3 results found
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Dealing with group AI Policies blocking out Opal
Customers with corporate AI policies restricting usage to approved vendors are blocking Opal adoption entirely or partially. While each scenario has its unique challenges, CS teams currently lack a structured playbook to navigate this. including security and compliance talking points, and escalation paths to help customers gain internal IT/legal approval to whitelist Opal.
1 vote -
Improved visibility on credit consumption
A recurring theme in user feedback concerns the predictability of credit consumption. Two related issues come up most often:
Lack of upfront visibility — users are unable to anticipate how many credits a given task will consume before running it.
Inconsistent consumption — when users have an expectation, actual usage varies run-to-run for comparable tasks
The underlying ask is for greater predictability and traceability: a clearer sense of expected cost before execution, and a transparent breakdown of what was actually consumed afterward. This would meaningfully increase customer confidence and reduce hesitancy around running larger or repeated tasks.
1 vote -
Allow MCP server to query MAU data
I've got an IT support ticket to get my Claude connected to the remote server. In the meantime, I'm using the locally installed server to audit our MAU consumption. The MCP server is struggling to get users for each a/b test rule, which feels like a miss that might be easily fixed in a future release? I was able to create a workaround with a script that queries the Results API, but this took a while and was quite clunky.
2 votes
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