Tempus AI, Inc.: net income or loss
Net income or loss for Tempus AI, Inc. Inspect selected reporting periods, original units and SEC filing links; download the financial history.
All Tempus AI, Inc. financial histories
What this measure means
Reported profit or loss for the period. Check the filing for attribution, exceptional items and discontinued operations before comparing companies.
Exact concept: us-gaap:NetIncomeLoss. 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 | -245,028,000 | USD | 2026-02-24 | 10-K · 0001193125-26-066961 |
| 2024-01-01 | 2024-12-31 | -705,809,000 | USD | 2026-02-24 | 10-K · 0001193125-26-066961 |
| 2023-01-01 | 2023-12-31 | -214,118,000 | USD | 2026-02-24 | 10-K · 0001193125-26-066961 |
| 2022-01-01 | 2022-12-31 | -289,811,000 | USD | 2025-02-24 | 10-K · 0000950170-25-025603 |
Related financial histories
- Tempus AI, Inc.: total assets
- Tempus AI, Inc.: total liabilities
- Tempus AI, Inc.: stockholders equity
- Tempus AI, Inc.: cash and cash equivalents
- Tempus AI, Inc.: operating cash flow
- Tempus AI, Inc.: capital expenditure payments
- Tempus AI, Inc.: contract revenue excluding tax
- Tempus AI, Inc.: financing cash flow
- Tempus AI, Inc.: investing cash flow
- Tempus AI, Inc.: retained earnings or deficit
- Tempus AI, Inc.: basic weighted-average shares
- Tempus AI, Inc.: diluted weighted-average shares
- Tempus AI, Inc.: basic earnings per share
- Tempus AI, Inc.: diluted earnings per share
- Tempus AI, Inc.: income tax expense or benefit
- Tempus AI, Inc.: net property, plant and equipment
- Tempus AI, Inc.: share-based compensation expense
- Tempus AI, Inc.: operating income or loss
- Tempus AI, Inc.: current assets
- Tempus AI, Inc.: current liabilities
- Tempus AI, Inc.: current accounts payable
- Tempus AI, Inc.: goodwill carrying amount
- Tempus AI, Inc.: net current accounts receivable
- Tempus AI, Inc.: net inventory
- Tempus AI, Inc.: selling, general and administrative expense
- Tempus AI, Inc.: research and development expense
Inspect the source
- Entity
- Tempus AI, Inc. / CIK 0001717115
- Captured
- 2026-09-21T17:24:40.778Z
- SEC response SHA-256
ff4c427aa528a3a5d2e60b2f8d5f49fa45ba6c291f8c47c0789a98289048887b
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/0001717115.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"])))