The vendors we work with, and what that actually means

For a client the relationship shows up in two places. First, a Claude rollout that survives past the first fortnight: prompting your team can repeat, Projects built on your own documents rather than the model's general knowledge, a clear line on what may be pasted where, and a named internal owner so the capability outlives the training day. Second, the build work after the chat window runs out, when the answer needs your own systems: the API inside the tools people already use, MCP connectors under your existing permissions, and agents with evaluation behind them.

What it does not get you is a recommendation that always lands on Claude. We hold relationships across competing vendors on purpose, and we will say when another tool suits the work better.

Databricks is the data foundation an AI programme stalls without. In practice the relationship means direct access to Databricks product and solution architects when a design question runs past the documentation, early sight of what is changing on the platform, and joint support on escalations. It does not mean a discount we can promise in advance, and anyone implying otherwise is guessing.

The work itself is lakehouse design on Delta Lake, Unity Catalog governance set up before the platform fills with users, and migration off the legacy warehouse one domain at a time. Our delivery team is based in Sydney, so an engagement elsewhere is arranged case by case, and we will be straight about whether we are the right fit for it.

Most organisations on Microsoft 365 are paying per user per month for Copilot that mostly sits idle. It lives in the ribbon, so nobody notices it; the first vague question gets a vague answer, and people conclude the tool is weak. Our Microsoft work is adoption, not licensing. Hands-on sessions run in your own tenant on your own documents, taught around the tasks each team repeats rather than a tour of every feature.

Where a process is worth automating, we build it with you in Copilot Studio and hand over something your team can maintain. Where the work needs your own systems rather than the Microsoft 365 surface, we design it on Azure AI Foundry with the governance settled up front. If you need licences, your existing reseller handles that and we work alongside them.

Once a business moves past assistants and wants AI working against its own systems, the questions change: which model, running where, and who can see the data. Bedrock answers those well for organisations already standardised on AWS, and we build on it. The work is model selection per workload rather than once for everything, retrieval over your own data, which is where most of the accuracy comes from and where most prototypes are weakest, and workloads that stay inside your own account and region with logging your auditors can inspect.

If your data and systems sit on another cloud, we would generally point you at its equivalent rather than argue for a migration you do not need.

Some workloads cannot leave your infrastructure, and Mistral publishes models you can run in your own cloud account or on your own hardware, which makes it the practical answer where a hosted API is simply not permitted. The relationship gives us the same thing every one on this page does: engineers who have been through the vendor's enablement and a route to its technical teams when a deployment question needs escalating.

What a client gets is the deployment itself: sized against the workload rather than guessed at, connected to your own material, evaluated in a way you can show an auditor, and handed over with a runbook. The first step is always to test whether the constraint is real. If a hosted model would in fact be permitted, self-hosting is a large cost for nothing, and we say so before you spend anything.