AI Workspace

One platform for all company AI. Secure, monitored, managed.

Your employees want AI. Your compliance team wants control. AIWorkspace gives both. A centrally managed, self-hosted AI platform where every query is monitored, every piece of sensitive data is filtered, every cost is tracked, and every action is logged. Your team gets the best AI models. You get full visibility.

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100%

self-hosted. Your data never leaves your infrastructure. No third-party access.

DLP

built-in PII detection and redaction. Sensitive data is stripped before it reaches any model.

RBAC

per-team access controls. Define who can use which models, with what budget, for what purpose.

€

real-time cost dashboards and alerts. Know exactly what you spend, per user, per team, per model.

The problem

Your company already uses AI. The question is: on whose terms?

Without a central platform, AI usage is fragmented, invisible, and risky.

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Shadow AI everywhere

Every department has people using ChatGPT, Copilot, Claude, Perplexity, and more. Each on their own subscription. IT has no visibility into what data goes where.

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Costs are invisible

The marketing team pays for ChatGPT Plus. Three developers have Copilot. Sales uses a different tool. Nobody knows the total spend, and nobody is optimizing it.

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Compliance is impossible

GDPR requires you to know where personal data goes. The AI Act requires documentation and risk classification. You can't comply with what you can't see.

What AIWorkspace gives you

Everything your team needs from AI, nothing your compliance team worries about

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Multi-model access

One interface, all major models. OpenAI, Anthropic, Azure OpenAI, Mistral, open-source models. Route queries to the best model for the task automatically, or let users choose.

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PII filtering (DLP)

Microsoft Presidio scans every query before it leaves your environment. Names, addresses, IBANs, phone numbers, ID numbers are detected and redacted automatically. Configurable per team.

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Per-team access controls

Define which teams get access to which models. Set monthly budgets per team or per user. Restrict specific capabilities (file upload, code execution) by role. Full RBAC integrated with your SSO.

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Real-time cost dashboards

See exactly what you spend on AI, broken down by user, team, model, and time period. Set budget alerts. Compare cost per query across models. Monthly reports generated automatically.

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Full audit logging

Every query and response is logged. Searchable, exportable, ready for compliance reviews. Retention policies configurable to match your requirements. AI Act and GDPR compliant.

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Self-hosted, your infrastructure

Deployed on your cloud (Azure, AWS, GCP) or on-premises via Infrastructure as Code. No data passes through third-party servers. You own the platform, we manage it.

Deployment process

From zero to managed AI platform in 6 steps

We handle the full deployment. Your IT team stays in control, but doesn't have to build it from scratch.

Step 1

Discovery & Design

We map your current AI usage, team needs, and infrastructure. Then design the workspace architecture.

→ AIShield audit results (or quick-scan if not done yet)
→ Team interviews: who needs what AI capabilities
→ Infrastructure assessment: cloud provider, network, SSO
→ Architecture design document for your approval

Step 2

Platform Deployment

Self-hosted AI platform deployed on your infrastructure via Infrastructure as Code.

→ LiteLLM proxy for multi-model routing
→ Chat interface for end users
→ API gateway for developer integrations
→ SSO integration (Azure AD, Okta, Google Workspace)

Step 3

Security & DLP Layer

PII detection and data protection configured and tested.

→ Microsoft Presidio deployment and configuration
→ Custom entity recognition for your domain (e.g. internal project codes)
→ Redaction rules per team and sensitivity level
→ DLP testing with sample data before go-live

Step 4

Cost Controls & FinOps

Budgets, alerts, and cost optimization rules configured.

→ Per-user and per-team budget limits
→ Model routing rules for cost optimization
→ Grafana dashboards for real-time spend tracking
→ Monthly automated cost reports

Step 5

Compliance & Logging

Audit trail and compliance reporting set up.

→ Query and response logging with configurable retention
→ AI Act compliance documentation templates
→ GDPR data processing records
→ Export capabilities for compliance reviews

Step 6

Rollout & Training

Phased rollout to teams with training and support.

→ Pilot group (IT/engineering) for initial testing
→ Phased rollout plan across departments
→ User training sessions and documentation
→ Feedback loop and iteration before full rollout

Available modules

Start with the core platform. Add modules as your needs grow.

AIWorkspace is modular. The core platform (chat, proxy, DLP, dashboards) is always included. These modules can be added at any time.

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Meeting Assistant

Records, transcribes, and summarizes meetings. Extracts action items and sends follow-ups automatically. Integrates with Google Meet, Teams, and Zoom.

