A financial sector client spent 8 hours every month consolidating data and producing their board report. We automated this in 3 weeks. Here is the full breakdown: architecture, tools, pitfalls and real ROI.
The initial problem
Our client consolidated data monthly from 4 sources: an ERP, a CRM, Excel exports and a BI tool. The process involved 3 people, took 2 full days and regularly generated input errors.
The architecture
Step 1: collection and normalisation
A Python pipeline with Pandas and SQLAlchemy retrieves data automatically from 4 sources. Data normalised and stored in PostgreSQL. Execution time: 12 minutes.
Step 2: KPI calculation
A calculation module generates the 23 board KPIs. Each compared to previous month and annual target. Significant variations automatically flagged.
Step 3: report generation
A Jinja2 template integrated in a Python workflow generates the Word report then converts it to PDF. Charts produced with Matplotlib and automatically injected at the right places.
Key to success: we spent 40% of project time validating calculation formulas with the client before writing any code.
Lessons learned
Start with the 5 most consulted indicators, deliver in week 1, validate, then extend. Always plan for edge cases: automatic retry and failure email alert are non-negotiable.
With care,
Excellent article, this matches exactly what we're seeing with our enterprise clients. The section on inference costs is especially valuable. It's a topic most articles gloss over but it's make-or-break at scale.
Thanks James! Inference cost optimization is often deprioritized during prototyping but becomes critical in production. Feel free to book a session if you'd like to go deeper on this.
Sharing this with my whole team. The distinction between an impressive demo and robust production is exactly the debate we're having internally right now. The human checkpoint advice is immediately actionable.
Great article. I'd push back slightly on the 18-day deployment estimate, in our experience with enterprise security and GDPR requirements, 4–6 weeks is more realistic for a first production agent.
Completely fair point David. The 18 days refers to a scoped first agent in a test environment. For full enterprise production with security constraints, your estimate is accurate.