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Your AI programme is not stalled. Your data is.

Every model, dashboard and agent inherits whatever state your masters are in. We fix that, prove it is fixed, and keep it fixed — with rules your own business users can write and read.

Why quality decays after every cleanup

Nobody is measured on it

Nobody is measured on it

The cleanup project ends, the consultants leave, and no one owns the number afterwards. Three years later the same business case gets written again. This is the single most common pattern we see.
The masters were never really resolved

The masters were never really resolved

Customer identity split across systems. Products with attributes that mean different things in different places. Suppliers duplicated under four spellings. Everything downstream inherits it.
Icons Generative AI for Retail

AI made it urgent

A dashboard with bad data produces a wrong number somebody can sanity-check. A model with bad training data produces a wrong number at scale, confidently, and nobody catches it. That is why this became a 2026 problem rather than a 2019 one.

What the programme covers

Data quality rules and continuous monitoring

Rules and continuous monitoring

Quality rules and scoring that run continuously rather than as a one-off pass — deduplication, standardisation and validation at millions-of-records scale. A no-code rule builder, so the people who know what correct looks like can write the rule themselves.
Master data stewardship for product, supplier and customer records

Master data stewardship

Product, supplier and customer masters, with a named owner and a per-domain score. The measurement half is what stops you repeating the project.
Data cataloguing and lineage across data sources

Cataloguing and lineage

Where a number came from, through every hop, so an answer can be defended rather than just quoted.
Data access governance and audit readiness

Access governance and audit readiness

Who can see what, and the evidence trail to show it held.
Data governance platform implementation with cataloguing, classification and lineage

Governance platform implementation

Microsoft Purview for Microsoft-estate clients — catalog, classification, lineage and policy.

Measurement is the product

Most data-quality work is sold as a cleanup. A cleanup is a project with an end date, and quality starts decaying the day it finishes. We sell the standing score instead: per domain, with an owner, visible, so decay is something you can see happening rather than discover.

Where this has run

Home Depot

Twelve years. We replaced their Oracle data-quality platform with our own framework, and integrated with Stibo so that seller uploads are quality-flagged before they publish rather than after. Across a catalog of more than 400 million SKUs, with 30% faster market insights.

SiteOne

An AI-powered data-quality framework standardising more than a million product and supplier records across a $4.7 billion distribution business.

Three common entry points

An AI initiative that stalled

The pilots worked and nothing reached production, because the training data could not be trusted. Governance is the unblock, and it is usually cheaper than the pilots were.

An audit or compliance deadline

A finding, a regulation, or a programme that needs evidence rather than assurances.

A merger

Two of everything, and no agreement on which is right. Consolidation is a data problem wearing a systems problem’s clothes.

What this unlocks

Three ways to buy this

Decision Sprint 2

Decision Sprint

Six weeks, fixed scope, one decision domain. You get a working pack running in shadow mode and a measured read on what it is worth before you commit to more.

Managed pod 1

Managed pod

A standing Iksula team that runs the layer for you — owns the exceptions, the adoption and the outcome, not just the uptime.

Transformation programme 1

Transformation programme

Outcome-based, where the scope is a business result rather than a deliverable list.

Frequently asked questions

Because nobody was measured on keeping it clean. That is not a criticism of your team — it is what happens when quality is a project rather than a standing score with an owner. Continuous per-domain scoring is the specific thing that stops the cycle.

Yes, and they should. The people who know what a correct product record looks like are rarely the people who write SQL. A no-code rule builder is not a convenience feature here — it is what makes the rules stay current after we leave.

No. We implement platforms where they fit, Microsoft Purview in particular, but a platform is a place to record decisions, not a substitute for making them. The work is the stewardship, the rules, and the owner.

You get a score per domain, continuously, with a name against it. That is the deliverable. If the score cannot go down visibly, nobody is really measuring it.

With an audit of one domain — usually product or supplier, because that is where the pain shows first. It is short and fixed-price, and it tells you what the rest of the programme should be, including whether there is one.

Start with one decision

Tell us the decision your team argues about most, and we will tell you what it would take to make it once, properly, on the data you already have.

Connect with our experts

DJ Website ImageB 1

DJ Basumatari

Chief Executive Officer

Abhishekh Jain BW

Abhishek Jain

Director - Solutions & Innovation

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