What we do

Private AI Workspace

A governed team AI environment for approved models and knowledge sources, with identity-aware access, administration, and deployment choices aligned to your policies.

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Designed around your operating context

Give teams a consistent place to use AI without scattering data across unreviewed tools. A private workspace can centralize approved model access, knowledge search, user controls, and operational visibility while preserving the organization’s chosen boundaries.

A workspace is useful when multiple teams need a safe starting point for research, drafting, summarization, and internal knowledge discovery. It is not a substitute for deciding which tasks are appropriate or which records users may access. We define user groups, data classes, approved models, administrator responsibilities, and rollout scope before connecting sensitive systems.

Where it helps

Use cases with a clear owner and outcome

  • Internal research and drafting

    Provide a shared interface for summarizing approved material, preparing first drafts, and asking questions about source documents. Keep citations and source navigation visible so employees can verify important details.

  • Team-specific knowledge assistants

    Offer separate knowledge collections for functions such as operations, engineering, or policy teams. Apply access filters before retrieval and clearly show when a user lacks permission or source coverage.

  • Model choice by task

    Make approved model options available through a consistent interface or gateway. Compare capability, data handling, latency, and cost for the task rather than assuming one model is suitable for every team.

  • Measured AI adoption

    Start with a defined pilot group, approved use cases, and feedback channels. Use aggregate usage and quality signals to guide expansion without treating raw activity counts as proof of business value.

System design

Architecture and decisions that matter

The workspace is an identity, policy, and operations layer around model and knowledge services. Its design should make the allowed paths easy to use and make administration, review, and removal of access straightforward.

  • Identity and workspace roles

    Integrate the agreed sign-in approach and map groups to roles, collections, and administrative capabilities. Define joiner, mover, and leaver processes, session behavior, and how access is reviewed.

  • Model access layer

    Route requests to approved providers or deployed models through a controlled service. Document model availability, provider data handling, retention settings, rate limits, and fallback behavior for each selected option.

  • Knowledge and connectors

    Connect only approved repositories, preserve source permissions and citations, and define refresh, deletion, and indexing ownership. Test retrieval with role-specific accounts before general release.

  • Administration and visibility

    Provide configuration ownership, usage policies, support routes, and appropriate logs. Keep telemetry proportionate: define what is collected, who can inspect it, and how long it is retained.

Acceptance and review

Define what good looks like

Success measures are agreed with the people who own the outcome. These are useful signals to consider, not a promise of a particular result.

  • Task usefulness

    Feedback on whether approved tasks are completed to an agreed quality bar.

  • Access enforcement

    Test results for allowed and denied user-to-source combinations.

  • Grounding and source use

    Whether users can verify answers against the intended content.

  • Operational adoption

    Support themes and use-case feedback, not activity counts alone.

What you receive

A delivery package your team can operate

  • Workspace requirements and access matrix for user groups, administrators, data collections, and approved use cases.
  • Architecture for identity, model access, knowledge connections, deployment, data handling, and operating ownership.
  • Configured workspace pilot with selected models, approved sources, user guidance, and a tested access boundary.
  • Evaluation and rollout plan including representative tasks, source checks, feedback collection, and expansion criteria.
  • Administration and support handover covering onboarding, offboarding, configuration changes, incidents, and periodic access review.

Enterprise delivery

Built for review, operation, and change

“Enterprise-ready” is not a certification claim or a one-size-fits-all stack. Scope, controls, service levels, and compliance responsibilities are agreed against your environment and policies.

  • Security and data boundaries

    Agree access roles, data classification, approved integrations, retention, and secret handling with the relevant owners. Implement controls in the application and platform where possible; do not rely on policy text alone.

  • Testable acceptance

    Translate requirements into reviewable scenarios, including ordinary use, invalid input, denied access, dependency failure, and recovery. Keep decisions and outstanding assumptions documented.

  • Production operations

    Identify service owners, useful logs and metrics, alert routes, escalation expectations, and runbooks before launch. Monitoring and support scope are agreed for each engagement.

  • Controlled change

    Use separated environments, reviewed changes, deployment checks, and a practical rollback or disable path appropriate to the system. Document who can approve and operate changes.

How we work

A phased path from discovery to handover

  1. 01

    Set the governance baseline

    Agree on permitted use, sensitive data categories, user populations, identity source, administrator roles, and provider constraints. Identify the decisions that remain with security, legal, and data owners.

  2. 02

    Design the workspace

    Choose hosting and model options, define role-to-source mappings, and specify logging and retention. Prefer a small, supportable set of approved paths over a broad menu that users cannot evaluate.

  3. 03

    Pilot with representative teams

    Test normal tasks and boundary cases using separate roles. Gather feedback on usability, citations, response quality, and support needs; correct data or access issues before widening the pilot.

  4. 04

    Operate and expand deliberately

    Document support, model changes, source refresh, and access review. Expand to additional groups only when ownership, controls, and useful acceptance signals are in place.

Good to know

Questions to consider

How is this different from a RAG assistant?

RAG describes a pattern for grounding a model with retrieved information. A private workspace is a broader team environment that can provide identity, model access, multiple tools or knowledge collections, administration, and rollout controls. It may include RAG features.

Does private mean data never leaves our network?

Not automatically. A workspace can use private infrastructure, a provider API, or a hybrid design. The exact data path, provider terms, retention, and network boundaries must be reviewed and documented for the chosen deployment.

Can different teams have different access?

Yes, if the identity and source systems expose suitable roles or permissions. Access should be enforced in the application and retrieval path, then tested with representative user accounts.

Do employees need AI training?

A rollout should include clear acceptable-use guidance, source verification habits, examples of restricted tasks, and a route to report problems. The content and format depend on the organization and should be agreed with its owners.