AI Workspace
Give teams a secure workspace with approved AI models, company knowledge and business systems, deployed in the organisation’s own environment or one managed by Agile Labs.
Agile Labs deploys a workspace in the organisation’s own environment, where staff reach approved models through one controlled route and every request is recorded. It answers from company knowledge under each person’s existing permissions, so it returns what that person is already entitled to open and nothing more. We connect the business systems teams actually use, then either operate the workspace as a service or hand it over for the organisation to run itself.
PAVURE
Pavure gives an organisation one place to use AI with control over its data, its model access and what the AI can reach. Agile Labs assessed it against what an enterprise buyer examines, then built the controls: one route for model access, tightened permissions, limits on what agents may do, and logging that shows what was allowed or blocked.
Read the Pavure story →
Approved models, company knowledge and the systems the work runs on, in one environment — and the environment is the company’s own. Nothing is copied into anyone else’s index.
Every answer is drawn only from what the person asking could already open, enforced at the moment the question is asked rather than filtered afterwards.
Private or in-country models take what cannot leave; the strongest available model takes everything else. One interface covers both, and the boundary is fixed in writing.
Providers retire and replace models on their own schedule. The workspace is built so a model can be swapped without rebuilding anything around it.
Who asked, what was retrieved, which model answered and what it cost — kept in the form a regulator or a customer’s auditor asks to see.
Budgets run per team and per model, enforced when the request is made rather than discovered on the invoice.
Decide what is approved, where each model runs, and what data it can access.
Build the workspace, routing, retrieval and audit layer in the company’s own infrastructure.
Review and correct access so the AI only returns information each user is allowed to see.
Launch once identity, budgets, connections and logging are verified.
We maintain the controls, integrations and audit record as the environment changes.
A deployed workspace drifts. Providers change models underneath it, permissions regrow, staff connect new tools, and spend moves with usage. The monthly service exists because the record the workspace produces has to keep being produced.
Anything asking to connect is assessed against what it would reach and what its contract permits, and the decision is recorded either way.
Sprawl returns within months without a standing cadence. Access to the knowledge the workspace can read is reviewed on a schedule rather than after an incident.
Per-team budgets at the gateway, deviation alarms against a trailing baseline, and attribution that names the workload rather than the company.
Deprecations tracked ahead of the date, and a fixed question set run on a schedule so a change in behaviour is noticed before users report it.
A conversation with the engineers who would deploy it — and a straight answer when a chat subscription is the better buy.