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FINTECH / SAAS

Headless Integration for Legacy Accounting Systems

≈ +80 hours/month freed

Replaces ~4h/day of manual accountant work × ~20 working days.

How we turned a closed accounting platform into an automated workflow, removing repetitive manual work without waiting for an official API.

PythonSeleniumBaseDockerReverse EngineeringAPI Design
Situation

A US startup needed to integrate data from a well-known legacy accounting platform into their own product. No public APIs existed for the required workflows, and anti-bot systems made a standard integration impossible. The goal was a technical bridge that could automate operations without putting user accounts at risk.

Risk

Without a reliable integration, the product depended on manual back-office work. Standard automation could also trigger account instability or break whenever the platform changed.

Decision

Validate whether a headless integration could safely automate the critical workflow before investing in a full production bridge.

Intervention

We structured the work in two phases: a 3-week PoC to confirm technical feasibility, then 2 months to production. We used SeleniumBase to handle authentication, building a session persistence layer that minimises logins and makes the automation look like organic traffic. To maximise efficiency, we reverse engineered the software's internal network calls, bypassing DOM interaction wherever possible. The whole architecture is Dockerised and stateless, allowing horizontal scaling of parallel sessions with granular log monitoring.

Result

The startup no longer needed people to repeat the same accounting workflow by hand every day. The closed platform became an automated backend the product could use safely, saving roughly 80 hours per month and giving the client a system they own and can scale independently.

Economic value

≈ +80 hours/month freed

Replaces ~4h/day of manual accountant work × ~20 working days.

Before / after

BeforeManual accounting workflow

AfterAutomated backend workflow

BeforeNo official API

AfterModern internal endpoints

BeforeRoughly 4 hours per day spent manually

AfterAbout 80 hours per month freed