I want to propose two highly critical improvements to how Ramp integrates with our ERPs. Currently, integration is largely a “one-way street” post-sync for card transactions, which creates downstream data issues and limits how quickly Ramp’s AI can learn our business.
Here are the two features we need to bridge this gap, keep our data clean, and supercharge Ramp’s intelligence.
Part 1: Allow Post-Sync Editing for Card Transactions (Stop the ERP Data Mismatch & AI Degradation)
Currently, once a credit card transaction is synced, its accounting fields (Class, Vendor, GL Account) are permanently locked in Ramp. If an error is made, we are forced to correct it directly in our ERP register.
While this “lock” is designed to prevent ledger discrepancies, correcting transactions in the ERP instead of Ramp creates several major downstream issues:
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Broken Analytics in Ramp: Because the ERP doesn’t sync back to Ramp, Ramp’s internal spend analytics, dashboards, and reporting remain permanently incorrect.
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AI Agent Degradation: Ramp’s AI suggestions and automated coding rules learn from our transaction history. When we are forced to leave incorrect historical data in Ramp, it trains the AI on wrong data, degrading the accuracy of future auto-codings.
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The “Overwrite” Risk: If a transaction is somehow touched, unlocked, or re-synced from Ramp later, it risks pushing the old, incorrect data back to the ERP—completely overwriting the manual corrections we made in our ledger.
Proposed Solution: Ramp already allows us to update and re-sync Bills post-submission. We need the exact same capability for Card Transactions. Allowing us to edit tracking categories in Ramp post-sync and push those updates to the ERP would ensure both systems stay 100% in sync and our AI remains highly accurate.
Part 2: Historical ERP Data Ingestion (To Train AI and Clean Up Pre-Ramp Data)
When a company onboard with Ramp, the AI has no historical context and has to learn our bookkeeping habits from scratch. We need a way to import historical, pre-Ramp transactions directly from our ERP into Ramp using a customizable date-range selector (e.g., pulling in the last 1–3 years of ledger history).
Here is why this would be a game-changer:
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Supercharge Ramp AI on Day One: By importing historical ERP transaction data (which is already finalized and correctly coded), Ramp’s AI would instantly learn our historical vendor patterns, GL account selections, departments, and classes. Auto-coding would be highly accurate immediately.
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Historical Data Enrichment & Cleanup: This would let us use Ramp’s clean interface, filters, and AI capability to enrich, organize, and clean up historical ledger data that might have been coded inconsistently in the past before we adopted Ramp.
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True Long-Term Trend Analytics: We would finally be able to see continuous spend and department trend analytics inside Ramp’s dashboard spanning both our pre-Ramp era and our current Ramp usage.
Conclusion
Allowing a retroactive “historical pull” from the ERP, combined with post-sync card editing, would turn Ramp from a card/bill provider into a highly intelligent, end-to-end bookkeeping partner that maintains perfect data integrity.
If you’ve run into these same reconciliation loops, had your ERP edits overwritten, or want your Ramp AI to be smart from day one, please upvote this so we can get it on the product team’s radar!