Intrascope Enterprise
Standardise How Your Enterprise Manages AI
Give employees a familiar AI workspace and keep the enterprise in control of identity, approved models, shared context, budgets, and audit, including private and self-hosted models.
- Vendor-neutral control across cloud and local models
- Deployment choice: managed SaaS, private cloud, on-premise, or hybrid
- Focused 6-8 week enterprise pilot with measurable proof
The enterprise challenge
AI adoption is growing faster than enterprise control
Adoption often develops outside formal IT governance. Employees use different models and accounts, company knowledge stays in private chats, and management has limited visibility over data, usage and cost.
Fragmented access
Teams use different AI tools, subscriptions and providers without a common enterprise standard.
Invisible usage
Leadership cannot easily see which teams use which models, for which work, at what cost.
Context leakage
Knowledge, prompts and decisions remain in private conversations instead of approved company context.
Governance arrives late
Policies, budgets and auditability usually appear only after adoption has already become risky.
The control layer
One employee experience. One enterprise control plane.
Intrascope sits between people, company workflows and approved model providers. Employees keep model choice for the task. The enterprise keeps central control.
Identity and access
User groups, roles, invitations and bulk onboarding, with enterprise SSO policies available for larger rollouts.
Model policies
Define approved models, restrict high-risk usage and align model choice to the work, including private and self-hosted endpoints.
Company context
Reusable manifests and project context keep approved knowledge inside the workspace instead of private chat histories.
Budgets and analytics
Track usage by user, project, team and provider. Set consumption limits before spend becomes a surprise.
Audit and visibility
Activity inside the governed environment is recorded with reporting leadership can actually use.
Integrations and automation
Connect CRM, documents, ticketing and knowledge bases through APIs, webhooks and n8n without leaving the control layer.
Close the AI loop
From people and systems to every approved model, including local ones
Most enterprises stop at chat access. Intrascope closes the full loop: employees and workflows enter through one governed layer, then route to cloud providers or models that stay inside your infrastructure.
01
Work enters here
- Employees and departments
- Projects and client work
- Agents and automated workflows
02
Intrascope control layer
- Identity and access
- Model policies and routing
- Company context and manifests
- Budgets, analytics and audit
- APIs and n8n orchestration
03
Models you approve
- OpenAI, Anthropic, Google, Mistral, xAI, DeepSeek
- Private and self-hosted models
- Hybrid mixes of cloud and local inference
That is how a company closes the AI circle: one governed path from people and systems to every model the enterprise trusts, without forcing a single-vendor lock-in, and without leaving local models outside the same policies, context and reporting.
Deployment options
Choose the environment that matches your control requirements
Enterprise AI needs an explicit processing boundary. Pick the option that fits your security, residency and sovereignty needs, including local models where they matter most.
Managed SaaS
Fast start on the shared Intrascope cloud service, with the full control layer available immediately.
Best suited for: Early enterprise teams and pilots
Private cloud
An isolated customer instance in an agreed region, with stronger separation from multi-tenant traffic.
Best suited for: Enterprises needing stronger isolation
On-premise
Customer-hosted platform inside your own infrastructure for regulated sectors and strict sovereignty.
Best suited for: Regulated and high-sovereignty environments
Hybrid
Local or private workloads plus approved external models, under the same identity, policy and audit layer.
Best suited for: Balance control, capability and cost
Scope a 6-8 week enterprise pilot
Tell us the business unit, deployment preference and whether local models are in scope. We will map the control layer to your first workflow.
Enterprise pilot
Prove the value in a controlled 6-8 week engagement
A focused pilot validates security fit, user adoption, model policies, reusable context and management visibility before a wider rollout.
- 01
Discover
Map current AI tools, users, risks, data flows and a cost baseline for the business unit you want to prove first.
- 02
Configure
Set SSO where required, model access (cloud and local), project context, budgets and admin controls.
- 03
Activate
Onboard 10-50 users with guided enablement and weekly reviews against the workflows that already matter.
- 04
Measure
Review usage, adoption, cost visibility, policy fit and an expansion plan with an executive pilot report.
Inside the product
What enterprise control looks like day to day
The same workspace employees use for AI work is the system leadership uses for visibility and policy.
Proof
Our first enterprise pilot was a 50-person consulting firm
A firm working across tax, finance, advisory and editorial moved from mostly free consumer accounts to a governed workspace with premium models. First-month model usage was $13, and we shipped a custom RAG module over more than 1GB of internal regulations.
50
employees onboarded
$13
model usage in month one
6-8 wks
pilot shape we recommend
Bring security, IT and a business owner to the same call
The fastest pilots start when deployment, model policy and a real workflow are decided together.
