Atelier consulting · Data, AI & Automation

Trusted data first. Then AI that earns its place.

Most banks and financial institutions have invested in a modern data platform. Fewer can say which figures they trust or how a number in a board pack was produced. Zoral’s data specialists turn the platform you own into governed data that reporting, regulators, credit decisions and AI can rely on. Then they put models and guardrailed AI to work on it, under human-in-the-loop supervision.

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Data debt shows up in the boardroom, not only in IT.

The same customer appears several times, so nobody sees the full relationship.
Reports are reconciled by hand before they reach the executive committee.
Evidence for a supervisor is assembled after the question is asked.
Models are trained on data nobody has certified, then questioned by the risk committee.

Adding people rarely fixes this. It needs a method that fixes data once, for everything else to reuse.

Fix data once. Let every report, model and system reuse it.

01
Assess before you build
We measure a quality baseline and assess your sources, the data behind your KPIs and regulatory obligations, and what you run, so good work is reused, not redone. It ends with a plan for you to decide on.
02
One business area at a time
Each step takes one domain, such as a single customer view or a regulatory report pack, into production, and retires the legacy reports it replaces.
03
Quality and governance built in
Quality rules, written as code, each have an owner. Failed records are set aside with the reason recorded. Your catalogue holds owners, definitions and lineage as the platform runs.
04
A capability, not a dependency
Your team runs what we build, with the standards, runbooks and knowledge base to do it. Support is yours, ours or shared.

Data quality you can measure, and show to an auditor.

Data quality, above all the quality of the data behind predictions and decisions, is one of our core skills. In a new build, such as a digital bank or product on the Zoral Finance Operating System (fOS), quality is designed in from the first day. In an existing environment, we measure where it stands, find the gaps and decide where each is best fixed. Spending heavily on a legacy system due for retirement is rarely the right answer. For those gaps, a layered, medallion architecture turns raw data into certified data, ready for AI, machine learning and regulatory reporting.

Critical data first
Each element of the data behind your KPIs, regulatory reporting and credit decisions has an owner, a definition, rules and a scorecard the business reads.
Fixed in the right place
Where worthwhile, data is fixed at source, with the evidence sent to its owner. Otherwise, the medallion layers fix it before it reaches models or reports.
One view of each customer
Records are matched with clear rules and probabilistic methods. Data stewards decide uncertain matches, and their decisions are kept.
Traceable and controlled
Data movements are logged, counted and reconciled, so figures in scope trace back to their sources. The platform applies retention, access and residency rules.

On the platform you already own.

We work on the lakehouse and cloud you have chosen, including Databricks on Azure or AWS and Google Cloud. We use the platform’s own tooling and established practice: medallion layers, dimensional models for reporting, one approved definition for each key metric. Many of our data engagements use no Zoral product at all.

Connected to your digital bank

Where you run Zoral fOS, the same team implements it. Zoral Data Fabric records everything that happens across Zoral fOS and its solutions, with data quality built in. It becomes one of the main trusted sources of an enterprise data lake, which we extend with your other systems. Machine-learning models are tuned on Zoral Data Fabric and run in Zoral Decision Engine in real time at high volumes. Every decision is recorded for audit. Portfolio and dimensional models run on the certified lake or on your own platform, such as Amazon SageMaker, Databricks or Google Cloud, with data flowing both ways. Scores and decisions are written back to Zoral Data Fabric and on to the lake, so every team works from the same record.

Models that move business results, and that a risk committee can follow.

Our real-time models are built with Zoral’s data-science-driven MVP methodology: discovery with your business, risk and data teams; analysis; a working prototype tested in shadow or A/B mode against your current models; and a plan for production. Its results feed the model documentation we review with your risk, data science and compliance teams before any model is released or retuned.

Data before models
We measure whether data is invalid, incomplete, inconsistent, inaccurate or late. We then close predictive data gaps with consented, lawful data that would make a model more accurate, so the improvement lasts beyond the project.
A proven starting point
Zoral’s model library, refined over many lending deployments, covers acquisition, fraud, default, limits and pricing, retention, cross-sell, customer lifetime value and collections. We tune proven designs on your data.
Judged on results
Validated on unseen data, compared with your current scores, shadow-tested on live decisions, and judged by what they change in approvals, losses and margin.
Monitored and explainable
We track accuracy, using standard measures such as AUC, and drift against agreed thresholds. Your model risk process receives documentation and the main reasons behind each score. Your committee approves.

