Financial Data Hardening

Find duplicate
vendors. Recover
trapped cash.

DataQubi hardens supplier data inside your Microsoft estate, so you recover trapped cash today and have AI-ready business context tomorrow. No ERP replacement. No data leaves your tenant.

Schedule 20-Min Strategy Call View How It Works
No ERP replacement Azure Native Data stays in your tenant
analysis_v2.sql
Duplicate Vendor Detected ID #8832
Recovery Opportunity $142,000
Supplier Master Quality +34% improved

Runs on the Microsoft ecosystem

SAP

The hidden problem

Duplicate vendors are rarely
the root problem. They are a signal.

When the same supplier exists three ways across your ERP, finance is not the team that broke it. Procurement onboarded it under one name. AP paid it under another. M&A inherited the third. The duplicate is the visible artifact. The decay is upstream, in supplier master structure.

What you see
Duplicate payments. Vendor sprawl. Audit findings.
  • Same supplier appears under 2 to 5 variations
  • Spend reports do not reconcile across business units
  • Rebate eligibility gets missed because spend is split
  • AP escalations spike at month-end and quarter close
What is actually broken
Supplier master has lost its structure.
  • No canonical identity for an entity across legal names, DBAs, and remit-to addresses
  • Metadata gaps: tax ID, classification, parent-child relationships missing or stale
  • No system of record after roll-ups; ERPs disagree silently
  • Governance lives in spreadsheets, not in policy

Fix the duplicates and the cash comes back. Fix the structure and the duplicates stop coming back.

Typical results

Measurable impact in weeks

From engagements with $100M to $1B supplier portfolios across PE-backed manufacturing, industrial, and distribution.

8–15%
Duplicates Found
Cleaner supplier master, reduced AP errors
$500K+
Cash Recovered
Immediate impact on working capital
30%
Faster Close
Accelerated month-end reconciliation

The AI readiness layer

AI does not fail at the model layer.
It fails at the context layer.

Every Copilot demo runs on a curated dataset. Every production Copilot runs on your supplier master, your GL, your spend categories, your contracts. If those records are fragmented, mis-classified, or missing metadata, the model is generating confident answers from broken context. The output looks fluent. The decisions are wrong.

01
Fragmented records
An agent asked "what did we spend with Acme last year" cannot answer when Acme exists as Acme Inc., Acme Industrial, and ACME INDUSTRIAL LLC. It picks one. It is wrong about three.
02
Missing metadata
Good metadata tells systems what a record means, how it relates, and whether it can be trusted. Without it, a model has tokens. It does not have business context.
03
No lineage
When an executive cannot trace a number back to its source, they will not act on it. That is not a model problem. That is a trust problem the model inherits.

The pattern is consistent: the firms getting real value from Microsoft Copilot and Fabric agents did the unglamorous work first. They hardened supplier data, defined business glossary, and built the metadata layer. The model became useful. The ones that skipped that step are still in pilot, still explaining the demo to the board.

The 14-day process

From chaos to clarity

First measurable value by Day 7

01
Day 1–3: ERP Data Snapshot
Read-only extraction of supplier, invoice, and payment tables. Zero disruption to production systems.
02
Day 4–7: Supplier Master Analysis
AI-assisted fuzzy matching identifies duplicates (8–15% typical).
First value delivered
03
Day 8–10: Payment Anomaly Detection
Flags suspicious invoices and cross-vendor duplicates.
Cash impact identified
04
Day 11–14: Intelligence Report
Consolidated duplicate list, recovery opportunities, and audit-ready documentation.
See Full Implementation Approach →

The practice

Five disciplines.
One outcome: AI-ready supplier data.

DataQubi hardens supplier data inside your Microsoft estate so automation and AI can act on clean business context. Not a platform. Not a consulting deck. A practice with five repeatable disciplines that run as a system.

