Sage 300 and Expensify integration

What moves between them

Expense reports flow from Expensify into Sage 300. ml-connector polls Expensify for approved expense reports on a schedule you define, extracts the detailed expense transactions, and maps each transaction to the appropriate Sage 300 GL account and cost center based on the Expensify category and tags. The mapped transactions are then batched and posted into Sage 300's GL journal batches. Vendor names from Expensify expenses are written as memo fields since Expensify has no vendor master entity. The flow is one-directional: Expensify into Sage 300. Sage 300 GL postings are never written back to Expensify.

How ml-connector handles it

ml-connector stores both credential sets encrypted: Expensify's API key pair and Sage 300's HTTP Basic Auth credentials (with uppercase normalization built in). For each polling cycle, ml-connector calls Expensify with date-range or status filters to export approved reports, then iterates the expense transactions, looks up the matching GL account in Sage 300 using the Expensify category and any tag-to-cost-center mapping you have configured, and constructs GL journal entries. Because both systems are polling-only, ml-connector runs on a schedule you control, typically daily or weekly depending on your expense cycle. Sage 300's IIS server may have app pool timeouts on large call volumes, so ml-connector implements exponential backoff on retries and tracks request counts to avoid triggering timeouts. Every expense report and transaction is logged with a full audit trail in ml-connector and can be replayed if a downstream GL post fails.

A real-world example

A mid-sized professional services firm runs Sage 300 on-premise for accounting and billing, and Expensify for employee expense management and corporate card reconciliation across multiple offices. Before the integration, the operations team exported approved expense reports from Expensify weekly, manually mapped each transaction to the correct cost center and project code, and re-entered the entries into Sage 300's GL. Month-end close included days of reconciliation and exceptions chasing. With Sage 300 and Expensify connected, each week's approved reports post automatically into GL, allocated to the correct project and office cost centers based on Expensify policy rules. Reconciliation happens automatically, and the manual entry step is eliminated.

What you can do

• •Poll Expensify for approved expense reports on a schedule and post them into Sage 300's general ledger.
• •Map Expensify expense categories and policy tags to Sage 300 GL accounts and cost centers.
• •Handle Sage 300's HTTP Basic Auth with automatic uppercase normalization and Expensify's API key pair.
• •Batch expense transactions and insert them as GL journal entries with full transaction detail and merchant information.
• •Maintain a complete audit trail of every expense report posted and support replay if a GL post fails.

Questions

Which direction does data flow between Sage 300 and Expensify?

Expense reports and transactions flow from Expensify into Sage 300. ml-connector polls Expensify for approved reports and posts them into Sage 300's GL. Vendor names from Expensify appear as memo fields since Expensify has no vendor master. The flow is one-directional only.

How does ml-connector handle Sage 300's HTTP Basic Auth requirement and Expensify's API key pair?

ml-connector stores both credential sets encrypted. For Sage 300, it normalizes the username and password to uppercase before every request, as Sage 300 requires. For Expensify, it includes the API key pair (partnerUserID and partnerUserSecret) with every JSON request. Both are refreshed from the credential store on each polling cycle.

How are Expensify expenses mapped to Sage 300 GL accounts and cost centers?

Each Expensify expense carries a category and optional tags (for project, class, or cost center). ml-connector looks up the GL account for that category and applies any cost center tag as a dimension in the GL entry. You configure the mappings when setting up the integration. If a category or tag is unmapped, ml-connector surfaces the error in the audit trail and does not post the transaction.