We help boards and executive teams decide where AI belongs in their organisation, what it will return, how it will be governed and which platforms to commit to. The work is model-agnostic, grounded in your data and processes, and structured so that the first ninety days end with something in production.
AI transformationOur advisory practice is organised around the questions we are most often asked by chief executives, chief risk officers and technology leaders. Each has a defined method and a written output.
A structured review of data, infrastructure, skills, governance and process maturity across the functions that would adopt AI first. The output is a scored baseline, the gaps that would block delivery, and the order in which to close them.
We identify candidate use-cases with process owners, score each on value, feasibility, risk and data readiness, and build a portfolio with explicit return assumptions your finance function can challenge.
Policies, roles and controls for model risk, auditability and data protection. We align the framework with your existing risk architecture and with the Digital Personal Data Protection Act, 2023, so that AI governance is an extension of what your board already oversees.
Independent evaluation of foundation models, orchestration platforms and hosting options against your requirements for accuracy, cost, residency and vendor risk. We recommend an architecture that can change models without rebuilding the pipeline.
Adoption fails when the people whose work changes are not part of the design. We plan role changes, training, incentives and communication alongside the technology, and measure adoption as a programme metric.
Advisory work can hand directly to our engineering practices: agentic pipelines for the first use-cases, data engineering for the foundations they need. The same delivery lead stays with the programme through both.
Agentic flow pipelinesThe roadmap is deliberately short. It is long enough to establish governance and prove one use-case under real conditions, and short enough that the organisation learns from a working system rather than from a plan.
Readiness assessment interviews across business, technology, risk and data functions. Executive workshop to agree ambition, constraints and the decision rights for the programme. Output: readiness scorecard and programme charter.
Use-case discovery with process owners; scoring and sequencing; draft responsible-AI policy and model-risk tiering aligned with existing risk committees and the DPDP Act. Output: prioritised portfolio and governance framework for board review.
Evaluation of candidate models and platforms against the first use-case's requirements; residency, security and cost analysis; data access and quality remediation for that use-case. Output: platform recommendation and architecture decision record.
Engineering of the first pipeline against an evaluation suite assembled from your historical cases; human-approval checkpoints and audit trail in place from the first build. Output: release candidate with eval results and runbook.
Limited production release under monitoring; adoption measurement; lessons fed back into the portfolio and governance. Output: production sign-off, quarterly programme plan and a board update with measured results.
We do not resell any foundation model and our platform recommendations are documented with the alternatives considered. Silver AI is offered where it fits and not otherwise.
Every value case states its baseline, assumptions and measurement method. We would rather present a smaller, defensible number than a large one that cannot be traced.
AI oversight is attached to existing risk, audit and technology committees rather than to a new structure that competes with them. As a listed company ourselves, we design for the scrutiny a board expects.
A readiness assessment takes two weeks and produces a scorecard your board can act on.