AI Assistant for Nodinite
Transform how you explore and understand your integration landscape with the AI Assistant for Nodinite.
Ask questions in natural language and get instant, contextual answers drawn from your live monitoring data, repository model, and business metadata — no SQL queries, no complex filters, just conversation.
What you'll achieve:
- Ask questions naturally — "Which Finance integrations failed today?" instead of building complex queries
- Get instant answers — AI understands your entities, relationships, and custom metadata to deliver precise results
- Stay secure — Choose local AI (OLLAMA), cloud AI (Azure OpenAI), or connect external tools via MCP protocol
- Empower all users — From operators to business analysts, anyone can query Nodinite data intuitively
- Connect any AI tool — Use Claude Desktop, Cline, or any MCP-compatible client to access Nodinite insights
Note
The AI Assistant is in active development with experimental features. Today: Repository queries (Entities, Relationships, Custom Metadata). Coming soon: Full CRUD operations, Log Views, BPM insights, and Audit Logs (subject to GDPR, retention, and PII governance).
Tip
Need AI-powered diagnostics log analysis? See AI Diagnostics for autonomous scanning of monitoring agent logs with local or cloud AI.
The Nodinite AI Assistant with integrated chat and configuration options.
Why AI in Nodinite?
Traditional monitoring tools provide raw data — the AI Assistant transforms it into understanding.
By interacting with Nodinite through natural language, users can surface insights, correlate events, and answer business questions instantly.
Business Value
- Smarter, faster decisions — Ask "Which integrations failed for our Finance domain today?" and get contextual answers in seconds
- Reduce time-to-resolution (MTTR) — From 30 minutes of manual log analysis to 60 seconds with AI — that's a 97% time savings
- Empower all roles — Technical operators, business analysts, and compliance teams can query Nodinite data intuitively
- Instant documentation — Generate summaries, impact analyses, and audit reports directly from your integration data
- Secure and compliant — Choose isolated local AI (OLLAMA), approved cloud services (Azure OpenAI), or connect external AI tools via MCP protocol
Quick Start — 5 Minutes to AI Insights
Using the Integrated AI Chat
The AI Assistant is built directly into Nodinite's web portal:
- Navigate to :Administration: → Settings → AI Assistant
- Configure your AI provider:
- OLLAMA — Local AI running on your infrastructure
- Azure OpenAI — Cloud-based AI service
- GitHub Models — Cloud-based AI service
- Click the AI Assistant button in the navigation bar
- Start asking questions: "Show me all failed integrations in the last 24 hours"
Configure your AI provider and settings in the AI Assistant settings dialog.
Connecting External AI Tools (MCP)
Nodinite exposes an MCP (Model Context Protocol) endpoint that allows external AI tools to access your integration data.
Note
MCP endpoint connectivity is available, but configuration details for external clients are still being finalized. Check back for updated documentation on connecting Claude Desktop, Cline, and other MCP-compatible tools.
How It Works
The AI Assistant provides two ways to access your Nodinite data:
Integrated Web Chat
Built directly into Nodinite's web portal — configure once and start querying.
- What it does: Chat interface in the Nodinite UI with direct access to your repository data
- Best for: Quick queries, operator troubleshooting, team members already working in Nodinite
- Configuration: :Administration: → Settings → AI Assistant (choose your AI provider and configure settings in the GUI)
MCP Endpoint (External AI Tools)
Nodinite exposes an MCP (Model Context Protocol) endpoint for external AI tools to access your integration data.
- What it does: Allows MCP-compatible AI assistants (like Claude Desktop, Cline, etc.) to query Nodinite data
- Best for: Power users who want to combine Nodinite insights with other tools in their AI workflow
- Status: MCP endpoint is available; external client configuration documentation coming soon
Both modes query the same Nodinite data and respect your authentication and permissions.
Example of the AI Assistant answering questions about your integration landscape.
MCP Endpoint — Connect External AI Tools
What is MCP?
The Model Context Protocol (MCP) is an open standard developed by Anthropic that allows AI assistants to connect to external data sources and tools. Think of it as "API for AI" — instead of you writing API calls, your AI assistant can discover and use Nodinite's capabilities automatically.
Why use the MCP endpoint with Nodinite?
