Azure OpenAI and Microsoft Foundry
The fastest path to frontier models, with enterprise data terms, regional deployments and private endpoints. Our default for most workloads.
- Azure OpenAI
- Microsoft Foundry
- Private Link
Services
Four tightly scoped services for CTOs, VPs of Engineering and technical founders who know they need to modernise for AI, and want it architected properly the first time. Most engagements start with anAI readiness assessment.
AI that works inside your rules, not around them.
Put AI to work inside your products and operations: assistants that answer from your own data, agents that take approved actions in your systems, and automations that connect them, with quality checks, cost limits and human sign-off built in. Managed or private models, your choice.
A cloud platform where the secure way is also the fast way.
We design and build Azure foundations (networking, identity, infrastructure as code, deployment pipelines and monitoring) so your teams ship AI features in days, not quarters, and every pound of cloud spend is accounted for.
Software that survives an audit as well as a traffic spike.
Payments, collections, investment products and financial back-office systems built the way regulators expect: no double charges, every transaction reconciled, a full audit trail, data kept where it must stay, and access limited to who needs it. That includes the AI now running inside them.
From a separate copy per customer to one platform that serves them all.
Many SaaS products still run a separate copy for each customer, which makes every release and every AI rollout slower and more expensive. We move you to one shared platform that keeps every customer’s data strictly separate, even when AI is doing the work, with usage-based billing built in.
How we deploy
We pick the model hosting per workload, by data sensitivity, latency and cost, and route between options through one gateway so the decision can change without a rewrite.
The fastest path to frontier models, with enterprise data terms, regional deployments and private endpoints. Our default for most workloads.
For sensitive data, strict residency or high steady volume: open-weight models served with vLLM on AKS GPU pools or Container Apps serverless GPU. Prompts and data never leave your environment.
An AI gateway routes each request by data classification, latency and cost, with per-customer limits and usage tracking. Switch providers without touching application code.
Workflow automation
Agents are only useful when they can act on systems of record. We deploy self-hosted n8n inside your network to connect models and agents to CRM, ERP, ticketing, finance and messaging, with approval steps where a person must decide and every workflow versioned in Git.
We run our own lead capture on it: this site’s contact form posts to an n8n workflow.
In two to three weeks, our AI readiness assessment gives you a score across six areas, your risks ranked by impact, a target architecture and a costed 90-day roadmap.