Contracts between your CRM, ERP and data platform that hold under load, partial failure and replay.
- Idempotent syncs that reconcile on re-run
- Server-side access enforcement, not config alone
- Per-run audit trails for traceability
Systems we can work with
If it has an API, we can integrate it.
The problem
Sales works in the CRM. Finance works in the ERP. Operations keeps a spreadsheet nobody admits to. Each is right about its own version of events, and none of them match.
1
Each system is right about its own version of the truth. Every hand-off is a person re-typing something a system already knows.
2
A defined contract between systems, not a nightly export and a spreadsheet. Each side stays insulated from the other’s changes.
3
The flow runs itself. Safe re-runs, failures that alert us before customers notice, and nobody re-keys anything.
Automated is the finish line
Reporting, analytics and AI read the same governed layer. Finance and sales stop arguing about whose figure is right.
$12.9M1
Average yearly cost of poor data quality
Matched records land upstream, so the manual patch-up work disappears.
88%2
Business spreadsheets that contain errors
Governed, documented data that an AI can answer from without inventing figures.
30%3
Generative AI projects on course to be abandoned
Routine questions like order status and availability get answered from live data on your customer portal instead of over the phone. The hours that frees go to selling and serving.
28%4
Of a sales rep's week is spent actually selling
Contracts between your CRM, ERP and data platform that hold under load, partial failure and replay.
Custom work where the platform genuinely runs out, and configuration everywhere else, so the footprint you maintain stays small.
The unglamorous work that decides whether an integration is still trusted a year on: monitoring, reconciliation and runbooks — on systems we built and on ones we take over from someone else.
How we work
Built to be handed over. Most of what separates an integration that lasts from one that gets quietly replaced is decided before any code ships, and every engagement ends with your team running the system without us. This is the standard we hold ourselves to, written down so you can hold us to it too.
A fixed scope you can hold us to.
The people who scoped it build it.
Re-runnable, monitored, tested.
Runbook and access end up in your repo.
Case studies
These are anonymised case studies from work our principals have delivered in previous roles, and these two are a sample of a larger set. Each is a full write-up of the decisions and trade-offs behind an integration that survives. Every one answers a question we get in nearly every scoping call.
Re-runnable by design
How do you keep two systems in step without creating duplicates?
Records match on a stable key and write with create-or-update semantics. Run the job twice and it reconciles instead of stacking copies. Run it after a failure and it resumes.
Read the case study
Handover-ready
How do you take over an integration built by someone who's gone?
Observe first, make it re-runnable, document it into runbooks, then change behaviour. The feared nightly job becomes one their own team can run without us.
Read the case study
Who you work with
Three certified specialists and two advisors. The people who scope the work build it.
Common questions
Short answers to what comes up most in scoping. The longer versions live in the reference architectures and the engagement models.
We match records on a stable key rather than the order they arrive, and write with create-or-update semantics. Run the job twice and it reconciles instead of stacking copies. Run it after a failure and it resumes where it stopped.
Read the case studyEach system captured the customer at a different moment, and the copies have drifted since. We resolve them with match keys and per-field survivorship rules into one golden record in the data platform, then sync it back so every system reads the same name, address and history.
Read the case studyThe won deal flows to an order, the order flows to an invoice, and fulfilment and payment status flow back to the deal. Nobody re-keys anything, so sales and finance read the same numbers instead of drifting apart.
Read the case studyA semantic layer defines the terms and retrieval binds each answer to governed data, so the assistant speaks from your records rather than from memory. Every answer traces back to a source, and the assistant declines when the data cannot support one.
Read the case studyOperational systems make poor reporting layers. Heavy queries slow the system that is running the business, history is limited, and you cannot join cleanly to data held in other systems. We land the data in a governed layer instead, where it conforms to shared definitions and reporting cannot disturb operations.
Read the case studyNo obligation, no pitch deck. We reply within one business day.
Location
Brisbane, QLD. Working across Australia.
Independent
The people who scope the work build the work.