Switched from a custom solution to Pleo. The pre paid cards are great, but the reporting is weak.

After nearly four years of maintaining a custom expense management system built on a PostgreSQL-backed microservice architecture, we recently migrated our team to Pleo. The primary driver was the administrative overhead of managing physical corporate cards and the reconciliation burden on our finance team. Pleo's pre-paid cards and real-time receipt capture have indeed delivered on that front, significantly reducing the manual entry workload.

However, from a data and reporting perspective, the platform feels notably constrained, especially for a team accustomed to deep-dive analysis. Our previous system allowed for complex, ad-hoc queries directly against our data warehouse. In contrast, Pleo's reporting dashboard appears to be a one-size-fits-all solution with limited dimensions for slicing data.

The main pain points we've encountered are:

* **Lack of Custom Dimensions:** We cannot add custom tags beyond the basic categories (e.g., "Project_ID," "Client_Code," "Campaign_Name") which are crucial for our internal cost allocation and profitability analysis.
* **API Limitations for Analytics:** While Pleo offers an API, the data model exposed for transactions and reports is simplified. Aggregating data at scale for monthly performance benchmarking requires multiple API calls and client-side processing, which is inefficient.
* **Inflexible Export Formats:** The standard CSV exports are rigid. They lack the ability to pre-join related data (e.g., cardholder department info with transaction-level details) in a single, report-ready stream, forcing manual reconciliation in spreadsheets.

For example, to approximate a simple report we used to generate, I now have to run a script that performs a series of API calls and merges the data. The logic is straightforward, but the volume of requests is non-trivial.

```python
# Simplified example of the multi-call aggregation needed
import requests

# 1. Fetch all transactions for a period
transactions = requests.get(f"https://api.pleo.io/v1/transactions?from={start_date}").json()

# 2. For each unique card, fetch cardholder details (separate call per card)
cardholder_data = {}
for txn in transactions:
card_id = txn['cardId']
if card_id not in cardholder_data:
cardholder = requests.get(f"https://api.pleo.io/v1/cards/{card_id}").json()
cardholder_data[card_id] = cardholder['user']['department']

# 3. Merge locally for reporting
# ... additional processing ...
```

This is a step backward from a direct `SELECT * FROM expenses JOIN users ON... WHERE project_id = 'X'` query against our own database.

My question for the community is: have others in technical or finance operations roles faced similar reporting gaps with Pleo? Specifically:

1. Have you developed a robust ETL pipeline to pull Pleo data into a dedicated data warehouse (e.g., BigQuery, Snowflake, Redshift) for better analysis? If so, what's your architecture?
2. Are there any third-party connectors or middleware (like Zapier/Make, but focused on data transformation) you recommend to enrich Pleo data before it hits your analytics layer?
3. Does Pleo's "Insights" or "Power BI" connector adequately address these limitations for high-volume, complex account coding?

The card product is excellent for spend control, but the data layer currently feels like an obstacle to a fully automated financial close process. I'm interested in benchmarking solutions against our previous custom setup's performance, where generating a complex departmental P&L took under 10 seconds. Current Pleo-driven workflows are orders of magnitude slower.

-ck