- Universal compatibility — Works with any MCP-compatible AI client (Claude Desktop, Cline, Zed, custom tools)
- Multi-tool workflows — Combine Nodinite data with filesystem access, web search, and other MCP servers in one conversation
- Developer-friendly — Query Nodinite environments while coding in VS Code or other development tools
- Automation potential — Build custom AI workflows that react to Nodinite monitoring events
Available MCP Capabilities
Once connected, external AI tools can access these Nodinite capabilities:
| Capability | Purpose | Example Query |
|---|---|---|
| Search Entities | Find entities by name, type, or domain | "Find all SAP integrations" |
| Get Entity Details | Retrieve full configuration for an entity | "Show me details for the Finance API endpoint" |
| List Relationships | Discover dependencies and connections | "What endpoints depend on our Payment Gateway?" |
| Query Custom Metadata | Search by business attributes | "Which integrations are tagged as 'Critical'?" |
| Get Domain Hierarchy | Understand organizational structure | "Show me the Finance domain structure" |
The AI assistant automatically selects the right capabilities based on your question.
Connecting External AI Tools
Note
Configuration documentation coming soon. The MCP endpoint is available in Nodinite, but detailed setup instructions for external clients (Claude Desktop, Cline, VS Code, etc.) are still being finalized. This section will be updated with:
- Authentication and API endpoint details
- Configuration examples for popular MCP clients
- Step-by-step connection guides
Check back for updates or contact Nodinite support for early access guidance.
What the AI Assistant Knows
The AI Assistant has comprehensive access to your Nodinite Repository Model, including:
Entities & Configuration
- All entity types: Endpoints, Domains, Applications, Business Processes, Custom Types
- Entity metadata: Names, descriptions, environments, lifecycle stages (Development, Production, etc.)
- Custom metadata fields: Business owners, criticality levels, compliance tags — any custom metadata you've defined
- Relationships: Dependencies, connections, and hierarchies (e.g., "Which Endpoints belong to which Applications?")
Business Context
- Domains and BPM: Business Process Models, organizational structure, which integrations support which business capabilities
- Tags and classifications: Search by any tag or custom classification you've applied
- Technical details: Connection strings, settings, configurations (respecting security permissions)
Current Limitations
- Log data: Not yet available (coming soon, subject to GDPR/retention/PII governance)
- Real-time monitoring: Not yet available (Repository data only for now)
- CRUD operations: Read-only today; create/update/delete capabilities in development
Example Conversations
Troubleshooting:
"Which Finance integrations failed today?" → AI identifies failed integrations, suggests investigating dependencies
Business Impact Analysis:
"If we take the Payment Gateway offline for maintenance, what will be affected?" → AI lists downstream dependencies and recommends maintenance windows
Compliance & Reporting:
"List all integrations tagged as 'GDPR-Critical' in Production" → AI finds tagged entities and offers to export for audit
Onboarding & Documentation:
"Explain how our Order Processing flow works" → AI describes integration flow across domains and offers to visualize in Mapify
Security & Governance
Data Privacy
- Local AI option: Use OLLAMA to run AI models entirely on your infrastructure — no data leaves your network
- Cloud AI option: Choose approved cloud providers (Azure OpenAI, GitHub Models) with enterprise agreements
- MCP connections: Authenticated via API keys with same role-based access control as Nodinite Web API
Access Control
- AI Assistant respects your existing Nodinite permissions — users only see data they're authorized to access
- External MCP connections use Nodinite's authentication system
- Audit logs track all AI queries and data access
Best Practices
- Start with local AI — Test with OLLAMA before enabling cloud AI in Production
- Review AI responses — AI-generated insights should be verified, especially for critical decisions
- Monitor usage — Track who's using AI Assistant and what questions are being asked (audit logs)
- Secure external access — If using the MCP endpoint with external tools, ensure proper authentication is configured
Advanced Use Cases
Automated Incident Response:
Monitoring alerts trigger AI analysis via MCP → AI suggests remediation → Automation executes fixes → Summary posted to Teams/Slack
Custom Dashboard Generation:
Natural language query → AI calculates metrics → Script renders dashboard in PowerBI/Grafana
Development & Testing:
Developers query Nodinite environments during development: "Check if Test environment has latest CustomerAPI version"
Measuring Success
Key Metrics
- MTTR (Mean Time to Resolution): Track how AI Assistant reduces incident investigation time
- Self-service adoption: Percentage of queries answered without escalation to senior engineers
- Cross-team usage: Business analysts and non-technical users querying Nodinite data
- Audit compliance: Time savings in compliance reporting and documentation generation
Example ROI Calculation
Before AI Assistant:
Average incident investigation: 30 minutes × 100 incidents/month = 50 hours/month × $75/hour = $3,750/month
After AI Assistant:
Average investigation: 5 minutes × 100 incidents/month = 8.3 hours/month × $75/hour = $625/month
Savings: $3,125/month ($37,500/year)
Total estimated ROI with self-service queries, faster compliance reporting, and reduced escalations: $50,000-$75,000/year for mid-sized integration environments
Tips for Better Results
- Be specific: "Which SAP integrations failed today?" is better than "Show me failures"
- Use business terms: The AI understands Domains, Business Processes, and custom metadata fields
- Ask follow-up questions: AI maintains conversation context — build on previous answers
- Verify critical decisions: AI provides insights fast, but always validate before making configuration changes
Frequently Asked Questions
Q: What AI models does Nodinite support?
