Data integration gets records out of the systems that hold them, makes them agree, and puts the result somewhere everything else can read. A warehouse is that place: one consistent, historical copy of the business.
Most reporting, forecasting and AI projects stall on the same problem. One customer exists under several names, and no two systems share an ID.
Each one depends on the one before it — matching without extraction is guesswork.
Scheduled, monitored pulls from each source system, including the old one with no API. A pipeline that fails silently is worse than no pipeline, because people keep trusting the numbers.
Deciding when two records are the same customer, product or site, and which source wins when they disagree. The rules are a business decision, and they need an owner who can make it.
Source systems overwrite. A warehouse keeps what a record looked like last quarter, which is the only way to answer why a number has moved.
Reporting, forecasting and AI all read the same assembled copy, so they cannot quietly disagree — and none of them are querying a production database at month end.
Rules that flag the broken rows on arrival and tell someone, rather than letting them surface in a board pack. Most data quality failures are old, small and repeated.
This layer cannot fix data the source systems keep breaking. Where a field is entered inconsistently at the point of capture, the durable fix is upstream — the warehouse only makes the cost of it visible.
Often not yet, and we will say so. Two systems with decent APIs and modest volumes are well served by direct connections and a small reporting database.
A warehouse earns its place when several systems must be reconciled, history gets overwritten, reporting slows your systems, or the connections no longer fit on a whiteboard.
Cost follows how much your records disagree, not data volume.
We look at your systems, IDs and questions, then tell you whether you need a set of connections or a real warehouse.
Storage and compute are cheap. The effort is reconciling records that describe the same thing differently.
Someone on your side must decide which records are the same customer. Without a named owner, projects stall here.
A first data layer is a fixed-price Amplify project. As new sources and rules keep arriving, it runs as an Apex partnership.
We profile the actual records rather than the schema: how many duplicate customers, how many blank required fields, how far the systems disagree. The answer is worse than expected roughly every time, and it is better known now.
A single system extracted, matched and landed with monitoring and quality checks in place. Doing one properly sets the pattern the rest follow, and it exposes the identifier problems early.
The remaining systems join one at a time, and the reporting, forecasting and automation work starts reading from the shared layer instead of from copies.
Web application development services for internal tools, customer portals and SaaS products. Built in Adelaide to a fixed price, with a one-year warranty.
We map how your business really runs — systems, costs, friction — and return a prioritised roadmap with a business case. $9,000 + GST, fixed.
AI agents that own a job end to end — invoicing, compliance checks, recurring reporting — and only come back to you for the exceptions.
AI automation that handles judgement, not just fixed rules. We map the process first, then automate what actually slows your team down.
For operations businesses running five or more systems. We connect your ERP, CRM, warehouse and field systems so nobody retypes data, and keep them connected as a long-term partner.
Not until several systems have to be reconciled rather than just connected, history matters because your systems overwrite it, reporting load starts affecting the systems it reads from, or the number of direct connections between systems has grown past what anyone can keep track of. Below that, point-to-point integration and a small reporting database are cheaper and do the same job.
With matching rules and a decision about which source wins for each field. Some of it is automatic — exact matches on ABN, email or account code. The rest is a judgement call someone in your business has to make and stand behind. Expect to name an owner for this; it is the part that stalls projects when nobody has.
Usually. Direct database reads, scheduled file exports and screen-level extraction all work, in that order of preference. The constraint is rarely getting the data out — it is that older systems often lack the identifiers needed to match their records to anything else, which is found in the profiling stage.
Related, and not the same. Integration keeps two systems in step operationally — an order placed here appears there. The data layer assembles a consistent copy for reporting, analysis and AI. The overlap is the matching: both need to know which records correspond, and doing that work once serves both.
For anything that reasons over your own records, yes. A model given inconsistent data produces confident, inconsistent answers, and the failure is harder to spot than a broken report. The good news is that the data work is not AI-specific — it is the same foundation reporting and integration need, so it is not a cost that belongs to the AI project alone.
In your own cloud account, in the Australian region unless you tell us otherwise, with access controlled per role and logged. You own the infrastructure and the pipelines the same way you own the code, so nothing here creates a dependency on us to keep operating.