Our Data & AI practice turns the terabytes your business already generates into operational outcomes — data platforms, warehousing, analytics, predictive models, generative AI and enterprise copilots, engineered for governance from day one.
Every enterprise has spent a decade buying BI tools, building dashboards, running analytics projects. The outcome is often a portfolio of reports nobody uses, a warehouse nobody trusts, and an AI pilot that never made it past the innovation lab. The technology baseline has moved — lakehouses, vector databases, enterprise copilots, agentic AI — and the competitive bar with it.
AI is no longer a research question. It is a deployment question. The organisations that will outperform over the next five years are those embedding AI in operational processes — fraud detection in payment flows, predictive maintenance on factory lines, AI copilots for knowledge workers, automated document extraction in back office. Not the ones running one more hackathon.
Our Data & AI practice delivers production-grade AI — not slideware. Every engagement starts from a measurable business use-case, builds on a governed data foundation, and ships models into live operations under MLOps discipline. AI systems we deploy pass the same audit, change-management, and security controls as the rest of the enterprise stack.
Every Digital Enterprise engagement is assembled from these modular services. Scope is agreed upfront, priced as fixed-outcome or time-and-materials, and governed by a single steering committee.
Strategy, target-state architecture, platform selection, roadmap — grounded in measurable business use cases, not vendor maturity models.
Modern data platforms — Microsoft Fabric, Databricks, Snowflake, Oracle — lakehouse patterns, medallion architectures, real-time streaming where latency earns its keep.
Enterprise BI — Power BI, Tableau, Qlik — with self-service discipline, governance, and performance engineered from the start.
Classical ML — forecasting, classification, clustering, anomaly detection — engineered with MLOps: feature stores, model registries, deployment pipelines, drift monitoring.
Production-grade generative AI — Microsoft Copilot, custom RAG, enterprise search, knowledge assistants — with evaluation, guardrails, and cost governance.
Agentic AI for customer service, operations, and knowledge work — bounded, auditable, integrated with enterprise systems.
Enterprise data governance — stewardship, lineage, quality, catalog, MDM — because AI is only as trustworthy as the data beneath it.
AI risk frameworks, EU AI Act gap analysis, model-risk governance, bias/fairness testing — essential for regulated sectors.
Domain-specific models — demand forecasting, credit risk, churn, predictive maintenance — and prescriptive optimisation for scheduling and pricing.
Every Digital Enterprise engagement follows the same reference architecture — adapted to your scale, cloud posture, and compliance requirements. This is the stack-level view we present to steering committees and auditors.
Most engagements combine multiple capabilities. These are the practices that most frequently operate alongside this one — each with dedicated leads, certified engineers, and standing playbooks.
Different entry points, same practice. Whether the trigger is a strategic initiative, a regulatory deadline, a new system, or an operational problem, the engagement pattern is recognisable.
Credit decisioning, fraud pipelines, transaction monitoring — under explainability and model-risk discipline. Integrated into core banking.
Sensor-to-cloud pipelines, anomaly-detection models, prescriptive scheduling — integrated with EAM. 6–9 months.
Full Copilot deployment — readiness, data security, governance, change management — measurable productivity.
Structured opportunity assessment — processes mapped against GenAI applicability, prioritised use-cases with ROI, production-pilot roadmap. Two weeks.
Digital Enterprise is platform-agnostic by design — we lead with the right tool for your scale and compliance load, not the one that pays us the highest margin. Our engineers hold certifications with every major vendor in this space.
Phased core banking modernisation across three subsidiaries — delivered against a central-bank audit deadline, a fixed-scope contract, and a zero-downtime commitment the steering committee demanded. The case study documents the scope, risks, and bankable business case.
Two weeks, fixed fee. Our leads map your data estate, current AI exposure, governance posture, and three highest-value AI use cases. Deliverable: 12-month roadmap with pilot commercials and target-state architecture.