Post-launch analysis is part of Discovery OS in the Bagel AI product brain. After a feature ships, it reads feedback, usage and revenue from the accounts that asked for it, then tells you whether it landed.
Audit log export for enterprise admins
Post-launch analysisExport audit logs as CSV for SOC 2 reviews. Released Oct 6. Sample data.
Export complaints per week from the 14 accounts that asked. The dashed line marks the release.
12 of 14 accounts use it and complaints are down 68%. The verdict goes to the account teams who heard the request.
Calls, tickets, usage and CRMCustomer CallAudit export cut our SOC 2 prep from a week to a dayCustomerNorthwind FreightCreatedNov 4ARR$32KRenewal noteRenewed for two years. Audit log export was on their list.CustomerCobalt PayCreatedOct 31ARR$21KSupport TicketTwo admins still run the old manual exportCustomerBrightline HRCreatedOct 26ARR$12KRenewal noteNamed the audit export as a reason to renewCustomerArcadia LabsCreatedOct 21ARR$24KUsageSix of the 14 accounts exported audit logs this weekCustomer14 accountsCreatedOct 16ARR$200KSupport TicketCan we filter the audit export by date range?CustomerLumen HealthCreatedOct 11ARR$18K
Usage, feedback and revenue live in three different tools, and none of them knows who asked for the feature.
| After launch today | With post-launch analysis in Bagel AI |
|---|---|
| After launch today Usage dashboards count clicks across every user, with no link back to the customers who asked. | With Bagel AI Usage is read for the accounts behind the original request. |
| After launch today Feedback tools stop counting once the roadmap item closes. | With Bagel AI Bagel AI keeps reading sales calls, support tickets, chats and threads after launch. |
| After launch today Revenue and renewals sit in the CRM, far from the feature that was meant to move them. | With Bagel AI ARR, renewals and churn for those accounts sit next to the verdict. |
| After launch today The account teams who heard the request never hear how it went. | With Bagel AI The result goes back to each account team that heard the request. |
| After launch today The next planning cycle starts without knowing what the last one bought. | With Bagel AI Every outcome joins the evidence for the next bet. |
Discovery OS recorded who asked for the feature before anyone built it. After launch, it checks those same accounts in your feedback, your product analytics and your CRM.
Bagel AI keeps reading sales calls, support tickets, chats and threads from these accounts. You see whether complaints dropped and what customers say about the fix.
Customer CallNorthwind Freight · $32K ARRSupport TicketLumen Health · $18K ARRUsage is counted for the accounts behind the original request, read from your product analytics. You see who picked it up and who hasn’t yet.
ARR, renewals and churn for those same accounts come from your CRM, so the revenue case behind the bet gets checked too.
Signal in. Verdict out. Read from your calls, tickets, analytics and CRM through 100+ integrations.
Every signal resolves to one customer. A ticket, a usage event and a renewal from the same account line up in one record, so twelve mentions from one admin still count as one account.
Book a 30-minute demoBefore anyone built the feature, Discovery OS recorded who asked, what they said and the ARR at stake. That record becomes the baseline.
Your team or your coding agents build from the scoped decision over the Bagel AI MCP. Once it ships, Bagel AI starts tracking the accounts behind the request.
New calls, tickets, usage and CRM changes from those accounts land in one view, each resolved to a single customer.
Bagel AI reports whether the feature landed. Mixed results show which accounts are still stuck and why.
The result joins the decision’s lineage, from the first call that asked for it to what happened after launch. Account teams hear how it went, and the next round of planning starts from it.
When the 30-day check comes up
Normally someone pulls a dashboard, skims tickets and asks CS how it’s going. Bagel AI already read all of it for the accounts that asked.
Result:
You walk in with the verdict and the evidence behind it.
When an account that asked is up for renewal
The account team sees whether the feature that account asked for shipped, whether its people use it and what they’ve said since.
Result:
The renewal call opens with proof the request was heard.
When the board asks what shipped work bought
Each launch comes back with adoption, feedback and revenue for the accounts behind it, in one report.
Result:
You show outcomes next to the roadmap.
When adoption stalls
Bagel AI shows which accounts haven’t picked it up and what they’re saying about it in calls and tickets.
Result:
You know whether to fix it, explain it or move on.
Most product teams already run a product analytics tool and a feedback or roadmap tool. Here is what each one sees after a feature ships.
| What matters after launch | Product analytics | A feedback or roadmap tool | Bagel AI |
|---|---|---|---|
| Knowing who asked for the feature | Product analyticsNo link to the requests behind it | A feedback or roadmap toolRequesters and votes on the item | Bagel AIEvery account, call and ticket behind the request |
| Usage of the feature | Product analyticsEvent-level usage across every user | A feedback or roadmap toolNot tracked | Bagel AIUsage for the accounts that asked, read from your analytics |
| Feedback after launch | Product analyticsNot tracked | A feedback or roadmap toolNew posts, if customers file them | Bagel AICalls, tickets, chats and threads, read as they land |
| Revenue and renewals | Product analyticsOnly if you join CRM data yourself | A feedback or roadmap toolCRM fields on each request | Bagel AIARR, renewals and churn for those accounts |
| Reporting back to the teams who heard the request | Product analyticsNot covered | A feedback or roadmap toolA status update to voters | Bagel AIThe result goes to each account team |
| Feeding the next decision | Product analyticsDashboards for the next review | A feedback or roadmap toolThe item closes | Bagel AIThe outcome joins the evidence for the next bet |
“Completes the long-time missing part between revenue, product and the customer. It is a product market fit pulse on autopilot.”
Leore JacobsVP Product Operations, Tipalti“Every SaaS company hits a point where feedback piles up but decisions stall. Bagel AI turns that mess into clear, revenue-focused priorities.”
Moran PerelmanGM and Head of Product Ops and Analytics, Gong“Companies aren’t lacking the metrics. Companies are lacking the closing the loop and storytelling.”
Bagel AI is SOC 2 Type II certified and reduces personal data by architecture before analysis. Your data never trains another customer’s model.
Post-launch analysis checks a shipped feature against the reason it was built. In Bagel AI, the record of who asked and the ARR at stake is already on file. After release, Bagel AI reads those accounts’ feedback, usage and revenue and reports whether the feature landed.
Feedback after release, from sales calls, support tickets, chats and threads. Adoption of the feature, from your product analytics. Revenue and renewals, from your CRM. Each one is measured for the accounts behind the original request.
Product analytics counts usage across every user. Bagel AI starts from the customers who asked for the feature and checks their usage, feedback and revenue together. Your analytics tool stays in place as one of its sources.
Closing a request records that the feature shipped. Bagel AI keeps reading signal from the accounts that asked and tells you whether the feature solved their problem.
Your feedback sources, your product analytics and your CRM, connected through 100+ integrations. Every signal resolves to one customer, so a ticket, a usage event and a renewal from the same account line up.
No. It shows what changed for the accounts that asked, with the evidence behind each number. Strict cause and effect needs a controlled experiment.
It’s part of Discovery OS, inside Everything AI. Results attach to the decision’s lineage, so the original evidence and the outcome sit together.
Product leaders and PMs see the verdict in Discovery OS. The account teams who first heard the request get the result for their accounts.
Bagel AI is SOC 2 Type II certified and reduces personal data by architecture before analysis. Your data trains your dedicated model and never trains another customer’s model. The Trust Center has the full detail.
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Every product decision, made.
Triaged, scoped, shipped.
