Enterprise AI Transformation

The feedback from clients and other enterprise CEOs is that everyone is taking a step back and designing their AI Transformation Architectures for 2027 and beyond. Most firms have seen a patchwork of adoption through 2025/26 with notable wins. They are thinking about how to build from these gains in 2027. Security, compliance, data and IP protection are front of mind. Trust and safety are primary program design considerations.

With this in mind we took some time this week to share our AI transformation architecture with the Options Technology management team at a global offsite this week.

The PrivateMind AI architecture is the cornerstone but only the start. We believed that the first step was to build a sovereign platform capable of delivering all the best Open Source models. The Kimi K3 release was a pivotal moment. As we say internally, once a user goes Kimi they never go back.

While Kimi is the favourite, PrivateMind serves up over 20 leading models, big and small, and a suite of internally trained models.

Serving models is only the start of the journey. We believed that to be successful we needed an orchestration and delivery platform that matched or exceeded the platforms offered by the Frontier firms. We settled on an architecture leveraging chat, API and integrating OpenCode, amongst others.

The next challenge is how to deliver the orchestration platform in a secure and frictionless manner. We've two delivery models: the PrivateMind Developer App and PrivateMind Quant Workspaces.

PrivateMind Quant Workspaces are leveraged by power users who need direct access to a GPU. Incredibly slick, the user gets to work directly on a private GPU or a full B300, including unlimited local storage. Workspaces enable the full spectrum of features including training custom models.

The PrivateMind Developer App is focussed on the regular AI application developer (or vibe coders). Anyone who wants to spin up an agent or create an AI app.

Both delivery models leverage containers, the data never leaves secure environments on the server side. Both delivery models utilise Single Sign On (SSO). They inherit the users enterprise security and access profiles so the user, models and agents can only access data sets the user is already permissioned for, with access controls and access logging. Compliance, trust and control built in.

Once the models and slick delivery layer are in place the next requirement is data. We believe slick and secure access to enterprise data sets is key. Our approach is best described as "MCP everything."

The users' Microsoft stack, email, teams, Sharepoint, plus all the other internal systems. The user has an MCP connector to every system they have access to in their regular day to day work.

With this platform in place the next big challenges are broad adoption and creating an innovation culture. We believe proactive change management programs are the cornerstone of cultural and behavioural change. We've been running waves of AI adoption programs to get every team member across the firm engaging with the technology day to day. Our weekly management meeting has evolved into a core forum for sharing ideas. Some team or other has presented a key AI innovation almost every week in 2026. Monkey see, monkey do. The great ideas spread like wildfire.

The key AI application design patterns evolve through the innovation forums. As one team masters an approach to a given problem that pattern spreads to other teams. Foundational design patterns evolve, applications written by AI with AI embedded in the workflow, Robo Worker frameworks, automated delivery pipelines leveraging Git, data cleansing models, strategies for iterative model training. The simplest use case of all should not be overlooked, rapid prototyping and deployment of enterprise software tools.

Engineers with the knack for AI application development eventually come to the fore. The next step, the creation of the internal AI engineer function, specialist engineers focused on supporting other teams leveraging AI.

The final stage of the process, enterprise application delivery, how the AI applications created are delivered to all the appropriate users across the firm. We believe this is a critical and often overlooked step. Application delivery needs to be slick, frictionless and secure.

We leverage the PrivateMind Pipeline. The same principle as with AI platform delivery applies. The AI applications are all served from containers in a locked down and secure server environment. Each container only has access to the underlying systems it requires. Application user permissions cascade so a given user can only access data sets they are permissioned for already in their day to day work.

Integrated, slick and secure.