The typical business we meet has its real operating data spread across a dozen linked spreadsheets, a bank export, an inbox and someone’s memory. It works, right up until the person holding it together takes a week off, or the business grows past the point where manual reconciliation is realistic.
Our data work starts by mapping where information actually enters the business and where it goes to die. Then we build the smallest system that removes the retyping: a proper schema, automated imports, validation that catches the bad rows before they become bad decisions, and reporting that answers the questions the owner actually asks.
This is the part of our work we care about most, and it is usually where the largest and fastest return sits. Automating a weekly reconciliation that takes a day is worth more than almost any campaign you could run with the same money.
What is included
Data audit and mapping
A written picture of every source, who owns it, how often it changes, and where the same fact is being stored in three conflicting places.
Cleaning and structuring
De-duplication, normalised names and dates, sensible keys, and a schema that will still make sense when the dataset is ten times larger.
Automated pipelines
Scheduled imports from bank exports, CSVs, APIs or scrapes, with validation, error reporting and safe re-runs so a failed import never corrupts the record.
Reporting and dashboards
The handful of views that actually drive decisions, plus exports for your accountant, board or BAS rather than another dashboard nobody opens.
Handover documentation
How the system works, how to fix the common failure, and what to do when a source changes format. Written for your team, not for us.
How we work
- 01
Map the data
A working session where we follow one real record end to end, from the moment it is created to the moment someone makes a decision with it.
- 02
Model and clean
We design the schema against your real data, not an idealised version of it, then run the historical set through the cleaning rules and show you what fell out.
- 03
Automate the intake
Imports, matching rules and validation get built so new data arrives without human handling. This is where the recurring hours come back.
- 04
Report and hand over
We build the views you asked for, sanity-check the numbers against a period you already know the answer to, then document and hand over.
Who this is for
Data work pays off fastest for businesses with repetitive reconciliation, high record counts, or a compliance obligation that makes accuracy non-negotiable.
- Property, rental and tenancy operators reconciling payments against expected charges
- Auction houses, resellers and wholesalers with large, inconsistent product catalogues
- Trades and service businesses with jobs, quotes and invoices in separate tools
- Any business where one person is the only one who understands the spreadsheet
What good looks like
A finished data system should be boring. The imports run, the exceptions are visible, and the reports match reality without anyone massaging a cell.
We built exactly that for a Canberra antiques centre with more than fifty tenanted spaces: automated bank reconciliation against expected rent, overdue detection, receipting and tenant communications, replacing a stack of linked spreadsheets. The case study is worth reading if you want the detail.
Frequently asked questions
Do you need access to our live systems?
Not to start. We can do the audit and the schema design from exports and a walkthrough. Live access only becomes necessary when we automate the imports, and we prefer read-only credentials and a copy of the data until the pipeline is proven.
What if our data is genuinely a mess?
That is the normal case and it is the reason to do the work. Cleaning is a defined stage of the project, and we show you exactly what the rules changed rather than silently rewriting your history.
Will this replace our accounting software?
No. We sit alongside the tools you already pay for and fill the gap between them. If a job is already handled well by your accounting or CRM package, we integrate with it instead of rebuilding it.
Who owns the data and the code?
You do. We build on standard tooling, and the database, the pipelines and the documentation are yours at handover.
Related work
The Canberra Antiques Centre
A bespoke rent management platform that replaced a sprawl of linked spreadsheets with automated bank reconciliation, overdue detection, and one-click tenant communications for a 50+ space showroom business.
Read the case study →AI SolutionsPrivate Client
A bespoke AI system for a second-hand goods retailer that learns from their own sales history, automatically categorises every product line, and continuously surfaces the highest-ROI restocking opportunities from global wholesale marketplaces, replacing hours of manual research with a ranked, ready-to-act shortlist.
Read the case study →Often paired with
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