Kodiak AI, Inc.: investing cash flow
Investing cash flow for Kodiak AI, Inc. Inspect selected reporting periods, original units and SEC filing links; download the financial history.
All Kodiak AI, Inc. financial histories
What this measure means
Net cash from investing activities, including asset purchases, disposals and investment transactions. This differs from capital expenditure payments alone.
Exact concept: us-gaap:NetCashProvidedByUsedInInvestingActivities. 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 2022-01-01 to 2025-12-31. The SEC response was captured on 2026-09-21.
Selected filing history
| Period start | Period end | Value | Unit | Filed | Source filing |
|---|---|---|---|---|---|
| 2025-01-01 | 2025-12-31 | -91,432,000 | USD | 2026-03-11 | 10-K · 0001628280-26-016910 |
| 2024-01-01 | 2024-12-31 | -3,212,000 | USD | 2026-03-11 | 10-K · 0001628280-26-016910 |
| 2023-01-01 | 2023-12-31 | 17,278,000 | USD | 2026-03-11 | 10-K · 0001628280-26-016910 |
| 2022-01-01 | 2022-12-31 | 0 | USD | 2024-02-28 | 10-K · 0001628280-24-007635 |
Related financial histories
- Kodiak AI, Inc.: total assets
- Kodiak AI, Inc.: total liabilities
- Kodiak AI, Inc.: stockholders equity
- Kodiak AI, Inc.: cash and cash equivalents
- Kodiak AI, Inc.: net income or loss
- Kodiak AI, Inc.: operating cash flow
- Kodiak AI, Inc.: capital expenditure payments
- Kodiak AI, Inc.: contract revenue excluding tax
- Kodiak AI, Inc.: financing cash flow
- Kodiak AI, Inc.: retained earnings or deficit
- Kodiak AI, Inc.: basic weighted-average shares
- Kodiak AI, Inc.: diluted weighted-average shares
- Kodiak AI, Inc.: basic earnings per share
- Kodiak AI, Inc.: diluted earnings per share
- Kodiak AI, Inc.: income tax expense or benefit
- Kodiak AI, Inc.: share-based compensation expense
- Kodiak AI, Inc.: operating income or loss
- Kodiak AI, Inc.: current assets
- Kodiak AI, Inc.: current liabilities
- Kodiak AI, Inc.: operating expenses
- Kodiak AI, Inc.: research and development expense
Inspect the source
- Entity
- Kodiak AI, Inc. / CIK 0001853138
- Captured
- 2026-09-21T17:32:42.142Z
- SEC response SHA-256
0422ae287d39d7f3db6379481a4e3c14ba233deafd5fe6c0a308fda805d699df
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
Read the published dataset with Python
import json
from urllib.request import urlopen
with urlopen("https://canlicapital.com/company-data/0001853138.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"])))