AI Advisory

Meeting regulatory requirements takes a combination of services

RBI's model risk management guidance and global MRM frameworks don't hand you a checklist. They're principles-based, and what satisfies them depends on your models, your use cases and your risk profile, not a standard template applied the same way everywhere.

That means the right response is rarely one service. It's some combination of governance and policy, controls and risk management, data and model management, monitoring and reporting, independent assurance and regulatory advisory, sized to what you're actually running. We bring these together as one engagement, not a set of separately scoped projects that don't talk to each other.

How we work

The specifics differ across data foundations, controls, monitoring and MLOps, but the shape of the engagement is consistent.

Data foundations for GenAI and ML

Better GenAI and ML outcomes start with data that has context, not just volume. We help organisations structure their data, build ontologies, and map it into graph databases, so systems have the relationships between data, not just the data itself.

Data quality for GenAI and ML

Data quality isn't one-size-fits-all. What counts as fit for purpose depends on the AI or ML use case, not just whether the data already works for existing business reporting.

ITGC in the GenAI world

Access controls are foundational, but there's no single GenAI architecture. Controls can sit at retrieval, in broader access with output controls, or across an agent's data, tools and memory, and where they sit changes what "adequate" actually looks like.

The same control objective can be met in genuinely different ways: retrieval controls, deterministic guardrails, output controls, a second model acting as evaluator, human review, or several of these combined. We assess whether the design chosen is adequate for the architecture, use case and risk level, not whether it matches a specific mechanism we expected to see.

Continuous monitoring turns assurance into an active control

A control that's only checked once a quarter isn't controlling anything in between. We help design monitoring that catches material deviations as they happen, not at the next audit cycle.

MLOps: explainability and versioning aren't optional extras

Explainability isn't one thing. A regression model can often be explained through its own structure. Complex ML like XGBoost or ensembles needs dedicated tooling to understand feature contribution. Neural networks and foundation models need evidence built around inputs, context, behaviour and the conditions under which outputs can be relied on. We assess whether the explainability approach fits the model actually in use, not a generic standard applied to all three.

Reproducibility depends on versioning everything that influences the output, not just the model. For GenAI, that list is longer than most MLOps practices were built for: prompts and templates, retrieval data and embeddings, orchestration and tool-chaining logic, guardrails, and the evaluation sets used to test it. Miss one of these and you can't actually reproduce how an output was generated, which means you can't really explain it either.