Hosted
A workspace operated in an agreed cloud environment, with model providers, data handling and access rules selected for your team. Hosting and ongoing operation are scoped separately.
Company AI infrastructure
Company AI infrastructure, built around your work.
A shared AI workspace with hosted, on-premises or hybrid deployment options. Choose the models, decide where company data is stored and processed, and connect AI to the workflows your team needs to complete.
The problem
Your team should not have to rebuild the context every time.
Useful instructions live in one person’s chats. Company knowledge sits across documents and business systems. Someone has to connect the pieces, check the result and remember what happened last time.
Company AI infrastructure brings those pieces together. The harness is the part that coordinates the work: loading the right context, using connected tools, keeping track of progress and checking the result. Your team gets one working environment rather than a separate setup for every task.
Company context→Scoped work→Human review→Checked result
Deployment options
Hosted, on-premises or hybrid.
Choose where the workspace runs, where information is stored and where models process it. These are separate decisions; we scope them around your requirements.
On-premises
Run the workspace and document storage on your infrastructure. Where information must stay within your network, scope local models and supporting processing too.
Hybrid
Keep documents and retrieval on your infrastructure while using approved external model APIs for selected tasks. Define which passages or other information may leave, and which work must remain local.
Local storage is not the same as local processing. If a model API receives retrieved document passages, those passages leave your network even when the original files stay on-premises. Embeddings, logs and backups also need an agreed location and handling policy.
What we deliver
One foundation your teams can reuse.
We design and implement the shared foundation your teams need: company context, connected tools, dependable execution and clear controls. The deployment and capabilities follow your business requirements.
Company context and memory
Relevant documents, business definitions, decisions and work history, with sources people can inspect and correct.
Connected workflows
Bring approved company context, reusable skills and business tools into the same workflow, with clear ownership and review before consequential actions.
Work that can continue
A visible place for ongoing tasks, background execution and resuming interrupted work, scoped to the workflow.
Review and verification
Agreed approval points, records of actions and checks against the outcome your team actually needs.
Control over models
Choose open-weight models, approved external APIs or a mix by task. Agree provider choices, usage visibility and a practical path to change models.
Control over data and access
Define where documents and work history live, what reaches each model, who can use it and how retention and export should work.
Simulated workday
See how the pieces work together.
Two stories showing company AI infrastructure in everyday use. Names and company details are fictional; the workflows are simulated.
A supplier changes its terms. Every branch needs the right answer.
Maya leads operations at a regional services company. A supplier emails a new price list and delivery terms. Branch managers start asking which open quotes are affected.
She asks the workspace: “Compare these terms with our agreement and show me the quotes we need to review.” It reads the approved supplier documents, checks the connected quoting system and prepares a list of affected quotes, with links to the source and uncertain matches marked for review.
Maya checks the exceptions and approves follow-up tasks for the branch owners. The workflow records those actions and checks that the tasks reached the right place. The next person sees the current terms and the decision, rather than restarting the conversation.
A new manager needs an answer from documents that stay on-site.
Daniel is taking over a branch. Before approving a customer credit, he needs the current policy, the approval limit for his role and the escalation contact.
He asks: “Can I approve this credit, and what do I need to check?” The workspace retrieves the relevant policy from the company’s on-premises document store using his permitted access. The answer links to the policy and identifies the missing information needed for a decision.
In this hybrid setup, only approved passages are sent to the selected external model. Restricted material follows a local processing path. Daniel supplies the missing detail; the system prepares the request for the designated approver and records its status. A useful answer becomes a traceable workflow.
Each story depends on the agreed source permissions, integrations, model routing and action controls. We establish and test those requirements for your actual environment.
Start with your work
From your requirements to a working system.
Tell us what your teams need to accomplish, the systems they use and the boundaries your information must stay within. We assess your existing setup, define the requirements and design the infrastructure around the work.
Measured Intuition handles design, implementation and integration, with testing against agreed acceptance criteria. We define delivery responsibilities, handover and ongoing operation as part of the engagement.
The engagement can cover hosted, on-premises or hybrid infrastructure, model access, company knowledge, connected workflows and the controls needed to operate them. The architecture follows your requirements.
Keep your existing systems where they work. Add shared capabilities where they improve the outcome, with practical portability across models and a clear plan for ownership and support.
No preparation deck needed. Tell us your role, the workflow, the systems involved and any data restrictions. Please leave credentials and confidential documents out of the initial enquiry.
Related reading
Choose the right hosting and data boundaries, prepare the vendor evidence and agree the production launch checks with your team.
Understand the foundation.
Planning access to company data and tools? Review the security decisions before rollout, from data flows and permissions to approval and recovery.