Hi all,
I am encountering a persistent data inconsistency when retrieving transcript data via the /api/v2/analytics/conversations/ai/summaries endpoint for calls that originated through our BYOC trunks. We manage 15 BYOC trunks across various regions, and while our US and EU trunks return populated transcripts arrays with accurate speaker labels and timestamps, our APAC trunks (specifically Singapore and Japan) are returning empty arrays or null values for the text field, despite the AI Bot engagement being logged correctly in the conversation summary.
The environment details are as follows:
- Genesys Cloud Version: Latest (as of late 2023)
- Region: APAC (Singapore)
- Integration: BYOC Trunks using standard SIP credentials
- AI Bot: Custom intent-based bot deployed globally
We have verified that the SIP audio streams are healthy and that the call quality metrics (MOS) are within acceptable ranges. Furthermore, manual verification of the call recordings confirms that the AI Bot did indeed engage and process the interaction. The issue appears to be specific to the transcription pipeline for traffic entering via our custom SIP trunks in the APAC region.
Has anyone else experienced similar latency or data omission issues with transcript retrieval for BYOC traffic in APAC? I am wondering if there is a known configuration mismatch in the region-specific media servers or if the AI Bot requires a specific routing rule to ensure the audio stream is tagged correctly for transcription when coming from a BYOC source. Any insights into potential carrier-specific quirks or API limitations would be appreciated.
This behavior is almost certainly a region-specific latency issue in the ASR pipeline rather than a data loss event. When using BYOC trunks, the audio stream must traverse the public internet to reach the Genesys Cloud ASR engine before the transcript is generated. For APAC regions, this round-trip can exceed the default timeout window for the analytics/conversations/ai/summaries endpoint, causing the API to return before the transcription job completes.
To resolve this, you should implement a polling mechanism in your ServiceNow integration rather than a single synchronous call. In your Data Action configuration, set the retry parameter to handle transient 202 Accepted or empty 200 OK responses. Specifically, configure the Data Action to wait 5 seconds between attempts, with a maximum of 10 retries.
Here is the recommended JSON structure for the ServiceNow REST message body when invoking the AI summary endpoint:
{
"conversationId": "${conversation.id}",
"retryCount": 0,
"maxRetries": 10,
"delaySeconds": 5,
"headers": {
"Authorization": "Bearer ${access_token}"
}
}
Additionally, ensure that your BYOC trunk configuration in Genesys Cloud explicitly routes media to the nearest ASR region (e.g., ap-southeast-1 for Singapore). If the media is being routed to a distant region for processing, the delay will be compounded. Check the mediaRegion setting in your trunk configuration under Admin > Telephony > Trunks. If the region is set to auto, it may default to a non-optimal location for APAC traffic. Explicitly setting the media region to match your primary APAC deployment will significantly reduce transcription latency.
While the latency hypothesis is technically plausible, we must consider the architectural implications of relying on asynchronous API polling for critical compliance and quality assurance workflows. In my experience managing performance dashboards for EU-West tenants, waiting for background transcription jobs to complete introduces unacceptable variability into our reporting cycles. If the API returns null because the job is still processing, our downstream analytics in Tableau or Power BI fail to capture the conversation context, leading to gaps in our agent performance metrics.
Instead of adjusting timeouts or implementing complex retry logic at the API level, I recommend evaluating the Conversation Detail View within the Genesys Cloud Admin interface as the primary source of truth for immediate verification. The UI often caches the transcript status more reliably than the raw API endpoint during high-latency periods. For automated workflows, you might consider leveraging the Architect Flow to trigger a webhook only after the conversation.status changes to completed and explicitly checking for the presence of the transcript object in the payload. This ensures that you are acting on finalized data rather than partial states.
Furthermore, have you verified the Speech Analytics settings in your APAC tenant? Sometimes, region-specific configurations for language models or confidence thresholds can silently suppress transcript data if the ASR engine cannot meet a minimum confidence score. This is distinct from a timeout and often manifests as empty arrays rather than errors. Checking the System Logs for ASR-specific warnings in the APAC region would provide more definitive evidence than assuming network latency. We have seen similar divergences in our EU flows where strict compliance rules filtered out low-confidence speech segments, resulting in empty transcript fields despite successful audio routing.