I’ve spent hours trying to figure out why the last_updated timestamp in the bulk export JSON payload for WhatsApp interactions differs from the start_time recorded in the conversation header when filtering for legal discovery requests. The environment is Genesys Cloud v2024.11 in the EMEA region, utilizing the /api/v2/analytics/conversations/details/query endpoint with a custom date range filter spanning 48 hours. The bulk export job completes successfully with a 200 OK status, but upon ingestion into our S3 bucket via the Data Action, a Python script validates the chain of custody metadata. The script flags approximately 12% of records where the last_updated field is exactly 300 milliseconds ahead of the start_time for single-turn interactions. This discrepancy breaks our automated hash verification process, which expects the last_updated timestamp to be equal to or greater than the start_time but not exceed it by more than network latency thresholds defined in our compliance policy. The Architect flow handles the digital channel routing correctly, and the NLP intent extraction is populating as expected, so the issue seems isolated to the analytics reporting layer or the bulk export aggregation logic. We have verified the system clock synchronization on our local servers and confirmed the S3 upload metadata is accurate. The error does not occur with voice recordings, only digital channels. We need to ensure the timestamp integrity for legal hold purposes, as any discrepancy could be challenged during discovery. Has anyone encountered this specific timestamp drift in digital channel exports? Is this a known behavior in the analytics API when processing high-volume chat sessions, or is there a specific parameter in the query body that forces strict timestamp alignment? We are using the Genesys Cloud Python SDK v3.12.0 for the export initiation. Any insights into the underlying aggregation mechanism would be appreciated to adjust our validation script accordingly.