Noveum.ai vs Arize & Braintrust
A detailed, practical reference comparing evaluation philosophies, data needs, integrations, outputs, and recommended workflows.
Useful for product teams, engineering leads, reliability engineers, and investors evaluating LLM & agent observability solutions.
"Did the agent follow the process correctly?"
"Was the output correct vs ground truth?"
"Does the answer match expected output?"
Noveum.ai Analytics Dashboard — Real-time agent monitoring & evaluation with 68+ built-in scorers
Understanding the Key Differences
Each platform excels in different areas. Understanding these differences will help you choose the right solution for your specific needs.
Noveum.ai
Full-stack LLM & agent evaluation + compliance platform built around trace-level understanding.
Primary Focus:
Evaluates how and why an agent behaved the way it did
- 68+ built-in scorers across 13 categories
- No ground truth required for most evaluations
- Complete execution traces with tool calls
- LLM-as-judge scoring for quality detection
- Agent behavior, RAG, hallucination, safety
Arize
Best suited for traditional Machine Learning and statistical monitoring.
Primary Focus:
Measures final answer correctness against ground truth
- Feature drift detection (PSI)
- Embedding-based monitoring
- Classical ML governance
- Statistical model monitoring
- Model comparison across versions
Braintrust
Best suited for human annotation, gold-label creation, and training-data generation.
Primary Focus:
Measures final answer correctness against ground truth
- Large-scale human annotation workflows
- Reviewer management and QA
- Dataset creation for audits
- Training data generation
- Input + expected pairs evaluation
One-Line Differentiator
"Did the agent follow the process and use the system/tools/context correctly?"
"Was the final answer correct compared to a ground truth?"
This fundamental difference shapes everything else: data requirements, evaluation methods, integration complexity, and ideal use cases.
High-Level Comparison Table
Noveum currently provides 68+ built-in scorers across 13 categories, including Agent, Conversational, RAG, Safety, Hallucination, Quality, and more.
| Dimension | Noveum.ai | Arize | Braintrust |
|---|---|---|---|
| Primary focus | Agent trajectory, trace correctness, behavioral checks | Model output quality, observability, dataset-driven evals | Dataset creation, annotation, expected-output based evals |
| Ground truth required | No (not for most checks) | Often yes for accuracy metrics | Yes — input + expected pairs are central |
| Uses production traces? | Yes — core data source | Yes — but needs labeling for GT metrics | Yes — can generate datasets but must add expected values |
| Hallucination detection | Structural detection (context mismatch, fabricated outputs) | Possible via LLM-judge or human labels | Human annotation required |
| Typical metrics | Prompt adherence, tool usage correctness, context leakage, step validity, drift | Accuracy, precision/recall, calibration, ROUGE/BLEU proxies, embeddings similarity | Accuracy, annotator consensus, dataset coverage |
| Best for | Agent debugging, reliability engineering, production monitoring | Model benchmarking, drift detection, LLM QA | Curating labeled test sets and human-in-the-loop evals |
| Integration complexity | SDK + trace schema + agent instrumentation | SDK + traces + annotation pipelines | Dataset upload + annotation UI / APIs |
Core Concepts Explained
Before comparing platforms, it's essential to understand these fundamental concepts that differentiate evaluation approaches.
Trajectory vs. Output
Trajectory
The ordered sequence of events the agent performs: system prompt, tool calls (with arguments), tool responses, retrievals, intermediate reasoning steps, and final answer.
Evaluating trajectory checks HOW the model arrived at an answer.
Output
Final text/structured response. Output evaluation checks WHAT the model answered against a reference.
Noveum inspects trajectory. Arize/Braintrust are usually focused on output.
Ground Truth and When It Matters
When Ground Truth is Essential
- Measuring correctness or computing traditional metrics
- Accuracy, exact-match, BLEU/Rouge evaluations
- Braintrust and Arize's ground-truth-based workflows
When Ground Truth is Optional
- Structural or behavioral failures
- Calling the wrong tool
- Using context not supplied
- Fabricating numbers not present in data
These can be detected with robust trace analysis alone (Noveum's approach).
