Case Study

How a beauty salon in Lisbon automated financial data collection and cut outsourced accounting costs

Pause was paying an external accountant every month to manually collect data and prepare reports. The timelines were fine, but the recurring cost of the most labor-intensive part — manual data gathering and reconciliation — could be eliminated. I built a pipeline that pulls data from the salon's CRM (Altegio) and financial operations register automatically, then assembles the reports. The process now takes under a minute.

Beauty salon Lisbon, Portugal Retainer

Context

The reporting worked, but cost too much

Pause used Altegio as its core system for bookings, visit tracking, and service analytics, and maintained a separate transaction register (ДДС) in Google Sheets for cash and bank operations. Every month, an external accountant would manually pull data from these two sources and compile reports — P&L, cash flow, and balance — in Excel. Dasha, the salon owner, wanted to reduce costs and her dependency on the external contractor without losing the picture the reporting gave her.

Industry

Beauty salon, Lisbon

Systems

Altegio (CRM), Excel (legacy reporting), Google Sheets (transaction register)

Objective

Eliminate dependency on manual labor for data collection

The approach

Five stages — from audit to a working system

Audit and deconstruction of existing logic

You can't just build a data export without understanding the financial logic of the business — otherwise you automate chaos. First, I fully deconstructed the existing logic (principles, formulas, data sources), confirmed it with the client and external accountant, and only then started building. Key constraint: don't rewrite reporting from scratch — automate only what's actually used.

Iterative workaround for CRM data access limitations

Standard access to Altegio data only covered ~40% of what was needed for reporting. First iteration: a semi-automated pipeline where the system pulled available data and generated direct links for the rest. Second iteration: the pipeline became 100% autonomous. Along the way, we found and fixed a historical revenue error caused by the complex discount and loyalty point logic.

Connected the transaction register to the reporting system

Cash and bank transaction data was maintained by an administrator in a separate Google Sheet and had to be manually copied into the report at month-end. The system now connects directly to the register: all transactions are automatically imported into the common reporting database.

Final report assembly

Raw data from CRM and the transaction register sits in the database, but it needs to be mapped into standard financial forms — P&L, Cash Flow — with proper account mapping. All imported data is now assembled strictly according to the logic confirmed during the audit. Final reporting is generated fully automatically.

Historical reconciliation and validation

Automation doesn't earn trust until proven absolutely accurate. We reconciled two months of historical data, comparing old manual reports against the system's output. Once accuracy was confirmed, we built the reconciliation into the pipeline itself: the system now cross-checks sources and flags discrepancies before they reach the report.

Solution architecture

How the pipeline works

Altegio (CRM)

Visits, services, bookings, commissions (~40% via standard access)

Altegio (extended access)

Remaining 60% of data — via workaround for standard access limitations

Processing

Data collection system

Aggregation, normalization, consistency checks before writing to database

Database

Unified storage

All raw data from all sources in one place

Financial reports

Single file, automated assembly

P&L CF Balance

Analytics

Revenue, staff utilization, payment mix

Transaction register

Cash and bank operations — directly into database

Results

Before and after

MetricBeforeAfter

Data collection Hours of manual work Fully automated

Data quality Manual reconciliation at month-end Auto-validation before each report

Support cost Monthly payment for manual data transfer Minimized

Takeaways

Three lessons from this project

Only automate what you actually use

The only forms and metrics worth automating are the ones you make decisions on. Everything else is noise that adds complexity without value.

Manual data transfer is a hidden tax

It is cheaper to build a system once than to keep paying someone every month to move numbers from one place to another.

No accounting policy means higher costs later

When accounting rules are documented upfront, every subsequent improvement costs a fraction of what it would otherwise. Documentation pays for itself.

“Klim, this is some kind of magic. I used to be afraid to ask how the money was looking -- the answer wouldn't come for another month. Now I don't depend on anyone -- the numbers come together on their own.”

Dasha, owner of Pause

Status and roadmap

The audit and pipeline build took just over a month, after which the project transitioned to an ongoing partnership. The roadmap is broken into fixed-scope tasks — the client sets the priority for each next step.

Unified accounting policy — single chart of accounts and one source of truth instead of manual reconciliation

Analytical reporting — room and staff utilization, service-level margins, unit economics

Bank statement automation — direct import, balance tracking, automatic transaction-to-account mapping