Data & AI Governance
Policies, lineage, and ownership defined before the pipelines are built – the foundation for safer enterprise AI adoption and audit-ready ML deployments.
Enterprise data consulting and AI-ready data platforms that turn raw data into decisions that move your business forward – with the data engineering, machine learning foundations, and governance to make it stick.
Most organisations have more data than they can act on – and most AI initiatives die there. Our data consultancy builds AI-ready data platforms, AI-augmented data pipelines, and production ML foundations that close the gap between data collected and decisions made. Data governance, lineage, and quality engineered in from the architecture stage – not bolted on once the models start drifting.
Five capability areas, each governed by senior Data practitioners and tied to a clear business outcome. No capability exists for its own sake – each one serves your programme's specific objective.
Policies, lineage, and ownership defined before the pipelines are built – the foundation for safer enterprise AI adoption and audit-ready ML deployments.
Event-driven architectures and high-throughput streaming pipelines built for both analytical and AI workloads – ready to feed models in production, not just dashboards.
AI agents assist pipeline design, schema generation, and automated quality checks – compressing build timelines and surfacing data issues before they reach the model.
From dashboards to production ML – insight, decision support, and intelligent automation that give decision-makers clarity, not noise.
Cloud-native warehouses and lakehouses architected for query performance, cost efficiency, and the scale that AI training and inference workloads demand.
A selection of data programmes our clients have entrusted to us – from enterprise-scale pipeline engineering to business intelligence transformations.
A selection of the senior Data practitioners who lead and deliver our client engagements. The people you meet in the discovery are the people who deliver.
Meet just some of the Vertex Agility Data team.
Common questions we get from senior technology leaders evaluating this work. Direct answers, no hedging. Open one to read in full.
A data platform built to support AI workloads alongside analytical ones. Streaming and batch ingestion in the same architecture. Feature stores that bridge analytics and ML. Governance that handles both human and model access. Lineage that traces every prediction back to its training data. The foundation that makes production ML possible without a re-platform six months in.
As architecture, not as deliverables that arrive at the end of the project. Governance metadata is captured at ingestion. Lineage is tracked through every transformation. Access control runs as policy-as-code. The lineage is queryable from the consuming application – an auditor traces a customer decision back to its source data in minutes.
Yes. Production ML platforms with model versioning, automated retraining, feature stores, A/B testing infrastructure, and monitoring across both model performance and data drift. Same operational pattern whether the models are classical ML, deep learning, or LLM-based. Deploy with the scaffolding required to run them reliably in front of real users.
Data quality is a first-class SLO. Enforced at ingestion. Monitored continuously. Quality failures alert the engineering team the same way uptime failures do. Quality contracts get designed between source systems and the platform – producers know what’s expected of them, consumers know what’s already been validated.
A greenfield ML platform with governance, monitoring, and a first production model lands in 12 to 20 weeks. Adding ML production capability onto an existing data platform takes 8 to 12 weeks. The variable here is governance maturity, never engineering complexity. Organisations with strong data governance already in place ship considerably faster.
Four areas, one standard. Each specialism is led by senior practitioners with deep domain expertise – explore the others.
Most AI deployments are performance theatre. Our generative AI consulting practice integrates AI – from workflow automation and intelligent decision-making to custom LLMs and agentic systems – where it demonstrably pays back, and we'll tell you directly when it won't.
Explore AI ConsultancyAI-assisted cloud architecture and migration across AWS, Azure, and GCP. AI agents generate IaC, forecast spend, and harden zero-trust controls – with FinOps governance and compliance built in from day one, not bolted on after the bill arrives.
Explore Cloud ConsultancyEngineering without architectural governance is expensive rework. We deliver full-stack software engineering, microservices, and software modernisation through AI-augmented pipelines – compressing roadmap-to-production timelines without trading away quality.
Explore Software ConsultancySend us a brief and we'll come back within one working day with a senior Data consultant – and a clear sense of how to start.