AI & Cloud Consultancy — London
Timeless Advisory.
Enduring Trust.
We help enterprises design, build, and recover AI and cloud systems where failure is expensive.
- Senior technical judgement.
- Implementation discipline.
- Systems that hold in production.
What we do
Four areas of practice
Production AI systems
We take AI from Jupyter notebook prototypes to the kind of production deployment that survives contact with real data, real scale, and real audit. That means inference architecture, latency and cost engineering, observability, and the governance paper trail that regulated industries require.
Cloud platform engineering
Platform teams inherit decisions made under pressure. We bring those decisions into the light: landing zones, cost structure, reliability, developer experience. We work embedded with your team, not in parallel to it, so knowledge transfers rather than depends on us.
AI strategy advisory
When your board or exec team needs a frank technical view on AI investment, vendor claims, or internal capability, we provide one. Written, spoken, or workshop format. Not slide decks that require a consultant to decode them afterwards.
Technical due diligence
For investors or acquirers looking at AI-heavy businesses, we examine what is actually in production: the codebase, the data pipeline, the infrastructure, and the team's ability to maintain what they have built. We write findings plainly.
AI Control Plane
Control over model and tool requests
For platform, security and technology leaders who need to show who authorised an AI action, which policy applied and what actually happened, while keeping model providers replaceable.
Two weeks
AI Control Plane Review
A standalone, paid assessment of one real AI execution path: an execution and trust-boundary map, a prioritised control-gap assessment, a recommended target architecture, a first governed execution-path proposal and a remediation backlog.
Six to eight weeks
AI Control Plane Accelerator
Optional. Implements the agreed first governed path with your team, connecting identity, policy enforcement and audit evidence, with documentation and handover.
Typical situations
- Your AI prototype works, but it's not trusted in production.
- Your cloud platform is expensive, fragile, or hard to change.
- You need an outside technical view before committing budget.
- Your team needs senior implementation assistance.
Selected work
Experience
Work led by Jason Southgate. Organisations are described by sector and scale rather than named, and no endorsement is implied.
Led exploratory data analysis and modelling-readiness for an 18-month spare-parts forecasting project, designing a leak-safe roadmap from statistical baselines to ML/neural/foundation model forecasting, with RAG and agentic layers for diagnostics, planning and decision support.
Analysed Nielsen BookScan sales data to identify books with commercially viable long-term sales, with time-series decomposition, ARIMA, XGBoost, and LSTM forecasting. Delivered recommendations to help independent publishers optimise stock levels, reprints, and acquisition strategy.
Applied unsupervised NLP (BERTopic, LDA, BERT emotion classification, and LLM-assisted extraction) to Google and Trustpilot customer reviews, demonstrating a stable shortlist of operational complaints suitable for branch-level intervention.
Led the security hardening of an Azure Cloud Foundation product to CIS and ISO benchmarks, delivering compliance-as-code through PowerShell and CI/CD pipelines while running point as hands-on Azure Administrator and Security Engineer.
Set the multi-cloud reference architecture for the platform estate on Terraform, Kubernetes and Serverless platforms, codifying the delivery stack: module standards, GitOps, secrets, and DR posture, as the default target for global delivery teams.
Recovered a stalled cloud migration programme. Diagnosed the engineering and organisational blockers, restructured the delivery plan, and embedded with the platform team as senior architect for six months to see it through.
Owned the delivery platform for a tier-one financial application, integrating Kubernetes, Jenkins, Nexus, Vault, Elasticsearch, and Traefik into a cohesive deployment pipeline. Delivered ahead of plan under an agile cadence.
Built in the same workshop
We ship products as well as advise
Vakyya
For enterprise media too confidential for ChatGPT, DeepL, or a freelancer’s laptop. A UK and European workflow for transcribing, translating, captioning, and enriching sensitive internal media without losing jurisdictional control.
vakyya.comGovernator Pro
For enterprises putting models, tools and agents into real work. One organisation-controlled layer between users and AI execution: identity, policy, routing, tool authority and evidence for every AI action. MVP available now.
Free PDF briefing
The Governance Gap in Enterprise AI
Why models, tools and agents need a defensible control plane, not just gateways, provider dashboards and policy documents. Written for AI platform, security, risk and enterprise architecture leaders.
Jason Southgate
About
Jason Southgate
I have spent almost 30 years designing and building production systems for organisations where getting it wrong is simply not an option: banking, retail, automotive, and other regulated industries. Most of that work has been in cloud and platform engineering, with the last few years concentrated on AI systems and the specific problems that arise when you try to make them reliable.
I have completed a University of Cambridge programme in data science, machine learning and AI.
We work directly with a select few clients at a time. This is a deliberate choice. The value we offer is attention, not scale.