Operator Brief / AIMDM by Azlan Data

Your AI strategy is a hallucination until you fix the data.

Every board is asking the same question: where is the return on our AI investment, and what is the risk if it fails? AIMDM is the answer. It cleans, consolidates and certifies enterprise master data so the data feeding artificial intelligence initiatives is trusted at board level. 2 to 4 weeks to first certified golden records in production, against the 12 to 24 weeks a traditional Master Data Management rollout takes.

Next: What it is →

What AIMDM is.

AIMDM (AI driven Master Data Management) is an enterprise platform built by Azlan Data. It addresses the AI trust problem that is stalling enterprise artificial intelligence initiatives: AI platforms are being built, but executives lose confidence because the underlying data is inaccurate, duplicated, or scattered across legacy systems. AIMDM ingests that data, profiles it for quality issues, applies a colony of specialised agents to clean and unify it, and produces certified golden records: a single trusted view of every customer, product, asset, and supplier. Agents handle the routine certifications; a human signs off where the data is ambiguous. The policy is simple. Only AIMDM certified data is permitted as input for AI initiatives and board reporting.

Next: Use cases →

Three things enterprises use AIMDM for.

The three use cases run against the same engine: ingest the data, profile it, route uncertainty to a steward, certify the resolved record as AI ready. Enterprises typically start with one data domain (Customer is the most common pilot domain), prove the engine against their own data quality issues, then scale to the next domain. The shape of the engagement is the same in all three.

AI readiness and trust restoration. Enterprises are losing executive confidence in their artificial intelligence initiatives because the underlying data is wrong. AIMDM rebuilds that confidence by enforcing a single policy: only certified master data feeds the AI. Every certification is logged, every routing decision is auditable, every governance step is reproducible. The board gets data it can defend; the AI gets data it can use.

Compliance and regulatory readiness. Network and Information Security Directive 2 (NIS2), Digital Operational Resilience Act (DORA), General Data Protection Regulation (GDPR), and Environmental, Social, and Governance reporting all require auditable, lineage tracked, accurate data. AIMDM produces that as the standard output of operating the platform: full data lineage, full audit history, automated quality dashboards that satisfy regulator and external auditor inspection without a project to assemble the evidence after the fact.

Cost recovery from data hygiene. Duplicate supplier records cause duplicate payments. Mismatched product records cause pricing leakage. Inconsistent customer records cause service errors and onboarding delays. The first year value of resolving these typically ranges from £0.4 million to £2.1 million on a single enterprise, before any AI value is layered on top.

Next: Validation →

Validation

Built for the boards that need to defend their numbers.

AIMDM is built by Azlan Data, an engineering firm focused on AI native platforms for asset heavy and data heavy industries. Azlan Data sits inside the small group of vendors who treat AI not as a feature bolted onto a legacy product, but as the core engine that decides every record. The deterministic rules are the safety net under the engine, not the engine itself. Every decision the platform makes is logged with the model version, the inputs, the confidence score, and the routing outcome. Compliance reproduction is a query, not a project.

Active pilots across two enterprise sectors. A pilot is live with a tier 1 European telecoms operator preparing a generative artificial intelligence rollout, where AIMDM is producing the certified master data the rollout will sit on. A second pilot is live with a regulated insurance carrier in a brokered distribution model, where AIMDM identified that the data quality gaps live at the broker file upload step rather than inside the carrier itself. That insight alone redirected the customer's internal data quality programme upstream to the broker layer.

What the platform produces. A typical enterprise running AIMDM on a single data domain sees a 60 to 80 percent reduction in defective records inside the first 90 days, and a over 70 percent reduction in manual data stewardship hours. The first measurable certification of AI ready records lands in week 4. Net first year benefit modelled across customers typically lands in the material range for an enterprise of that size, with payback inside 3 to 6 months.

Honest framing. Both pilots are pilot stage, not yet converted to multi domain production engagements. Mayfair21 is explicit about this. AIMDM is the right answer for an enterprise willing to run a 90 day pilot domain against its own data, validate the engine, then decide on scaling. It is not yet the right answer for an enterprise that needs reference customer logos from year five of production. Operators considering the platform are invited to discuss what pathway applies to their estate.

Next: What you could expect →

What a typical enterprise could expect.

Three domain shapes. One platform.

The shapes below anchor the typical conversation. Each is modelled against the AIMDM operating envelope reported across pilots, applied to a representative enterprise. The numbers exist to frame the enterprise's own business case, not to substitute for one. Mayfair21 validates every figure against the enterprise's own data before commercial commitment.

