Results

Real outcomes from real businesses

No vanity metrics. No inflated claims. These are measurable results from projects I have personally designed, built, and deployed.

5 days → 1 day
Month-end close time
15+
Sites automated
Daily
Forecast refresh frequency
HospitalityMulti-site hospitality group

Financial forecasting automation for a multi-site hospitality business

The Challenge

A hospitality group with 15+ sites was producing monthly forecasts manually in Excel. Each site took half a day. The finance team spent more time compiling numbers than analysing them, and forecasts were outdated by the time they reached the board.

The Approach

Built an automated forecasting system pulling live data from SAP, EPOS, and payroll systems. Implemented two forecast methods — run-rate and seasonal — with automatic daily cache refresh. The finance team now reviews and adjusts forecasts instead of building them from scratch.

PythonSAP HANAReactFastAPISSAS Tabular
We went from dreading month-end to having real-time visibility. The board gets better numbers faster, and the finance team actually has time to think strategically.

Finance Director

Multi-site hospitality group

40+
Hours saved per month
0
Manual journal entries
Daily
Automated reconciliation
RetailFarm shops and retail outlets

ERP and retail system reconciliation eliminating manual processes

The Challenge

A retail business with multiple farm shops and outlets was manually reconciling stock between their EPOS system and SAP every day. The process involved CSV exports, manual matching, and journal entries — consuming 40+ hours per month of skilled finance time.

The Approach

Built an automated daily reconciliation pipeline connecting CBE EPOS directly to SAP B1. The system generates stock journals, goods received notes, and reconciliation reports automatically. Includes drift monitoring and exception alerting.

PythonSAP B1SQL ServerAutomated scheduling
What used to take someone most of their week now happens automatically overnight. The accuracy is better than manual processing ever was.

Operations Director

Multi-site retail group

13
Reports automated
Weekly
Automated distribution
0
Manual steps required
CorporateGroup holding company

Board governance and reporting automation

The Challenge

A group with multiple subsidiary companies was producing board packs manually — pulling data from SharePoint, SSAS cubes, and Excel models, then formatting into PDF reports. 13 reports, produced weekly, each requiring manual intervention.

The Approach

Built an automated report generation system that pulls from SharePoint and SSAS Tabular models, produces formatted Excel reports, converts to PDF, and distributes via email — all on a weekly schedule with zero manual intervention.

PythonSSAS TabularSharePoint APIMicrosoft Graph
Every Monday morning, the reports are there. No chasing, no formatting errors, no delays. It just works.

CFO

Group holding company

Minutes
Time from question to answer
6+
Saved report templates
Plain English
Query interface
InfrastructureEV charging network

AI-powered operational analysis for infrastructure optimisation

The Challenge

An EV charging network needed to understand utilisation patterns across their estate to inform investment decisions. Data was scattered across multiple systems with no unified view.

The Approach

Built a natural language query interface over their operational data, enabling leadership to ask questions in plain English and receive formatted reports. Includes pivot tables, time-period analysis, and automated exclusions for non-comparable data.

PythonFlaskDAXSSAS TabularNatural language processing
I can ask a question about our utilisation data and get an answer in seconds. No need to wait for someone to build a report — I just type what I need.

Managing Director

Infrastructure operator

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