An alternative to manual data cleansing
Matches, merges and cleans scattered records into one trusted golden master, with human stewardship and full audit trails.
A governed, auditable path to clean, trusted data
Most teams rely on manual spreadsheet work, one-off scripts, or generic deduplication utilities to bring scattered records into shape. These approaches are slow, error-prone, and leave no traceable history of what changed or why. Duplicate entries accumulate across procurement, finance, and operations systems, creating conflicting versions of the same supplier, product, or asset that erode confidence in every downstream report and decision. The longer the problem goes unaddressed, the more costly and disruptive a cleanup becomes — yet the tools most organisations reach for offer no structural way to prevent the same issues from recurring.
Why teams choose Allmaz over conventional data-cleansing tools
One trusted golden master record replaces scattered, conflicting duplicates across all connected systems, giving procurement, finance, and operations a single consistent source of truth.
Human stewards remain in full control — no AI proposal reaches production without explicit review and approval, so automated processing never bypasses your governance policies.
Reversible by design: every edit is stored as an override on top of the original source data, so any change can be rolled back at any time without data loss or a restore from backup.
A full audit trail and source lineage are maintained per record, providing a clear, timestamped history of what changed, who approved it, and where every value originated.
Automatic UNSPSC taxonomy classification brings internationally recognised structure and comparability to your catalogue, supporting spend analysis, supplier benchmarking, and compliance reporting without manual coding.
A deterministic, rule-based matching engine delivers consistent, explainable results that you can inspect and justify to stakeholders, rather than opaque probabilistic outputs that are difficult to audit.
What sets Allmaz apart
Golden master consolidation
Duplicate records from multiple sources are matched and merged into one authoritative golden master, eliminating the conflicting versions that slow down procurement, finance, and operations teams.
Deterministic matching engine
Matching logic is rule-based and transparent. You can inspect exactly why two records were linked, making results auditable and straightforward to explain to stakeholders and regulators.
Human-in-the-loop approval
Every suggestion produced by the system is held in a structured review queue. A human steward must approve it before it is published, so automated processing never bypasses your governance policies.
Reversible override architecture
Edits are stored as overrides on top of the original source data, never written back in place. Any change can be reversed without affecting the underlying records or requiring a restore from backup.
UNSPSC taxonomy classification
Records are automatically classified into the UNSPSC hierarchy, giving your catalogue a consistent, internationally recognised structure that supports spend analysis, supplier benchmarking, and compliance reporting.
Full audit trail and source lineage
Every record carries a complete history of its sources and the decisions made about it. Auditors and data owners can trace any value back to its origin at any time, with no gaps in the chain of custody.
How Allmaz cleans your data
Frequently asked questions
Can Allmaz overwrite or delete my original source data?
No. Allmaz is reversible by design. Every edit is stored as an override on top of your source records and is never written back in place. Your original data remains intact, and any change can be rolled back at any time without requiring a restore from backup.
What happens if the matching engine makes an incorrect suggestion?
No suggestion is published without human approval. A steward reviews every proposed match or merge before it reaches the golden master. An incorrect proposal is simply rejected and logged rather than applied, and the rejection itself becomes part of the permanent audit trail.
How does UNSPSC classification work in Allmaz?
Once a record is approved and consolidated into the golden master, Allmaz automatically classifies it within the UNSPSC taxonomy hierarchy. This gives your catalogue a consistent, internationally recognised structure without requiring manual coding or specialist taxonomy knowledge from your team.
How can I demonstrate to an auditor exactly what changed and why?
Every record in Allmaz carries a full audit trail showing each decision made about it, the identity of the steward who made it, and a precise timestamp. Source lineage is also maintained per record, so you can trace any individual value back to its origin data source at any point in time.
Is Allmaz suitable for teams with limited data engineering resources?
Yes. The human stewardship model is designed for domain experts, not just engineers. Reviewers work through a structured approval queue and do not need to write code, configure matching rules, or manage infrastructure to keep the golden master accurate and up to date.
Ready to replace manual cleansing with a governed, auditable process?
See how Allmaz matches, merges, and classifies your records into one trusted golden master — with human stewardship, reversible overrides, and a complete audit trail built in from the start.
Request a demo