax
• Cost of revenue
• Operating expenses
• Selling, general and administrative expense
• Operating income or loss
• Total assets
• Total liabilities

All 66 AI Financial histories

Inspect the source

Entity

AI FINANCIAL CORPORATION / CIK 0000862861

Captured

2026-09-20 04:59 UTC

SEC response SHA-256

b5e952a29c52e609e4d4079dcc4d7c2058d4209c591c2d8251622c54f17e895f

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

Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.

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

The MCP server is free and MIT-licensed: npx -y canli-mcp. If these records help your research, a star on GitHub helps other researchers find them.

import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0000862861.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"])))