🚀 Noveum.ai Overview

Welcome to Noveum.ai—the end to end observability and autofix platform built specifically for AI applications. Whether you're building LLM-powered chatbots, RAG systems, multi-agent workflows, or any AI-driven application, Noveum provides the insights you need to understand, Propose the fixes and autofix it for you.

Platform Overview

Dashboard Overview

Real-time monitoring and analytics dashboard

Traces & Spans

Hierarchical trace visualization

Traces Visualization

Advanced trace analysis and visualization

Agent Flow

Multi-agent workflow visualization

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Dashboard Overview

Real-time monitoring and analytics dashboard

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🔄 How Noveum Works - Step-by-Step Process

🚀 Core Capabilities

Why AI Applications Need Specialized Observability

Traditional monitoring tools fall short when it comes to AI applications because they don't understand:

• AI-Specific Metrics: Token usage, model costs, prompt effectiveness
• Complex Workflows: Multi-step RAG pipelines, agent interactions, tool usage
• Context Flow: How data moves through embeddings, retrievals, and generations
• Cost Attribution: Which operations drive your AI spending
• Quality Metrics: Beyond latency - understanding output quality and relevance
• Custom Evals: Scorers tailored to your specific use case and business requirements

Noveum.ai bridges this gap with purpose-built observability for the AI era.

🔄 Complete Workflow - How it happens

Step 1: SDK Integration

Add tracing to your code with minimal changes using context managers:

from noveum_trace import trace_llm
import openai
 
# Minimal LLM tracing
with trace_llm(model="gpt-4", provider="openai"):
    openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": "Hello"}]
    )

Step 2: Trace Collection - Noveum-Trace

Comprehensive end-to-end tracing for your AI application

• LLM Operations: Model calls, token usage, costs
• RAG Pipelines: Document retrieval, embeddings, context assembly
• Agent Workflows: Multi-agent interactions, tool usage, decision trees
• Tool Calls: Function calls, tool executions, parameter passing
• API Calls: External service requests, responses, status codes
• DB Calls: Database queries, transactions, connection pooling
• Custom Operations: Business logic, external APIs, data processing

Step 3: Platform Visualization

View and analyze traces in the Noveum dashboard:

• Hierarchical Trace Views: Complete workflow visualization
• Performance Metrics: Latency, throughput, error rates
• Cost Analysis: Token usage, provider costs, optimization opportunities
• Real-time Monitoring: Live dashboards and intelligent alerting

Step 4: Background ETL Processing

In the background, automated ETL jobs continuously process your traces:

• Dataset Creation: Convert traces to evaluation datasets automatically
• Model Evaluation: Run systematic evaluations with NovaEval scorers
• Score Generation: Calculate performance metrics and quality scores
• Dashboard Updates: Push results to real-time dashboards for analysis

Step 5: Score Visualization & Reasoning - NovaEval

Access detailed insights through the Noveum Dashboard:

• Call-by-Call Analysis: View scores and reasoning for every individual AI call
• Performance Breakdown: Understand why certain calls performed better or worse
• Reasoning Transparency: See the evaluation logic behind each score
• Interactive Exploration: Drill down into specific traces and spans for detailed analysis

Example:

answer_relevancy scorer = 3.1

Reasoning:
The response contains hallucinated information and fails to address the user's 
actual question. The answer introduces fabricated facts not present in the 
provided context and veers off-topic, demonstrating poor relevancy alignment.

Step 6: Intelligent Autofix Suggestions - NovaPilot

NovaPilot analyzes low-scoring traces and provides actionable recommendations:

• Pattern Recognition: Identifies common issues across failed or low-scoring traces
• Root Cause Analysis: Validates evaluation reasoning and pinpoints specific problems
• Suggested Fixes: Generates concrete improvement recommendations based on detected patterns
• Guardrail Examples: Proposes system prompt enhancements, such as adding safety guardrails or clarifying instructions
• Contextual Guidance: Provides implementation examples tailored to your specific use case

Example: If NovaPilot detects hallucinations in responses, it might suggest:

Add the following guardrail to your system prompt:
"Always cite sources and acknowledge when information is uncertain. 
Never fabricate facts or make assumptions beyond the provided context."

Noveum Products

Noveum Trace

Trace all your AI calls

Learn More →

NovaEval

Evaluate with comprehensive scorer library

Learn More →

NovaPilot

Automated analysis & fixes

Learn More →

Noveum SDK

Python SDK with comprehensive API

Learn More →

🎯 Complete End-to-End AI Monitoring

Noveum traces everything, provides powerful dashboards for visualization, and offers comprehensive scoring and evaluation - delivering complete end-to-end monitoring for your AI applications.

🚀 Quick Start Actions

🤝 Community & Support

• 💬 Discord Community: Join our Discord
• 📧 Email Support: support@noveum.ai
• 🐛 Bug Reports: GitHub Issues
• 📖 Knowledge Base: Help Center

Ready to get started? Head to our SDK Integration Guide to begin tracing your AI applications in under 5 minutes!

Built by developers, for developers. Noveum.ai understands that AI applications are different, and we've designed our platform from the ground up to meet their unique observability needs.