Why Businesses Need AI Logs

• Quality Control: see where AI performed successfully and where it failed.
• Cost Control: tokens and cost estimates help plan the budget.
• Team Transparency: easily track who ran what.
• Quick Recovery: erroneous results can be reviewed and applied manually.

What's on the Page

1. Period filter (today, 7 days, 30 days, custom).
2. Summary KPIs: calls, tokens, total cost estimate.
3. Model statistics (calls, tokens, estimated cost).
4. Detailed log table by operations.
5. Modal window with full input/output text.

Buttons and Their Functions

• "AI Settings" - go to the AI settings center.
• "Clear Old Logs" - deletes logs older than 30 days.
• "Apply" - runs the filter for the selected period.
• "Reset" - resets the filter to "Today" mode.
• "Show" - opens details of a specific log.
• "Upload to Database" - records AI result in the database for a task with error/manual confirmation.
• "Close" - closes the details modal window.

Step-by-Step Workflow

1. Select the analysis period (e.g., last 7 days).
2. Click "Apply".
3. Check KPIs: number of calls, tokens, cost estimate.
4. In the models block, see which models bear the main load.
5. In the logs table, find entries with "Failure" or "In Progress" status.
6. Click "Show" to open full details.
7. If needed, click "Upload to Database" and confirm the application of the result.

Practical Tips

• Review model statistics and cost estimates weekly.
• After changing the model, be sure to check how it affected tokens and quality.
• For problematic cases, record the error type and update the prompt in the AI center.
• Do not accumulate unnecessary logs: periodically run old record cleaning.

What It Brings to Business

• Transparent control of AI operations in daily work.
• Timely error detection and quick correction.
• Better control over AI costs.
• More stable content quality when scaling.