A: For integrated web chat: OLLAMA (local), Azure OpenAI (cloud), GitHub Models (cloud). For MCP mode: Any MCP-compatible client (Claude Desktop, Cline, Zed, custom apps).
Q: Can the AI Assistant change my Nodinite configuration?
A: Not yet — current version is read-only. CRUD operations (create, update, delete) are in development and will be released with admin approval workflows.
Q: Does AI Assistant access log data?
A: Not in the current version. Log data integration is coming soon, subject to GDPR, retention policies, and PII governance. Today: Repository Model only (Entities, Relationships, Custom Metadata).
Q: Is my data sent to external AI providers?
A: It depends on your configuration:
- OLLAMA: No — AI runs entirely on your infrastructure
- Azure OpenAI / GitHub Models: Yes — data is sent to cloud AI providers (ensure your enterprise agreement covers this)
- MCP mode: Depends on which AI client you connect (Claude Desktop sends data to Anthropic, Cline with local models keeps data local)
Q: Can I use AI Assistant in Production?
A: Yes, but with caution:
- Read-only queries: Safe for Production
- Local AI (OLLAMA): Recommended for sensitive environments
- Cloud AI: Ensure compliance with your organization's data governance policies
- External MCP access: Ensure proper authentication and network security if exposing the MCP endpoint
Q: How do I connect external AI tools via MCP?
A: The MCP endpoint is available in Nodinite, but detailed configuration documentation for external clients is coming soon. Contact Nodinite support if you need early access guidance for connecting Claude Desktop, Cline, or other MCP-compatible tools.
Q: Can I customize the AI's behavior?
A: Not yet, but roadmap includes:
- Custom system prompts (e.g., "Always prioritize Finance domain integrations")
- Domain-specific knowledge bases
- Integration with your organization's runbooks and documentation
Q: What if the AI gives a wrong answer?
A: AI responses are probabilistic and should be verified for critical decisions. If you notice errors:
- Check if the question was ambiguous or lacked context
- Verify the AI has access to current data (Repository Model sync)
- Report issues to Nodinite support for model tuning
Q: How does AI Assistant compare to Mapify?
A: They're complementary tools:
- AI Assistant: Natural language queries, text-based answers, conversational insights, automation-friendly
- Mapify: Visual dependency graphs, interactive exploration, graph-based navigation, high-level architecture view
Use AI Assistant for asking questions and Mapify for visualizing relationships.
Q: What's the difference between AI Assistant and AI Diagnostics?
A: They serve different purposes:
- AI Assistant: Query your integration landscape (Entities, Relationships, Custom Metadata) using natural language — ask questions like "Which Finance integrations failed today?"
- AI Diagnostics: Autonomous analysis of monitoring agent diagnostics logs to detect issues, provide root-cause analysis, and suggest fixes
Use AI Assistant for exploration and queries, AI Diagnostics for automated log analysis and troubleshooting.
Related Topics
- Mapify — Visual dependency graphs and interactive relationship exploration
- AI Diagnostics — AI-powered diagnostics log analysis for monitoring agents
- Repository Model — Understanding Entities, Relationships, and Custom Metadata
- Monitoring Service — Real-time monitoring (coming soon to AI Assistant)
- Log API — Accessing log data (coming soon to AI Assistant)
- Settings — Configure AI Assistant (OLLAMA, Azure OpenAI, GitHub Models)