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Golden records don't stay golden on their own. We design the match, merge, survivorship and stewardship logic that keeps them trusted, then build the pipelines that carry those records to every ERP, channel and marketplace that depends on them.
Master data work almost always begins with one of these six sentences. Pick the one you've said out loud this quarter.
"It takes weeks to add a new product to our channels."
AI extraction reads supplier feeds, spreadsheets and PDF spec sheets, then hands structured attributes to a governed workflow. A new SKU goes from received to enriched, approved and syndicated in hours instead of weeks.
"The same item exists five times, five different ways."
We profile the real overlap across your ERPs, tune matching on the attributes that genuinely discriminate between records, and build survivorship your business can defend in an audit.
"We bought an MDM platform and it's half-implemented."
We take over mid-flight builds. What the previous integrator got right, we keep. What doesn't hold up, we rework, against a re-baselined plan you can actually commit to.
"Every acquisition adds another product catalogue."
Fold acquired catalogues into one taxonomy and attribute model without disrupting ongoing operations. The legacy match engine keeps running in parallel until cutover has proved itself.
"Our AI initiatives keep hitting bad master data."
Search, recommendations and agents inherit whatever your master data believes. We fix the foundation, meaning attribute coverage, lineage and trust signals, then instrument it so it stays fixed.
"Nobody owns the data, so nothing gets decided."
Domain ownership with names against it, a decision forum that actually closes items, and written data contracts between the systems that produce data and the ones that consume it.
Many MDM programmes stall when governance becomes a queue of manual reviews. We design for straight-through processing first, routing only genuine exceptions to a steward.
Taxonomy, attribute models, family and variant hierarchies, packaging levels and unit-of-measure logic that survive contact with real catalogue data.
Hierarchy-aware party mastering across ship-to, bill-to and parent account structures with vendor equivalence resolved consistently across source systems.
Matching tuned on your own data, not a vendor default. Survivorship rules you can explain to the business and audit line by line.
Ownership, approval paths and data contracts per domain, plus a steward console that surfaces only the records your rules genuinely could not resolve.
Channel-specific transforms stay out of the golden record. Publish to commerce, marketplaces, ERP and partners from one source, with channel IDs written straight back.
Profiling before design, not after go-live. Fill-rate, conflict and drift metrics wired into dashboards, so a regression arrives as an alert rather than a customer complaint.
Large firms staff MDM with headcount. We staff it with a small pod of senior platform engineers, each paired with AI accelerators that absorb the volume work: Profiling, classification, attribute extraction, rule drafting, test-data generation and regression checks.
The engineer stays accountable for every rule that reaches production. The accelerator does the reading, the drafting and the repetition. That is how a 6-person pod covers ground that normally takes 20.
Accelerators propose. Engineers approve. Nothing reaches a production rule set without a named human owner and a decision record.
Purpose-built for master data work and proven on live client engagements, not in a demo environment. They sit alongside our platform accelerators: SchemaSense, DFCA and AskQL.
Reads supplier PDFs, spec sheets, spreadsheets and product images, and returns structured attribute values mapped to your target model. Every value carries a confidence score and the source snippet it came from, so a steward verifies in seconds instead of retyping.
Crosswalks legacy category structures onto a new taxonomy, proposes a leaf assignment for every SKU, and flags the nodes where classification is genuinely ambiguous. Fill-rate analysis shows which level of the hierarchy your attribute data can actually support.
Decodes manufacturer part numbers, normalises vendor names, and clusters items into product families and variant sets before they ever reach the MDM platform, so the platform materialises decisions rather than guessing at them.
Drafts and regression-tests survivorship and validation rules against your full data set, then keeps watching published golden records for conflict, fill-rate drops and drift long after go-live.
We concentrate on sectors where product and party data carries operational weight, where a wrong attribute means a wrong shipment rather than just a wrong report.
Distributors carry other people's products, in other people's formats, at every packaging level. We model pack hierarchies and orderable units properly, so pricing, inventory and fulfilment all resolve to the same item.
Commerce platforms impose hard constraints: A fixed number of option axes and strict variant rules. We keep those constraints in the channel layer, so the golden record stays a true description of the product.
Duplicate material masters quietly inflate inventory and distort spend analysis. We unify item, material and vendor data across ERP instances without stalling plant operations during the transition.
Four programmes, four industries, the same discipline: Profile first, govern the exceptions, and publish a record the business can trust.
TriMark is North America's largest foodservice design, equipment and supplies distributor. Growth by acquisition left product data spread across multiple ERPs with no single definition of an item, and a commerce roadmap that could not move until that was fixed.
A multi-banner outdoor and sporting goods retailer built from a decade of banner acquisitions. Loyalty, point of sale and commerce each held a different version of the same customer, and the storefront's variant filters broke every time a new product line launched.
Client makes industrial components across plants running SAP and JD Edwards side by side, the legacy of two plant acquisitions. Duplicate material masters were inflating inventory, distorting spend analysis and occasionally sending engineers after the wrong part.
Client sells household and personal care products through a dozen major retail partners, each with its own new item form. Formulation and regulatory attributes lived in one system, commerce and marketing content in another, and GDSN submissions were rejected often enough to delay listings by weeks.
Platform-fluent, not platform-tied. The recommendation follows your domains, volumes and existing estate, and sometimes the right answer is the platform you already own.
Showing all 29 platforms we deliver on.
No twelve-week strategy phase that produces a slide deck. Every stage ships a working artefact you keep, whether or not you continue to the next one.
We profile your live data before designing anything: Duplicate rates, fill rates, attribute conflicts, and which fields actually discriminate between records.
Taxonomy, attribute model, match and survivorship rules, and the governance operating model, with every decision captured as a signed decision record.
Platform configuration, upstream pipelines, integrations and outbound syndication, built in sprints with a working demo against real data every two weeks.
Phased cutover with the legacy engine still running in parallel, then managed stewardship and quality monitoring, or a clean handover to your team.
Straight answers to the questions we hear before every governed-data engagement.
In three specific places. Ingestion: extracting structured attributes from supplier PDFs, spreadsheets and images that would otherwise be keyed by hand. Design: profiling catalogues, proposing taxonomy crosswalks, and drafting match and survivorship rules an engineer then reviews and tunes. Operations: monitoring published records for conflict and drift. What AI does not do is own a rule. Every rule that reaches production has a named human owner.
A pod is a small, fixed team of senior specialists, covering architect, configurators, data engineer, steward and test lead, with AI accelerators embedded in the workflow rather than bolted on afterwards. Because the accelerators absorb the volume work that normally drives headcount, a pod delivers the coverage of a much larger team. You pay for judgement, not for keystrokes.
Yes, and we do it regularly. We start by evaluating the existing build honestly: what holds up, what needs rework and what should be abandoned. You get a re-baselined plan with the reasoning behind every call, so the decision to keep or rebuild stays yours rather than becoming a black box.
It depends on your domains, data volumes and existing estate. Stibo STEP is strong where product data is deep and multi-domain governance matters. Semarchy suits fast time-to-value and iterative rollout. Informatica and Reltio fit large customer-centric estates. We're certified across the major platforms, and we'll help you determine whether the platform you already have is the right fit.
A profiling report in two to three weeks, a signed-off data model in six to nine, and a running pipeline producing golden records against real data inside the first delivery quarter. Total programme length depends on domain count and integration surface, but you should never wait a quarter to see working software.
Whether you're scoping a first MDM programme or rescuing one that stalled, we'll profile a real slice of your data and show you what a governed record looks like, before you commit to a full engagement.