Our Data & Analytics Roadmap is one of our most-value added engagements. We've completed more than 200 of them, and they typically run as a focused 4-to-8 week exercise with your leadership team. We assess your current state, map your business priorities to a sequenced set of initiatives, and deliver a clear, phased plan with the dependencies, investments and quick wins to make it real.

The output isn't a strategy deck that sits on SharePoint. It's a jointly-built, living plan with named owners, quarterly milestones, and a business case for every step.

We build modern, cloud-native data platforms on Azure, Microsoft Fabric & Databricks. Lakehouse principles, medallion architecture, clean separation of storage & compute, and the engineering practices to operate it without burning out a small team.

The modern data platform handles both structured and unstructured data and provides a stable foundation to build new data products.

We don't just hand it over: we co-develop with your engineers and analysts, transferring methodology and best practices so your team owns it from day one.

Most "data problems" turn out to be master data problems. Three systems with three versions of the customer. Products that change SKU between divisions. Manual hierarchies being maintained in Excel. Every report, dashboard, and AI model built on top inherits the confusion.

We help organisations unify their core entities (customers, products, materials, suppliers) with the right data models, stewardship workflows and operating model to keep master data actually master over time.

Modern data governance is operational, not bureaucratic. We help organisations stand up the operating model, the stewardship workflows, the data quality framework and the catalog & lineage approaches that make data trustworthy in practice. We start with the domains that matter most and grow from there.

As organisations move toward AI, governance evolves. The question shifts from "what data do we have?" to "what do our concepts mean?". We help our customers build that semantic backbone (business glossaries, concept hierarchies, ontologies) so that humans and AI agents reason over the same definitions.

Data governance tells you whether the data is trustworthy. AI governance tells you whether what you build on top is trustworthy. Two different questions, and increasingly two different practices.

We help our customers build the governance framework around their AI portfolio: model risk management, responsible AI translated into operational checkpoints, AI-readiness assessments, and (for agentic AI) the boundaries and observability that let the organisation trust agents to act on its behalf. When the framework is clear, teams ship faster, because legal, risk & procurement have the evidence they need to say yes.

We embed security into the fabric of the platform: row-level & column-level access, sensitivity labelling that flows from source through to reports & models, and DLP that protects without blocking legitimate work. Calibrated protection, not maximum restriction.

Copilot and other generative AI tools change the threat model. Every prompt is a potential data leak. Our Data Security Posture Management approach gives you visibility into how AI is actually being used: which sensitive documents are being fed into which tools, where shadow AI is running, and what your real exposure is. You can't protect what you can't see.

BI remains the discipline that turns raw data into the numbers people use to run the business every day. We design and deliver BI programs that stand the test of time: a single governed semantic layer, well-architected data models, role-based dashboards from exec to analyst, and the self-service capabilities that let business users answer their own questions.

It's also the grounding layer that every AI capability depends on. A trustworthy semantic model is what makes a copilot answer reliably instead of plausibly.

Traditional BI delivers dashboards. Conversational BI delivers answers. Business users ask questions in their own language and get trustworthy, governed responses, grounded in the semantic model rather than hallucinated.

We build Conversational BI on Microsoft Fabric Data Agents, Power BI Copilot, Qlik Answers and the Qlik MCP Server, with the governance & security to make it production-ready, not just a demo.

We're not building AI because we can. We build it because it has a real business impact, and we always look for the simplest path to value. Forecasting that influences inventory. Segmentation that reshapes marketing spend. Predictive maintenance that drives work orders.

Our Data Science approach is structured around problem framing rooted in business economics, MLOps that makes retraining routine, and integration into the processes where decisions actually happen. The models only matter when they move a number that someone cares about.

Generative AI use cases. Retrieval-augmented assistants. Intelligent document processing. And, increasingly, autonomous agents that execute multi-step workflows: processing orders, triaging tickets, reconciling data, orchestrating approvals.

The real challenge isn't building a single agent. It's building the operating system around them. Our Agentic OS brings the orchestration, guardrails, observability, cost controls and human-in-the-loop patterns that turn promising experiments into trustworthy capabilities running in production.

And around it all sits our AI Factory: a structured way of working that brings together clean data, the right technology, skilled users and responsible governance to scale AI across teams & domains. Not one project at a time, but a portfolio that compounds.