Noveum.ai provides an AI observability platform for enterprise LLM‑driven agents, capturing hierarchical traces of prompts, responses, tool calls, and agent handoffs. The platform offers real‑time quality evaluation across 30+ metrics, cost analytics by model and user, and AI‑generated remediation suggestions, with integration via Python decorators, TypeScript SDKs, and LangChain/LangGraph callbacks.
Funding
Funding not disclosed
Founders
Product
Problem
B2B organizations that deploy large language model (LLM) agents at scale lack visibility into model behavior, multi‑agent orchestration, and runtime costs. Traditional application‑performance monitoring tools cannot trace LLM calls, detect hallucinations, or surface silent quality drift, leading to prolonged debugging cycles and unexpected budget overruns.
Solution
Noveum.ai delivers an AI observability platform designed for production LLM‑driven agents. The service captures hierarchical traces of every prompt, response, tool invocation, and agent handoff, presenting them in a unified dashboard. Built‑in evaluators automatically score outputs across more than 30 quality dimensions, flagging hallucinations and relevance issues in real time. Cost analytics break down token usage and API spend by model, feature, and user, enabling proactive budget control. The platform also generates AI‑powered remediation suggestions to improve prompts or configuration. Integration is achieved via lightweight Python decorators or TypeScript SDKs that support LangChain, CrewAI, LangGraph, and custom pipelines, with zero performance overhead and OpenTelemetry compatibility.
Target Audience
The platform targets AI product and operations teams in enterprises that run customer‑facing chatbots, autonomous research agents, or any LLM‑based service requiring reliable, cost‑controlled production deployments.
Features
- Hierarchical trace visualization that maps end‑to‑end request flows across multiple LLM calls and tool interactions
- Multi‑agent support for tracking handoffs and debugging complex orchestration patterns
- Automated quality evaluation using 30+ LLM‑as‑judge metrics (accuracy, relevance, safety, custom business criteria)
- Real‑time cost and usage analytics segmented by model, feature, and user to identify optimization opportunities
- AI‑driven auto‑fix recommendations for prompt tuning and configuration adjustments
- Python decorators, TypeScript SDK, and LangChain/LangGraph callback handlers for rapid, zero‑overhead instrumentation
- OpenTelemetry‑compatible data pipeline that scales to millions of traces per day
- SOC 2 Type II, GDPR, and HIPAA‑aligned security controls with audit‑ready logs and role‑based access