LLM-as-Judge: A Middle Path
Arize & Others
Support LLM-as-Judge: use an LLM to score outputs with heuristics rather than fixed expected values. This reduces manual labeling but introduces judge noise and calibration needs.
Noveum's Approach
Checks are less subjective: they rely on deterministic schemas, tool contracts, and trace invariants, which are easier to operationalize for agent correctness.
Detailed Differences by Category
Explore the specific differences across key dimensions to understand which platform best fits your needs.
Data & Schema
Noveum
- Trace events (system prompt, user input, each agent step, tool call events, tool responses, retrieved docs, embeddings)
- Metadata (request id, timestamps, user id), agent config
- Ordered events with typed tool-call records
- Source of truth for retrievals, tokenized contexts
- No required expected_answer column for many failure modes
Arize
- Model inputs and outputs, optional ground truth labels
- Scoring metadata (confidence, probabilities), embeddings, features
- Input/output pairs, label columns for ground-truth metrics
- Timestamps for drift analysis
Braintrust
- Dataset rows with input, expected, plus optional annotation fields
- Tags, notes, annotator id
- Canonical dataset shape enabling human annotation
- Consensus and dataset export
Annotation Workflows
Noveum
- Minimal human annotation required
- Human review used for escalations or one-off signals
- Focus on automating detection via rule-based or trace-aware heuristics
Arize
- Supports human-labeled datasets for ground truth
- Human review to create high-quality test sets for calibration
Braintrust
- Built for human annotation at scale
- Tools to crowdsource or manage annotators
- Dataset versioning and quality control
Hallucination Detection
Noveum
- Compare statements/claims against retrieved context and tool outputs in trace
- Detect unsupported claims or invented tool outputs
- Flag context leakage and out-of-context assertions
Arize
- Human labeling of hallucinations
- LLM-as-Judge prompts that assess hallucination likelihood
- Requires careful calibration
Braintrust
- Rely on annotators to mark hallucinated content
- Produces human-grounded labels
Metrics & Alerts
Noveum
- Alerts on structural failures (tool-misuse rate, context-mismatch rate, prompt-violation rate)
- Trending of failure modes
- Per-agent scenario scoring
Arize
- Quantitative metrics (accuracy, F1, calibration curves, similarity scores)
- Drift detection, feature attribution
- Per-model comparisons
Braintrust
- Dataset quality metrics (annotator agreement, coverage)
- Evaluation run metrics (pass/fail vs expected answers)
Use Cases
Noveum
- Production agent monitoring
- Compliance checking for tool usage
- Debugging complex agent orchestration
- Regression detection in agent reasoning flows
- Live traffic checks without labeling burden
Arize
- Model comparison and benchmarking
- Long-term metric tracking
- Experiments comparing model versions
- Supervised metric reporting to stakeholders
Braintrust
- Curated benchmark creation
- Dataset curation for supervised evaluation
- Human-in-the-loop labeling projects
Example Workflows
See how each platform is typically used in practice with these real-world workflow examples.
Production Monitoring with Noveum
No Ground Truth RequiredIngest Traces
Integrate noveum-trace SDK to capture structured trace events from your AI agents in production
Auto-Generate Datasets
Traces are automatically converted to dataset items using AI-generated ETL mapper code
Run Evals & Scorers
73+ scorers analyze each trace, identify errors, and provide detailed reasoning for every issue
NovaPilot Experiments
AI agent uses scores and reasoning to generate fix experiments, running them in simulated environments across generations
Auto-Apply Fixes
Final tested improvements (prompts, models, parameters) are delivered as a PR ready to merge
Benchmarking with Arize
Small Ground Truth SetCollect Traces
Gather representative traces from production
Create Labels
Sample a subset and create human-labeled expected answers
Upload Data
Upload input/output/expected to Arize
Run Reports
Run accuracy and calibration reports; use LLM-as-Judge to expand coverage
Model Selection
Use results to choose model/version for production
Creating a Braintrust Dataset
From LogsExport Logs
Export logs as CSV (input, model_output, metadata)
Add Expected Values
Use Braintrust UI/API to add expected values and start annotation rounds
Monitor Agreement
Monitor annotator agreement and finalize dataset for model evaluation
Integration Points & Engineering Effort
Understanding the integration requirements and ongoing effort needed for each platform.
