AI Strategy & Implementation
High-impact use cases tied to executive-level business goals – not a proof of concept in search of a problem. Roadmap, prioritisation, and a defensible plan for production deployment.
Our dedicated generative AI consulting practice – for enterprise programmes where AI itself is the product. Custom LLMs, intelligent automation, and agentic systems shipped to production. Honest counsel on where AI pays back, and where it won't.
Most AI deployments are performance theatre. Our Applied AI & GenAI consultancy ships enterprise AI into production where it demonstrably accelerates outcomes – large language models (LLMs), computer vision, intelligent automation, and agentic AI – built on honest assessment and architectural rigour. When AI isn't the right tool, we'll tell you. When it is, we'll show you exactly where the payback lives.
Five capability areas, each governed by senior AI practitioners and tied to a clear business outcome. No capability exists for its own sake – each one serves your programme's specific objective.
High-impact use cases tied to executive-level business goals – not a proof of concept in search of a problem. Roadmap, prioritisation, and a defensible plan for production deployment.
Fine-tuned LLMs, computer vision, and bespoke models built for your data, your constraints, and your production environment – not a generic capability dropped into an existing architecture.
Multi-agent systems and intelligent automation built for enterprise deployment – architected for reliability, cost efficiency, and the governance the regulators are about to demand.
High-performance pipelines designed for training and inference workloads. AI is only as good as the data it trains and infers from – we architect the data foundation alongside the model.
Responsible deployment with compliance frameworks, model monitoring, evaluations, and red-teaming that hold up under regulatory scrutiny – from EU AI Act readiness to ongoing risk management.
A selection of AI programmes our clients have entrusted to us – from regulatory compliance automation to enterprise-scale machine learning deployments.
A selection of the senior AI 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 Applied AI & GenAI team.
Common questions we get from senior technology leaders evaluating this work. Direct answers, no hedging. Open one to read in full.
Start with the business problem and work backwards. If a rules engine, better data, or a redesigned process would solve it, we say so on the call. AI earns the engagement when the problem involves pattern recognition at scale, content generation under hard constraints, or decisions where partial automation lifts throughput without dropping the human judgement. A surprising number of AI projects fail because the problem never needed AI in the first place.
Both. The workload decides. API-based models from Anthropic, OpenAI, and Google are the default for most enterprise use cases – production-ready, well-priced, improving every quarter. Custom or fine-tuned models earn their keep when the domain language is specialised, when latency budgets are tight, or when data residency rules out third-party APIs entirely. We’ll recommend whichever pays back faster.
Governance is architecture, not a folder of PDFs. Model cards, decision logs, and feature attributions sit inside the inference path itself. Explainability is a runtime requirement on every production model we ship. When an auditor needs to understand why a model decided something, the answer is in the system – not in a spreadsheet someone has to rebuild from logs.
A system where an LLM coordinates multi-step actions across tools, data sources, or APIs – calling functions, reading the results, picking the next step, looping back when something doesn’t fit. Worth building when the workflow has too much variability for a fixed pipeline but the individual steps are well-defined and observable. Wasteful when a regular workflow would do the job for a tenth of the cost. We’ll tell you which one yours is.
A first production use case with governance, monitoring, and real users typically lands in 8 to 14 weeks. Larger programmes – agentic systems, custom models, full GenAI platforms – run 4 to 8 months. Most of the timeline is data foundations and governance. The model integration itself rarely takes long.
Four areas, one standard. Each specialism is led by senior practitioners with deep domain expertise – explore the others.
AI-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 ConsultancyMost organisations have more data than they can act on. We build AI-ready data platforms, AI-augmented pipelines, and production ML foundations that close the gap between data collected and decisions made – with governance and lineage architected in from the start.
Explore Data 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 AI consultant – and an honest assessment of where AI can move the needle for your organisation.
