Compliance-grade data out of the box. DAMA-DMBOK 2.0-aligned governance, master data management, dashboarding, and predictive analytics — built on the cleanest data source most businesses already have and rarely use: their validated invoice flow.
Every invoice that passes Peppol or clearance validation has already had its vendor identifiers, customer records, tax codes, and item data checked against the schema, the code lists, and the business rules of a tax authority. A validated invoice flow is a free master-data audit — one you have already paid for by complying.
That changes the economics of a data programme. The conventional first phase — months of profiling, deduplication, and remediation before anything useful appears — shrinks, because the highest-value domains arrive pre-verified. Vendor masters that transmit cleanly through an Access Point are, by construction, correct on the fields a regulator cares about. We extend from that verified core outwards rather than scrubbing everything from zero.
And if you are not on e-invoicing yet? The practice still works. We build the foundation from your system extracts and reconcile it the conventional way — and when a mandate does reach you, the same foundation feeds the compliance work instead of fighting it.
Ownership, definitions, and quality rules set out per DAMA-DMBOK 2.0 — scaled to your organisation, not to a multinational's operating model. The output is a working governance routine your finance team actually runs, not a binder.
Vendor, customer, item, and GL/tax domains — the four that decide whether an invoice clears and whether a report reconciles. Where you run e-invoicing, the validated flow seeds the golden records; where you don't, we build them from system extracts.
First dashboard live within thirty days: receivables, payables, cash position, and rejection rates, in Arabic and English from the same model. Built on the 8-connector integration layer, so the numbers refresh from your ERP without a re-keying step.
Once the foundation holds, we build forward-looking models on Amazon SageMaker and Amazon Bedrock: payment-behaviour scoring, cash-flow forecasting, anomaly flags on transaction streams. Models trained on your data, deployed in your environment.
When an e-invoice bounces, the error code rarely describes an invoicing problem. It describes a master-data defect: a VAT number that no longer matches the registry, a tax code mapped to the wrong category, an item line missing a mandatory attribute, a customer endpoint never updated after a re-registration.
Read in bulk, the rejection log is a ranked list of which records are wrong, in which domain, and how often each defect costs you a transmission. That is precisely the prioritisation exercise a master-data programme normally spends its first weeks constructing from interviews and profiling runs — except this version is generated continuously, by a tax authority's own validation rules, at no extra cost.
We start MDM engagements there whenever the client has a validated flow. It is the fastest route from "our data is probably messy" to a numbered defect list with an owner against each line.
Read the field note →No. A validated invoice flow is the best starting material we know of, but it is an accelerant, not a prerequisite. Without it, we build the foundation from your system extracts — ERP exports, statements, registries — and reconcile it the conventional way. When a mandate later reaches you, the same foundation feeds the e-invoicing implementation rather than duplicating it.
The DAMA-DMBOK (Data Management Body of Knowledge, version 2.0) is the data-management profession's reference framework — it defines the disciplines of governance, quality, master data, metadata, and analytics and how they fit together. Using it means your programme is structured in terms auditors, regulators, and future hires already understand, instead of a consultancy's proprietary method that leaves when the consultancy does.
One semantic model, two renderings. Measures, dimensions, and definitions are maintained once, with Arabic and English labels carried as attributes of the model — so the AR and EN dashboards always show the same numbers, and the data dictionary reads correctly in both languages. It is not a translated copy that drifts out of date; it is the same artefact rendered twice.
We connect through the same 8-connector integration layer that carries our e-invoicing practice — Zoho, QuickBooks, Xero, Tally, and Odoo as standard, with SAP, Oracle, and Dynamics 365 as scoped engagements. Dashboards are delivered on your incumbent BI platform where you have one; predictive work runs on Amazon SageMaker and Amazon Bedrock, deployed in your environment.
Engagements are calibrated to domain count, source-system complexity, and dashboard scope, so we do not publish a rate card. What we commit to: directional pricing in the first conversation, and a fixed-fee written quote within 24 hours of the discovery call.
Tell us your ERP, your master-data pain points, and what you wish you could see on one screen. We tell you where to start, what the first 30 days deliver, and what it will cost — with a written fixed-fee quote within 24 hours.