Noveum.ai
Engineering Requirements
- Instrument agent to emit structured, ordered traces
- Best if tool calls and retrievals have typed schemas
Effort Level
Requires tracing and type instrumentation, but lower annotation pain
Deployment
Runs on live traffic; able to surface issues fast
Arize / Braintrust
Engineering Requirements
- Capture inputs/outputs reliably
- Establish annotation flow for ground truth
- Human + tooling + storage for labels
Effort Level
Annotation pipelines and human QA; higher costs for labeling
Deployment
Powerful for benchmarking and compliance reporting, but annotation overhead is a gating factor
Choosing the Right Tool
Use this framework to determine which platform is best for your specific needs.
If You run agentic systems that orchestrate tools
and care about "did the agent behave correctly"
If You need strict accuracy metrics against a known gold standard
(QA correctness, retrieval exactness, labeled tasks)
If You want both
continuous monitoring AND periodic benchmarks
Ready to Try Trace-Based Evaluation?
Start monitoring your agents with Noveum.ai today. No ground truth required, no manual labeling overhead.
Limitations & Trade-offs
No evaluation approach is universally sufficient. Understanding where each platform excels and falls short helps make the right choice.
Where Noveum.ai Can Have Limitations
Does not measure semantic correctness (when ground truth is required)
Noveum excels at detecting structural and provenance-based errors using complete trace context. It can surface partial correctness, degrees of completeness, and context-supported vs. unsupported claims. However, cases requiring external canonical answers (precise numeric solutions, legal statutes, regulated medical facts) may still need ground truth.
Weak for some canonical ground-truth needs
For tasks demanding a single authoritative ground truth (exact math solutions, regulatory compliance assertions matching statute text), organizations often still maintain a gold dataset for auditability.
Depends on trace quality
Noveum's strengths are proportional to trace fidelity. Well-instrumented traces (typed tool-call records, retrieval provenance, token-level context) enable rich scoring; poor traces reduce coverage.
Operational complexity for non-instrumented systems
If you cannot or will not instrument your agent to emit detailed traces/spans, Noveum's advantages are limited. This is why Noveum provides ETL pipelines to convert raw traces into clean, enriched dataset items.
Where Arize is a Better Fit
Traditional ML model monitoring
Arize is a strong fit for non-LLM / non-agent workloads: classical ML models, tabular models, time-series forecasting, recommendation systems, classification/regression pipelines.
Feature-level drift & statistical ML analytics
Arize excels at feature distribution drift, population stability index (PSI), embedding drift, and statistical monitoring over long time horizons for ML systems.
ML governance & compliance reporting
In organizations where ML governance frameworks already exist (model cards, dataset lineage, statistical audits), Arize fits naturally.
Note: Noveum is explicitly not designed for traditional ML and does not aim to replace Arize in this space.
Where Braintrust is a Better Fit
Human annotation at scale
Braintrust is optimized for large-scale human review workflows, reviewer management, and annotation QA. Noveum minimizes human labeling and is not an annotation platform.
Explicit gold-label creation for audits
When regulations or customers require explicitly human-verified labels stored as datasets, Braintrust is often required.
Training data generation
Braintrust-labeled datasets can be reused directly for fine-tuning or supervised learning workflows.