Pilot domain

One data domain, 90 days, board level proof.

Shape

The standard pilot. One data domain (Customer is the most common). 30 days mobilisation, 60 days first certified golden records live, 90 days operating handover and approval of the AI input policy at executive level.

Modelled outcome

60 to 80 percent reduction in defective records inside the pilot domain. Over 70 percent reduction in manual stewardship hours. First measurable AI ready certification by week 4.

60-80%

Defect reduction within first 90 days, single domain.

Multi domain rollout

Three to five data domains certified, 6 to 9 month window.

Shape

After pilot, AIMDM's per domain configuration migrates cleanly to additional data domains in tranches. Customer, Product, Supplier, Asset, Contract typically follow Customer in that order, depending on the enterprise's revenue model and the location of the highest value defects.

What the enterprise gains

Material first year recovery from duplicate payment elimination, pricing accuracy, and faster supplier onboarding. The bulk of the value moves on the second and third certified domain as the cross domain relationships become visible.

3-5 domains

Customer plus Supplier plus Product certified inside the 9 month window.

Estate wide certification

All enterprise master data domains certified, AI policy in force.

Shape

AIMDM becomes the standing reconciliation and certification layer across the enterprise. Sentry agents watch for drift continuously. Every artificial intelligence model and every executive report runs against certified data only. The board gets a dashboard view of trust, defect rate, and value realised.

What the board gets

Defensible AI. Audit ready compliance. An indicative dashboard view at maturity: data quality score above 94 percent, automated match rate above 88 percent, AI adoption above 75 percent, AI trust score above 99 percent. Value compounds year on year as more business processes consume certified records.

Policy in force

Only AIMDM certified data feeds the AI. Drift watched continuously.

All figures modelled, illustrative, and validated against the enterprise's own data before commercial commitment. The dashboard indicators describe what a mature AIMDM estate typically looks like at month 12, not delivered customer outcomes from a specific reference site.

Next: How it fits →

How it fits with your existing systems.

AIMDM does not replace the enterprise's existing systems. It runs above them, certifies the data they hold, and feeds certified records back to the consuming systems that need them. The combinations below cover the situations most enterprises present.

Against a traditional Master Data Management programme. Traditional Master Data Management rollouts typically take 12 to 24 weeks because they require a complete reference data model agreed before the first record is touched, plus the integration plumbing built top down. AIMDM inverts the order. The colony of agents starts profiling and resolving against the data as it sits, surfacing the reference model as a byproduct rather than as a precondition. First certified records inside 2 to 4 weeks. Boards get evidence of progress before the budget question for year two needs answering.

Against doing nothing and hoping the artificial intelligence works. Many enterprises have approved artificial intelligence budgets but no data foundation underneath. The AI vendor delivers, the chief financial officer asks for the saving, the answer comes back wrong because the underlying data is wrong, and the project loses board confidence. AIMDM is the layer between the AI ambition and the data reality. It is what makes the AI numbers defensible.

Against the in house data team building it themselves. Most enterprise data teams could build the cleaners and matchers AIMDM ships with. Few have the time, the agent orchestration experience, or the audit reproducibility design that compliance demands. AIMDM is the equivalent of buying versus building a security operations centre. Possible in house, faster and more defensible from a vendor that has already shipped it.

Against the Big Four data quality engagement. A Big Four consulting engagement on data quality typically delivers a slide deck of recommendations and a 12 to 24 week implementation plan executed by junior consultants. AIMDM is software that runs, not a report that is read. The engagement converts to outcome the same week as the agent goes live on the enterprise's data, not the same year.

Read the full capability and architecture inventory →

Next: About →

About

AIMDM, Azlan Data, and Mayfair21.

AIMDM is built by Azlan Data, an engineering firm focused on AI native platforms for asset heavy and data heavy industries. Mayfair21 represents AIMDM under its commercial representation framework. Engagements are opened at executive level at the enterprise, conducted on a paid analysis basis, and underwritten by Mayfair21's commitment to deliver at least three times the analysis fee in measurable return, or the analysis is free. Procurement still runs procurement; the engagement enters that process with executive sponsorship already attached, the technical groundwork visible, and the commercial structure already agreed. It does not remove the competition. It does mean the conversation starts at a different altitude.

Platform capabilities and architecture →
Next: Contact →

Contact

Open to a conversation.

For technical evaluations, pilot scoping, or introductions to the team behind AIMDM.

contact@mayfair21.com