Beyond ChatGPT: Building Enterprise AI Capability

Consumer AI tools are easy. Enterprise AI is harder. You need a stack that delivers capability with control, security, and governance.

Here’s what that stack looks like.

The Consumer vs. Enterprise Gap

Consumer AI:

• Sign up with email
• Paste data into browser
• Get response
• No audit, no control, no governance

Enterprise AI:

• Integrated with identity
• Data stays in perimeter
• Policies enforced
• Full audit trail
• Cost controlled

The gap is significant.

The Enterprise AI Stack

A complete enterprise AI stack includes:

[User Interface Layer]
├── AI IDE (coding)
├── AI Lab (data science)
├── Chat Studio (business users)
└── Deep Agent (automation)

[Orchestration Layer]
├── Langflow (visual workflows)
├── Agent coordination
└── Multi-model routing

[Governance Layer] (Zentinelle)
├── Policy enforcement
├── Audit logging
├── Content scanning
├── Secrets management
└── Budget controls

[Infrastructure Layer]
├── Model providers
├── Vector databases
├── Data connectors
└── Deployment platform

User Interface Layer

Different users need different interfaces:

Developers: AI IDE with copilot, chat, and agent modes Data Scientists: AI Lab with Jupyter and AI assistance Business Users: Chat Studio for data queries Automation: Deep Agent for autonomous tasks

One tool doesn’t fit all.

Orchestration Layer

Beyond single queries:

Visual workflows: Build without code in Langflow Agent coordination: Multi-agent task execution Model routing: Right model for each task

Orchestration turns AI tools into AI systems.

Governance Layer

Enterprise requirements:

Policy enforcement:

• What can users do?
• What data can they access?
• What content is allowed?

Audit logging:

• Who did what, when?
• Complete trail for compliance

Content scanning:

• Detect PII, secrets, toxicity
• Prevent data exfiltration

Secrets management:

• API keys secured
• No credentials in code

Budget controls:

• Per-user, per-team, per-project
• Alerts and cutoffs

Infrastructure Layer

The foundation:

Model providers:

• OpenAI, Anthropic, Google, Azure
• Local models via Ollama

Vector databases:

• Pinecone, Weaviate, Chroma
• For RAG and semantic search

Data connectors:

• Databases (PostgreSQL, Snowflake, etc.)
• Files (S3, local)
• APIs (REST, GraphQL)

Deployment options:

• Cloud hosted
• Dedicated cloud
• On-premise
• Desktop

Building vs. Buying

Most organizations should buy, not build:

Build when:

• AI is core differentiator
• Unique requirements that platforms don’t address
• Sufficient AI/ML engineering capacity

Buy when:

• AI is enabling capability (not the product)
• Standard use cases
• Want to focus on outcomes, not infrastructure

Building the stack yourself takes 6-12+ months and ongoing maintenance. Buying gets you started in days.

Evaluating AI Platforms

Key questions:

Capability:

• Does it cover your use cases?
• Can developers and business users both work?
• Does it support your preferred models?

Security:

• Where does data reside?
• What deployment options exist?
• Is there proper authentication integration?

Governance:

• Can you enforce policies?
• Is there audit logging?
• Can you control costs?

Scalability:

• Will it grow with you?
• Can you add users, data, models?

The Platform Advantage

Platforms like Calliope provide:

Integrated stack: All layers work together Maintained: Updates, security patches, new features Supported: Help when you need it Faster: Start using, not building

The alternative is assembling pieces yourself—possible but expensive.

The Enterprise AI Checklist

Building enterprise AI capability:

• User interfaces for different personas
• Orchestration for complex workflows
• Governance for control and compliance
• Infrastructure for reliability
• Integration with existing systems
• Support for your deployment requirements

Build the stack, enable the organization.

Get the complete AI stack with Calliope →