When Each Tool Wins
A quick reference guide to help you choose the right platform for each scenario.
| Scenario | Best Choice |
|---|---|
Agent misuse, tool errors, hallucinations, partial correctness | Noveum.ai |
Live production monitoring without labels | Noveum.ai |
Model comparison without ground truth | Noveum.ai |
LLM-as-Judge evaluations at scale | Noveum.ai |
Debugging complex agent flows | Noveum.ai |
Traditional ML (non-LLM) monitoring | Arize |
Human annotation & gold-label creation | Braintrust |
Publishing accuracy metrics with ground truth | Arize / Braintrust |
Human-reviewed correctness | Braintrust |
Important Clarifications
Model Comparison
Noveum is stronger than Arize/Braintrust for agent and LLM model comparison when ground truth is unavailable. You can run the same agent or prompt against GPT-4, Claude, Gemini, etc., and compare aggregate scores across 68+ scorers (hallucination, completeness, context fidelity, tool correctness, safety, bias, format, etc.) using identical traces and evaluation logic.
LLM-as-Judge at Scale
Noveum provides first-class LLM-as-judge scoring across its scorer library. It can identify hallucination, partial correctness, accuracy failures, and qualitative degradation directly from traces, without expected outputs. This is not an area where Arize is better.
Recommendations & Best Practices
Follow these proven strategies to get the most out of your evaluation approach.
Instrument Early
Structured traces (tool events + retrieval provenance) are the foundation for both behavioral checks and for creating labeled datasets later.
Start with Trajectory Checks
Catch structural issues cheaply with Noveum-style checks before investing heavily in labeling.
Maintain a Small GT Test Set
Keep a curated labeled set (50–500 examples) for calibration of any LLM-judge methods and for regression testing.
Use Hybrid Flows
Detect likely failures with trajectory checks, then send prioritized samples to human labelers (via Braintrust-style tools) to build GT selectively.
Version Datasets and Trace Schemas
Make it simple to reproduce evaluations across model changes.
Example Implementations
Example Alert Rules (Noveum Style)
If scenario required bank_api call but no tool event for bank_api appears → alert.
If final answer cites a doc id or snippet that wasn't present in retrieved docs → alert.
If answer includes user data not in current session context → alert.
If final_answer event occurs before required validation/step events complete → alert.
Example Evaluator Templates (Arize/LLM-Judge)
Accuracy Template
Prompt judge with input + model_output + expected; ask for pass/fail and confidence.
Hallucination Template
Prompt judge with input + model_output + retrievals; ask if any claims are unsupported.
Noveum ETL & Golden-Dataset Workflow
Traces → ETL → Enriched items (goldens): Noveum provides ETL pipelines that take raw traces/spans from production, normalize and enrich them (add retrieval provenance, tool-call typing, tokenized contexts, metadata), and produce clean dataset items. These items can act as goldens for targeted benchmarking, be exported for human annotation, or be used directly as high-quality test cases in automated eval pipelines.
Why this matters: It allows teams to get the best of both worlds: continuous, trace-based monitoring plus the ability to extract and maintain curated golden datasets for audits, regulatory needs, or external benchmark comparisons.
Frequently Asked Questions
Get answers to the most common questions about choosing between these platforms.
Yes — Noveum can triage and prioritize traces. Use high-confidence structural failure detections to seed a labeled dataset for human review or for Arize benchmarking.
Ready to Try Trace-Based Evaluation?
See how Noveum.ai can help you understand and improve your AI agents without the overhead of manual labeling.
Evaluating for your enterprise?
Get a personalized demo and see how Noveum.ai fits your specific use case.
Explore more resources
About Noveum.ai — The AI Reliability, Evaluation & Auto-Fixing Platform
Noveum.ai is the leading platform for AI Reliability, LLM Evaluation, AI Agent Monitoring, and automated improvement workflows. As companies deploy AI Agents to power customer service, workflow automation, voice interfaces, RAG search systems, and enterprise copilots, the need for predictable, safe, and efficient AI performance has never been higher. Modern AI Agents frequently hallucinate, skip steps, misunderstand instructions, misuse tools, or generate unpredictable outputs that undermine trust. Noveum.ai solves this across three pillars: Monitoring, Evaluation, and Auto-Fixing.