01
Detect
Find duplicate suppliers, duplicate payments, and structural drift across ERPs. Fuzzy matching tuned to your industry, your geography, and your acquisition history. Not generic Levenshtein. Real signal.
02
Reconcile
Build canonical supplier records that hold up across legal name, DBA, remit-to, and parent-child structure. One golden record per entity, with a defensible audit trail back to source.
03
Enrich
Add the metadata that makes a record usable: tax ID, classification, category, payment terms, contract status, risk tier. The fields your AI agents need to make decisions you can defend.
04
Govern
Move governance from spreadsheets to policy. Ownership, stewardship, change control, and approval workflows that run inside your Microsoft estate. Auditable by design.
05
Monitor
Drift detection on supplier records, classification accuracy, and metadata completeness. Catch decay in the first 30 days, not at the next audit.

Street-smart hardening

Generic rules break
on real data.

The Big Four governance deck looks great in a steering committee. It does not survive contact with a vendor master that grew through three acquisitions and a Dynamics-to-S4 migration. We build rules that respect how your business actually runs.

Context-aware matching
A subsidiary is not a duplicate.
Generic dedupe collapses "Acme Industrial" and "Acme Industrial UK Ltd" into one record. We treat them as a parent-child pair, because they file taxes separately, sign contracts separately, and get paid through different remit-to accounts.
Industry-specific signal
Different industries, different match rules.
A construction GC matches vendors on license number and bonded status. A clinical lab matches on FEI number and regulatory classification. A distributor matches on parent network and rebate program. Generic libraries miss all three.
Operating-reality remediation
Do not break what your team relies on.
Merging supplier records sounds simple until you realize three POs are open, two invoices are in dispute, and the controller has a custom report keyed off the old vendor ID. We sequence the cleanup to preserve operations, not break them.
Governance you will actually enforce
Policies that survive the first procurement escalation.
A workflow that requires three approvals to onboard a vendor gets bypassed inside a quarter. We design governance with realistic friction: more rigor for high-risk categories, less for low-risk, all auditable, all defensible.

If your data governance program reads the same for every client, it is governance theater. The work that holds is the work tuned to your operating reality.

Built for your Microsoft estate

Your data stays in your tenant.
Your ERP stays in place.

DataQubi runs inside your Azure subscription, on your Fabric workspace, against your existing ERP. No data leaves your environment. No platform migration. No rip-and-replace. The hardening compounds inside infrastructure you already pay for.

  • Tenant-resident. Compute, storage, and processing all stay inside your Azure subscription. We never extract data to our servers.
  • Fabric-native. Built on Microsoft Fabric (OneLake, Notebooks, Lakehouse). Your data team owns the artifacts after we leave.
  • ERP-agnostic. Works against SAP, Oracle, NetSuite, Dynamics 365, and Microsoft AX/GP. Read-only connection. Zero production impact.
  • Purview-compatible. Classification and lineage flow into your existing Purview catalog. Governance you already invested in.
  • Copilot-ready. The hardened supplier layer becomes the trustworthy context for Microsoft 365 Copilot and AI Foundry agents.
  • Audit-grade lineage. Every match, merge, and classification decision logged with source, rule, and timestamp. Defensible at year-end.

Next steps

Find what is hiding
in your supplier data.

Pick the path that fits where you are. Three ways to start, each calibrated to a different level of intent.

For buyers
Schedule a 20-minute strategy call
No pitch deck. We look at your supplier data, identify where cash is hiding, and tell you whether a deeper engagement makes sense.
Book the call
For evaluators
Read the 90-Day Vendor Cleanup Playbook
The operator-grade playbook for supplier normalization, duplicate control, and AI-ready master data on Microsoft Fabric. CFO and procurement teams.
Open the playbook
For researchers
Browse the resources hub
Articles on AI governance, agentic enterprise architecture, CIO and CFO alignment, and the data hardening required to make Copilot land.
Browse articles
No ERP replacement Azure tenant deployment Full audit lineage Copilot and Fabric ready