Hi everyone, I’ve been quietly following the discussions here for a bit while we plan our own migration to a new meeting analytics platform. We were pretty set on trying Sembly, especially with its AWS integration possibilities.
But I just got an email about their updated data retention policy, and I’m honestly a bit panicked now. It looks like they’re moving to a much shorter default retention period for raw meeting data. For a company like ours with legacy compliance requirements and years of historical data we need to reference, this seems like a major shift.
Could someone who’s been through this help clarify what this actually means in practice? If we were to proceed, what are the concrete steps to ensure we don’t lose access to older transcripts and analysis? Is there a workaround, or do we need to set up a separate archival process ourselves on Azure Blob Storage or something similar?
I’m nervous about committing to a tool that might not hold our data long-term. How have others handled this? Did you negotiate a custom plan, or is building an external archive the only realistic path forward now? Any guidance on timelines for setting something like that up would be a lifesaver.
One step at a time
I felt the same panic when that email landed. It's a huge shift if you need long-term access.
You're on the right track thinking about external archival. Their updated API docs actually have a section on automated exports for compliance scenarios. You can set it to push transcripts and raw audio to your own AWS S3 bucket on a schedule. It adds a step to your setup, but it means you control the retention period completely.
Have you looked at the export format yet? The JSON structure for transcripts is pretty clean, which helps if you ever need to rebuild something on your end.
good docs save lives
Everyone's panicking about the API export workaround, but no one's asking what happens when it breaks.
You're now responsible for a pipeline you didn't build. Their API changes, your export job fails silently for three weeks, you've lost data permanently. Their status page says everything's operational, but your S3 bucket is empty. Who owns that incident? You do.
This just moves the compliance burden from them to your team, with zero SLAs on the export process. It's a trap.
If you absolutely must proceed, you need monitoring on the export, not just the schedule. Check for byte counts, validate the JSON schema nightly, set alerts for empty payloads. Assume the integration will fail.
Don't panic, have a rollback plan.
Panic is justified. Their policy shift means they're offloading storage costs and liability. That's the new reality.
The API-to-cloud-storage workaround is your only realistic path now. Don't wait to negotiate a custom plan, they've made their economics clear. You'll build the archive yourself.
> nervous about committing to a tool that might not hold our data long-term
It won't. That's the point of the update. So you need to treat the platform as a real-time processor, not a system of record. Your archival pipeline becomes a core, non-negotiable piece of the integration.
Timeline? If you're comfortable with their API and have infra in Azure Blob ready, a basic scheduled export can be up in a week. But making it production-grade with the monitoring user49 mentioned adds another 2-3 weeks. Factor that into your migration plan.
Integration is not a project, it's a lifestyle.
You're right to be nervous. The policy update formalizes their exit from being a system of record. Their API integration isn't a workaround; it's now the intended architecture. You must view the platform as a transient processing engine.
The concrete steps align with what others said, but with a key nuance: your archival process needs version tolerance. When you build that pipeline to Azure Blob Storage, do not just schedule a direct export. Insert a transformation and validation layer. Use a tool like dbt core or a simple Python script to check the incoming JSON structure against a defined schema and log any drift. This gives you early warning if their API output changes, which it will.
Negotiating a custom plan is likely a dead end for standard tiers. The cost economics of long-term object storage make it prohibitive for them at scale. Your timeline depends entirely on your validation rigor. A naive script to dump data takes a week. Building a fault-tolerant pipeline with the monitoring user49 mentioned is a three to four week project for a senior engineer. You're not just moving data; you're assuming responsibility for a critical data contract they no longer guarantee.