With a lightweight SDK integration, Noveum captures everything your AI Agent does: prompts, decisions, intermediate reasoning, tool calls, token usage, context retrievals, model behaviors, and workflow execution traces. This creates a complete AI observability layer, giving companies unprecedented insight into how their agents behave internally. From cost spikes to hallucination risks, from broken workflows to reasoning failures, Noveum identifies issues before customers experience them.
The platform also includes an advanced evaluation engine powered by 67 specialized scoring functions, covering accuracy, factuality, hallucination detection, RAG retrieval quality, role adherence, conversation health, structured output validation, safety, bias, and compliance. Noveum.ai generates detailed reasoning and provides clear explanations behind every score — helping teams see why an issue happened, not just where.
Finally, Noveum assists with AI agent auto-fixing by running controlled experiments in a sandbox. It tests prompt variations, model alternatives, parameter adjustments, and tool-call corrections to find the best configuration. After evaluation, Noveum provides improvement recommendations and a full report on how to fix issues. Teams can apply improvements manually or enable auto-fixing workflows that automatically update the agent logic and push PRs.
Noveum.ai is the world's first end-to-end AI Agent Maintenance Platform, helping teams achieve reliability, cost efficiency, and continuous performance improvement without hiring a large ML engineering team. This footer provides a comprehensive reference to how Noveum.ai works and includes deep explanations of all evaluation scorers used to assess AI quality.
Why AI Reliability Matters for Modern Teams
AI Agents are becoming the backbone of digital operations — answering customer questions, triaging support tickets, qualifying leads, routing tasks, summarizing documents, analyzing data, and automating workflows. But unlike traditional software, AI is inherently probabilistic. Even well-designed agents will:
- hallucinate facts
- misunderstand instructions
- misuse tools
- return irrelevant answers
- generate inconsistent formats
- exceed token budgets
- produce partial responses
- violate compliance rules
- degrade over time without monitoring
Unreliable AI leads to increased support burden, lost revenue, compliance risks, poor customer experience, and rising operational costs. Noveum.ai provides the infrastructure needed to measure, monitor, and maintain AI Agent quality at scale.
Noveum Monitoring — Complete AI Observability
Noveum captures all agent behaviors in real time:
- Full prompt & response history
- Tool call inputs and outputs
- Token usage tracking
- Cost attribution per action
- System and user message flows
- Context retrieval logs
- Branching workflow behavior
- Intermediate reasoning
- Agent chain-of-thought traces
- Latency analysis
- Failure modes and exceptions
The Agent Visualizer produces a pre-order execution graph showing how the AI Agent reasons and acts across steps. This visual monitoring layer enables developers, product managers, and operations teams to understand the internal logic of their AI Agents and diagnose complex failure patterns.
Noveum Evaluation — Scoring AI Performance with 67 Specialized Scorers
Noveum.ai includes the most comprehensive evaluation library available for enterprise AI Agents. Every scorer has been designed to evaluate a different dimension of AI output quality — from hallucination detection and factual grounding to conversation health, safety compliance, RAG retrieval quality, structured formatting, and system performance.