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Internal Helpdesk AI

🔒 Every agent runs through AIWorkspace. All actions are logged, PII is filtered, costs are tracked per user, and IT can revoke access or restrict capabilities at any time. No shadow AI. Full visibility, full control.

Built on proven open-source

No vendor lock-in. No proprietary black boxes.

AIWorkspace is built on battle-tested open-source components. You can inspect, modify, and migrate at any time. We add the management layer, monitoring, and enterprise-grade deployment.

LiteLLM

Multi-provider AI proxy. Routes requests to OpenAI, Anthropic, Azure, Mistral, and open-source models through a single API. Handles cost tracking, rate limiting, and failover.

Microsoft Presidio

Enterprise-grade PII detection and redaction engine. Identifies names, addresses, financial data, and custom entities. Configurable per team and sensitivity level.

Grafana + OpenLLMetry

Observability stack for AI workloads. Real-time dashboards for cost, latency, token usage, and model performance. Alerting and anomaly detection included.

Langfuse

LLM tracing and analytics platform. Tracks prompt chains, agent actions, and response quality. Enables debugging and optimization of AI workflows.

Docker / Kubernetes

Containerized deployment for consistency and scalability. Works on any cloud provider or on-premises. Deployed and managed via Infrastructure as Code (Terraform, Helm).

Your SSO provider

Native integration with Azure AD, Okta, Google Workspace, and other OIDC/SAML providers. Users log in with their existing company credentials. No separate accounts.

Why not just buy ChatGPT Enterprise?

AIWorkspace vs. vendor platforms

Feature	ChatGPT Enterprise	Microsoft Copilot
Self-hosted (data stays with you)
Multi-model (OpenAI + Anthropic + open-source)
Built-in PII filtering (DLP)		Partial
Per-user cost tracking
Custom AI agents	Limited	Limited
No vendor lock-in
AI Act compliance logging	Partial	Partial
Integrates with existing SSO

Works with our other AIOps services

AIWorkspace is the core. Build more on top.

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Start with AIShield

Not sure what AI your organization uses today? Run an AIShield audit first. The findings feed directly into your AIWorkspace configuration: which tools to replace, which teams get access first, what DLP rules to set.

Learn about AIShield →

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Extend with AIForge

Need more than chat and agents? AIForge builds custom AI solutions on top of the same infrastructure: RAG systems, automated test analysis, AI-powered CI/CD. All running through AIWorkspace's security layer.

Learn about AIForge →

Frequently Asked Questions

Common questions about AIWorkspace

How long does deployment take?

Typically 3-4 weeks for the core platform (chat, proxy, DLP, dashboards). Add 1-2 weeks per additional module (Meeting Assistant, Helpdesk AI, Agents). We start with a pilot group and roll out in phases.

Which cloud providers do you support?

Azure, AWS, and GCP. We can also deploy on-premises if you have Kubernetes infrastructure. The platform is deployed via Terraform and Helm, so it's portable across providers.

Can employees still use ChatGPT directly?

That's your decision. Most clients block direct access to public AI tools and route everything through AIWorkspace. This way employees get the same (or better) capabilities, but through a controlled channel. We help configure the blocking as part of deployment.

What AI models are available?

All major providers: OpenAI (GPT-4o, o1, o3), Anthropic (Claude Sonnet, Opus), Azure OpenAI, Mistral, and any open-source model you want to self-host (Llama, Qwen, DeepSeek). LiteLLM handles routing and failover.

How does pricing work?

AIWorkspace is available as part of our Standard and Premium DevOps as a Service packages, or as a standalone engagement. You pay for our management, not for the underlying tools (which are open-source). AI model costs (API tokens) are billed through your own provider accounts.

What about existing Copilot or ChatGPT Enterprise licenses?

AIWorkspace doesn't require you to cancel existing licenses immediately. Many clients run both in parallel during transition. Over time, most consolidate to AIWorkspace because it offers multi-model access at lower cost with better control.

Can we manage it ourselves after deployment?

Yes. We provide full documentation and training. The platform uses standard open-source tools your team likely already knows (Grafana, Kubernetes, Terraform). Ongoing management is optional but recommended for updates, optimization, and support.

Is there a minimum company size?

AIWorkspace makes most sense for organizations with 50+ employees. Below that, the overhead of a self-hosted platform may not justify the investment. We'll be honest about this during the consultation.

Give your team AI. Keep your data safe.

Book a 30-minute call. We'll discuss your current AI situation, show you a demo of the platform, and outline what deployment would look like for your organization.

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