AI control room
enterprise AI automation platform – orchestrate intelligent agents under your control
Your AI. Your infrastructure. Your rules.
Orchestrate intelligent agents for every department – with full control and no vendor lock-in.
AI Control Room is an enterprise AI automation platform designed for control, flexibility, and scale.
Deployed inside your own cloud (Azure, AWS, GCP), it lets you build and orchestrate intelligent AI agents, agent teams and worksflows across your business – with full transparency, and no compromise on security.
End users interact via a familiar, ChatGPT-like interface – secure, responsive, and built for enterprise use.
So… what’s in it for your business?
So… what’s in it for your business?
-
Run AI Control Room fully in your own cloud – Your cloud, your rules, no shadow AI. You stay in control of your data, models, logic, and integrations at all times.
-
Eliminate expensive per-user licenses like CHAT-GPT etc. Deliver a secure, scalable AI assistant experience to your entire organization – with no seat-based pricing.
-
Free your teams from repetitive, manual work like ticket management, document generation, or Q&A – and redirect time to higher-value tasks. Automation that pays for itself.
-
Plug AI directly into your business systems. Our agents don’t just respond – they retrieve, reason, and act within the tools you already use.
-
Start small – expand fast. Deploy AI agents across sales, support, HR, finance, and IT from a single, governable platform. No code rewrites, no new vendors.
Book a demo and get 30-day free trial
Features
Possible Use Cases
Enables real-world automation across departments and industries.
Customer Support Automation: Automatically categoryze incoming support tickets and generate accurate, context-aware responses
Marketing & Sales: Generate campaign content, segment leads, and personalize outreach at scale.
Finance & Admin: Extract data from invoices and receipts, automate report generation, and validate entries.
HR Operations: Answer employee FAQs, assisst with onboarding, and automate policy-related inquiries.
IT and DevOps: Monitor systems, summarize logs, and trigger remediation tasks.
Legal & Compliance: Scan documents for key clauses, summarize legal content, and ensure regulatory alignment.
By combining enterprise-grade flexibility with secure deployment, AI Control Room becomes a foundational tool for driving productivity and innovation at scale.
pricing
Subscription Models
Good
399€/month
- ✔ Free deployment
- ✔ Personal Chat with LLM models
- ✔ Limitless Agents
- ✔ SSO Authentication with EntraID
- ✔ Basic connectors to M365 & Confluence
- ✔ Fixed cloud environment as Service
Better
1499€/month
- ✔ All the features from Good
- ✔ Advanced Connector
- ✔ Slack / Teams integration
- ✔ MCP Support
Best
1999€/month
- ✔ All the features from Better
- ✔ Agent Teams
- ✔ Custom Connectors
- ✔ Workflows
- ✔ Data (fabric etc connections)
- ✔ Advanced Security Features
- ✔ Multilayer RAG
- ✔ API Access
*Customers retain full control and responsibility for all cloud environment and AI usage costs incurred within their own environment.
Soon available also on AWS, Azure and GCP Marketplaces
Cost Comparison Calculator
AI Control Room vs. Copilot Studio
-
This calculator estimates monthly costs for AI Control Room and Microsoft Copilot Studio based on:
Users: Set the number of users needing AI agents.
Usage: Assumes 800 messages per user per month, with ~500 tokens per message (split 50/50 between input and output).
AI Control Room Pricing: Subscription plan auto-selected (Good, Better, Best) plus AI platform costs based on OpenAI o1 pricing.
Copilot Studio Pricing: Choose between Message Pack (€200/25,000 messages) or Pay-as-you-go (€0.01/message), plus €30/user license fee.
Notes: Currency fluctuations and minor usage variations are not reflected.
The goal is to provide a transparent comparison of typical monthly costs.
Real world use case
It started from our own needs!
AI-Powered Ticket Categorization:
At Cloud2, we built the AI Control Room to solve common pain points in our cloud operations. One of them was manual ticket categorization. Instead of relying on engineers to sort, tag, and route incoming tickets, our solution uses a pre-trained AI agent to do it in real time.
It reads the content, understands the intent, and categorizes each event with high accuracy - without needing rules, templates, or human review. By embedding this into our existing ticketing workflows, we helped our team focus on resolution, not repetition.
Faster workflows, lower error rates, and no more wasted time on categorization.
Speeds up time-to-resolution by ensuring every ticket starts its lifecycle pre-classified.
Improves accuracy in category assignment, reducing rework and misrouting.
Business Outcomes
Quantifiable Benefits
Metric | Before AI Control Room | After AI Control Room | Improvement |
---|---|---|---|
Time per Ticket | 1 minute | 10 seconds | 83% reduction |
Monthly Processing Time | 117 hours | ~19.5 hours | 97.5 hours saved |
Cost per Ticket | €2.00 | €0.01 | €1.99 savings per ticket |
Monthly Operational Cost | €14,000 | €70 | €13,930 savings per month |
Comparison:
AI Control Room vs. Alternatives
Feature | AI Control Room | Azure AI | Google Agentspace | AWS Bedrock | Rasa (Enterprise) | Kore.ai |
---|---|---|---|---|---|---|
Delivery model: SaaS | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ |
Self-managed deployment | ✅ | ❌ | ❌ | ⚠️ | ✅ | ✅ |
Cloud-agnostic (any cloud) | ✅ | ❌ | ❌ | ❌ | ⚠️ | ⚠️ |
On-prem support | ✅ | ⚠️ | ❌ | ❌ | ✅ | ⚠️ |
Multi-agent orchestration | ✅ | ✅ | ⚠️ | ✅ | ⚠️ | ✅ |
RBAC (access control) | ✅ | ✅ | ✅ | ✅ | ⚠️ | ✅ |
Audit logging | ✅ | ✅ | ✅ | ✅ | ⚠️ | ✅ |
End-user chat interface | ✅ | ❌ | ⚠️ | ❌ | ❌ | ✅ |
End-user channels (Teams, Slack, etc.) | ✅ | ⚠️ | ⚠️ | ⚠️ | ⚠️ | ✅ |
Visual workflow editor | ⚠️ | ⚠️ | ✅ | ⚠️ | ✅ | ✅ |
Prebuilt integrations | ✅ | ✅ | ✅ | ✅ | ⚠️ | ✅ |
Multi-cloud LLM support | ✅ | ❌ | ❌ | ❌ | ⚠️ | ⚠️ |
Self-hosting w/o vendor lock-in | ✅ | ❌ | ❌ | ❌ | ✅ | ⚠️ |
Enterprise-ready | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
Flat-rate subscription pricing | ✅ | ❌ | ❌ | ❌ | ⚠️ | ⚠️ |
Legend:
- ✅ = Fully supported
- ⚠️ = Partially supported / conditional
- ❌ = Not supported
Technical Highlights
Multi-Cloud LLM Support: Use models from Azure OpenAI, Amazon Bedrock, Google Vertex AI, and open-source LLMs.
Secure by Default: Runs inside your infrastructure. No data leaves your cloud
Structured I/O: Accepts chat, forms, and file inputs; outputs in markdown, JSON, etc.
Multi-Cloud platform: Run in any cloud – Azure, AWS, Google Cloud or on-prem. No lock-in, no dependencies.
Custom Agents and Workflows: Orchestrate tasks using prompt chaining, API triggers, and logic-based flows.
Version Control: Every agent has change history and rollback support.
Question?
Contact:
Jarno Lepistö +358 405259396
jarno.lepisto@cloud2.fi
Our partners
BOOK A demo
Leave your email address, and we'll get in touch to show you how to achieve unprecedented visibility into your cloud!