Business outcome
What changes once AI has a control layer
One standard for company AI
Employees stop inventing their own tool stack. Access, context and policy live in one place.
Cloud and local models under the same rules
Self-hosted inference is not a side channel. It runs through the same identity, budgets and audit trail.
Knowledge stays with the company
Approved context sits in projects and manifests, not in personal consumer accounts that leave with people.
Spend becomes attributable
Finance and IT can answer who used which model, on which work, at what cost.
Security can say yes with conditions
Deployment choice and model policy make the processing boundary explicit instead of implied.
A path from pilot to rollout
A measured 6-8 week engagement produces adoption evidence, governance proof and a pricing proposal.
Before and after
Enterprise AI without a control layer, and with Intrascope
The question is not whether employees will use AI. It is whether the enterprise can see, steer and secure that usage.
| Topic | Without Intrascope | With Intrascope Enterprise |
|---|---|---|
| Model access | Whatever each person subscribed to or installed locally. | Approved cloud and local models behind one workspace policy. |
| Company knowledge | Trapped in private chats and personal tools. | Held in projects and reusable manifests the company owns. |
| Local / self-hosted models | A separate stack with no shared governance. | Part of the same control loop as cloud providers. |
| Usage and cost | Scattered invoices and anecdotal adoption stories. | Usage and spend by user, project, team and provider. |
| Deployment | One SaaS chat product, take it or leave it. | Managed SaaS, private cloud, on-premise or hybrid. |
| Rollout decision | Buy seats and hope governance catches up. | Pilot with success criteria, then expand with evidence. |
Model access
Without Intrascope
Whatever each person subscribed to or installed locally.
With Intrascope Enterprise
Approved cloud and local models behind one workspace policy.
Company knowledge
Without Intrascope
Trapped in private chats and personal tools.
With Intrascope Enterprise
Held in projects and reusable manifests the company owns.
Local / self-hosted models
Without Intrascope
A separate stack with no shared governance.
With Intrascope Enterprise
Part of the same control loop as cloud providers.
Usage and cost
Without Intrascope
Scattered invoices and anecdotal adoption stories.
With Intrascope Enterprise
Usage and spend by user, project, team and provider.
Deployment
Without Intrascope
One SaaS chat product, take it or leave it.
With Intrascope Enterprise
Managed SaaS, private cloud, on-premise or hybrid.
Rollout decision
Without Intrascope
Buy seats and hope governance catches up.
With Intrascope Enterprise
Pilot with success criteria, then expand with evidence.
Enterprise use cases
Start with governed workflows that already matter
Use cases should be concrete enough to buy and simple enough to pilot in six to eight weeks.
Legal and compliance
Policy Q&A, contract summarisation, approved context, human review and restricted model access.
Sales and proposals
Customer research, proposal drafting, reusable commercial language and consistent company context.
HR and knowledge
Employee onboarding, internal policy support and controlled access to approved company knowledge.
Technology and operations
Requirements analysis, incident summaries, documentation and safe workflow automation via n8n.
Regulated workloads on local models
Keep sensitive inference on private or self-hosted models while still giving teams the same workspace experience.
FAQ
Questions teams ask before they switch
It is the enterprise packaging of Intrascope as a control layer: one governed AI workspace for employees, with central identity, model policies, shared context, budgets, analytics and audit across approved cloud providers and private or self-hosted models.
Yes. Enterprise deployments can include private and self-hosted models alongside approved external providers, so sensitive work can stay inside your infrastructure while everyday tasks still use the cloud models you allow.
No. You can start on managed SaaS, move to a private cloud instance, host on-premise, or run a hybrid mix. The point is matching the processing boundary to your control requirements.
A typical pilot runs 6-8 weeks for one business unit or priority workflow, with 10-50 users. Deliverables include a configured workspace, usage and governance visibility, and an executive report with rollout and pricing recommendations.
Common targets include strong user activation, weekly active usage, one reusable approved context library, a management report covering cost, usage and governance evidence, and a clear annual rollout recommendation.
Those products centralise access to one provider. Intrascope is vendor-neutral: projects, manifests, model policies, usage analytics and deployment choice across multiple providers and local models.
No. Intrascope does not use your chats, prompts, manifests or workspace data to train AI models. For provider-side behaviour, use API or enterprise terms and keep sensitive workloads on private models where required.
Share your details on this page or book a call. We will scope the first workflow, deployment preference and pilot success criteria before anyone rolls out seats company-wide.
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Ready to bring company AI usage under control?
Standardise how your enterprise manages AI across cloud providers and local models, with a control layer security can approve and employees will actually use.