Guardrailed AI that does the routine work, while your people stay accountable.

Wherever AI runs, in Zoral fOS, on Databricks or on Google Cloud, it is guardrailed and under human-in-the-loop supervision. It uses approved data only, within permissions you set. A person approves what matters. Every action is recorded. In operations it reads and checks documents, automates identity and KYC checks against sources available in your market, and prepares work in credit and servicing. It does not make credit decisions. Accountability stays with your bank.

See Zoral Document Fabric · See Zoral Intelligence Layer (ZIL)

Ready for mission-critical work
Our consultants work with your teams to make each use of AI compliant before it goes live: the agent skills it follows, a knowledge base where every fact carries its evidence, a golden corpus of approved questions and answers, and regression tests every change must pass. This is part of our Data Product Engineering and DataOps Management Methodology and our AI-enabled DevOps.
In how we deliver
AI for Zoral, our governed delivery assistants, drafts routine programme work, such as conversions, tests and reports, for a specialist to approve. They work on code and metadata rather than customer data, are not trained on your data, and run within the environments and agreements you control.

Ways to start.

Data and AI assessment
Sources, quality baseline and priorities, ending with a roadmap and the first business area defined.
First business area into production
One domain delivered end to end on your platform, so you can judge the method on results.
Model review and tuning
How your models are fed, monitored and tuned, with practical recommendations and tuning where it pays.
AI into production, safely
One use case from pilot to production, with guardrails, a golden corpus, regression tests and the evidence your reviews need.

Alongside your leaders, advisers and integrators.

We work under your chief data officer’s direction, alongside the advisory firms and integrators you already use. Where it helps, senior Zoral people work as counterparts to your leads. Your teams sign off each deliverable.

Founded in 2004, Zoral has delivered over 100 projects. Our data specialists have worked with financial-services firms in the UK, Europe, North America, Asia and Australia, on AWS, Azure and Google Cloud.

Frequently asked questions

Data and platforms

Do we need Zoral fOS, or another platform?

No. We work on the platform you have chosen, with its own tooling. Where you run Zoral fOS, Zoral Data Fabric becomes one of the main sources for your data lake.

Can our data scientists keep their own machine-learning platform?

Yes. Portfolio models can run on Amazon SageMaker, Databricks, Google Cloud or what you use today. Zoral connects both ways. Their scores can feed Zoral decisions, and decision data flows back to them.

Our source systems are the problem. Can you still help?

Yes. Where a source can be fixed, the defect goes to its owner with the evidence. Where it cannot, the medallion layers correct and certify the data. Records that still fail stay visible and out of reports and decisions until resolved.

How do you show data quality to an auditor or supervisor?

Figures in scope trace back to their sources, with the rules and versions that produced them. Quality results are kept as evidence for each release.

AI, models and control

Do AI agents make decisions about our customers?

No. Credit decisions and customer treatment follow the rules and models your bank approves. Agents prepare and check work within the permissions you set, and what they do is recorded.

How is AI tested before a critical process relies on it?

Against a golden corpus and regression tests built with your teams, wherever it runs. Every change to a model, prompt or skill must pass them, and the results are kept for your compliance and model risk reviews.

What happens to our data when you use AI in delivery?

Our assistants work on code and metadata, are not trained on your data, and run within the environments and agreements you control. A specialist approves every artefact.

How do new models get approved?

Through your model risk process. We provide the documentation, validation, shadow-test results and monitoring plan.

Working together

Who owns what you build?

You own what we build for you: the code, the models trained on your data, the definitions and the documentation. Rights in Zoral products are set out in your contract.

Start with the data your next decision depends on.

Bring us the report, regulatory return or credit decision you trust least. A senior Zoral data specialist will show where its data comes from, what it would take to trust it, and a sensible first step.