{
  "slug": "noveum-ai",
  "name": "Noveum.ai",
  "description": "Noveum.ai is the closed-loop evaluation layer for production AI agents, enabling teams to debug, validate, and ship fixes. It provides a unified platform to trace, evaluate, and automatically fix chat, voice, and workflow agents by identifying failures, validating fixes through simulation, and shipping them as pull requests.",
  "url": "https://optimly.ai/brand/noveum-ai",
  "websiteUrl": "https://noveum.ai/",
  "logoUrl": "https://logo.clearbit.com/noveum.ai",
  "baiScore": 60,
  "bai_tier_status": "active",
  "bai_score_status": "active",
  "archetype": null,
  "archetype_status": "active",
  "category": "AI Agent Evaluation Platforms",
  "categorySlug": null,
  "keyFacts": [],
  "aiReadiness": [],
  "competitors": [],
  "competitorsProse": null,
  "inboundCompetitors": [],
  "aiAlternatives": [],
  "parentBrand": null,
  "subBrands": [],
  "updatedAt": "2026-09-14T05:14:05.648Z",
  "verifiedVitals": {
    "website": "https://noveum.ai",
    "category": "AI agent evaluation platform",
    "what_it_does": "Noveum is an AI agent evaluation platform that traces every run of production chat, voice, and workflow AI agents, scores them against calibrated scorers, simulates fixes, and ships validated fixes as pull requests to debug, validate, and ship fixes for AI agents.",
    "primary_audience": "Engineering teams running AI agents in production, AI platform teams across telecom, real estate & financial services, and enterprise and high-growth teams.",
    "core_product": "A closed-loop evaluation layer for production AI agents, encompassing NovaTrace (observability), NovaEval (evaluation), NovaSynth (simulation & synthetic data), NovaPilot (root cause & fixing), NovaGuard (runtime guardrails - Beta), and Noveum MCP (integration).",
    "pricing_model": {
      "kind": "freemium",
      "detail": "Offers a free tier for the first trace and options to book a demo or talk to sales for other plans."
    },
    "named_competitors": [
      "Langfuse"
    ]
  },
  "intentTags": {
    "problemIntents": [
      "AI agent failures in production",
      "Time-consuming manual debugging of AI agents",
      "Difficulty in validating AI agent fixes",
      "Ineffective agent evaluation without production data",
      "Missing context in voice agent debugging due to reliance on transcripts",
      "Multi-step agents failing between steps",
      "Overhead in managing agent evaluation and deployment pipelines"
    ],
    "solutionIntents": [
      "Automated AI agent debugging and fixing",
      "Closed-loop evaluation for production AI agents",
      "Traceability of LLM calls and agent steps (observability)",
      "Evaluation of AI agents with 100+ calibrated scorers",
      "Simulation and synthetic data generation for testing voice agents",
      "Automated generation and validation of AI agent fixes (NovaPilot)",
      "Integration with existing AI stacks (LangChain, OpenAI, Anthropic, etc.)",
      "Enterprise-grade deployment options (on-prem, BYO ClickHouse, SOC 2, GDPR, SSO)",
      "Reduced iteration time for AI agent improvements"
    ],
    "evaluationIntents": [
      "AI agent performance evaluation",
      "Root cause analysis of agent failures",
      "Backtesting of proposed fixes on failing calls",
      "End-to-end re-simulation of agent behavior",
      "Scoring chat, voice, and workflow agents",
      "Monitoring hallucination rates and call success rates",
      "Comparing agent performance before and after fixes"
    ]
  },
  "businessProfileClaims": [],
  "timestamp": 1789861458316
}