Each evaluation produces:
- numeric score
- natural-language reasoning
- step-by-step justification
- references to context used
- recommendations for improvement
Full Scorer Library With Explanations (All 67 Scorers)
1. Accuracy, Matching & Semantic Quality Scorers
- ExactMatchScorer
Checks if the AI output perfectly matches the expected answer. Ideal for classification or deterministic tasks. - AccuracyScorer
Measures the proportion of correct responses across a dataset — essential for baseline LLM quality benchmarking. - MultiPatternAccuracyScorer
Supports multiple correct output forms. Useful for flexible workflows where several answers are acceptable. - F1Scorer
Balances precision and recall to measure how accurately the AI captures all important elements. - BLEUScorer
An n-gram matching metric for evaluating text similarity. Used broadly in summarization and generation tasks. - ROUGEScorer
Measures recall overlap with reference text, especially useful for long-form content evaluation. - LevenshteinSimilarityScorer
Calculates edit distance between expected and generated text, identifying near-matches efficiently. - SemanticSimilarityScorer
Uses embedding-based comparisons to assess meaning similarity, not just surface text similarity. - InformationDensityScorer
Scores how substantive and information-rich an answer is, identifying fluff or low-density output. - ClarityAndCoherenceScorer
Evaluates readability, logical structure, and flow — vital for customer-facing AI responses. - AnswerCompletenessScorer
Checks whether the AI addressed every part of the prompt completely. - QuestionAnswerAlignmentScorer
Ensures direct alignment between user question and generated answer. - TechnicalAccuracyScorer
Validates correctness for domain-specific answers in finance, engineering, legal, or medical tasks. - TerminologyConsistencyScorer
Ensures specialist terminology is used appropriately and consistently in AI responses.
2. Relevance, Faithfulness & Hallucination Scorers
- AnswerRelevancyScorer
Measures how relevant the AI's response is to user intent. - FaithfulnessScorer
Checks whether the AI stays grounded in provided context without adding unsupported claims. - FactualAccuracyScorer
Identifies factual inaccuracies by comparing output with known truths. - HallucinationDetectionScorer
Detects fabricated or invented information. One of the most important scorers for enterprise safety. - ConflictResolutionScorer
Identifies contradictions in the AI's reasoning. - ContextPrioritizationScorer
Ensures the AI selects the most important context pieces when generating a response. - ContextFaithfulnessScorerPP
Validates that the answer is grounded in provided documents. - ContextGroundednessScorer
Checks if every important claim is supported by context. - ContextCompletenessScorer
Measures whether the AI uses enough context to form a complete answer. - ContextConsistencyScorer
Ensures consistency with reference material across statements.
3. RAG (Retrieval-Augmented Generation) Scorers
- ContextualPrecisionScorer
Evaluates relevance of retrieved context snippets used in the final answer. - ContextualRecallScorer
Measures how much relevant content the AI included from the available context. - RAGASScorer
Industry-standard RAG quality scoring across groundedness, relevance, and answer quality. - ContextualPrecisionScorerPP
Enhanced precision evaluation for more complex retrieval chains. - ContextualRecallScorerPP
Advanced recall scoring that checks deeper semantic relevance. - RetrievalF1Scorer
Harmonic mean of precision and recall for retrieval effectiveness. - RetrievalRankingScorer
Evaluates document ranking quality for RAG pipelines. - RetrievalDiversityScorer
Ensures retrieval results include diverse, non-redundant knowledge sources. - AggregateRAGScorer
Provides a combined score summarizing RAG performance. - RAGAnswerQualityScorer
Evaluates whether the final answer is high-quality and well-grounded.
4. Conversation & Role Behavior Scorers
- ConversationRelevancyScorer
Checks turn-by-turn relevance in multi-turn dialogues. - ConversationCompletenessScorer
Ensures the entire user intent has been addressed in the conversation. - RoleAdherenceScorer
Ensures the agent sticks to its assigned role and persona. - ConversationalMetricsScorer
Composite conversational quality scorer measuring flow, engagement, and relevance.
5. Safety, Bias & Harm Prevention Scorers
- AnswerRefusalScorer
Detects whether AI refuses unsafe or disallowed tasks appropriately. - ContentModerationScorer
Flags potentially harmful, explicit, or policy-violating content. - ContentSafetyViolationScorer
Scores violations of safety, compliance, or governance guidelines. - ToxicityScorer
Evaluates presence of insulting, aggressive, or harmful language. - IsHarmfulAdviceScorer
Detects dangerous recommendations — crucial for sensitive domains. - NoGenderBiasScorer
Ensures fair, unbiased responses related to gender. - NoRacialBiasScorer
Evaluates outputs for racial bias or stereotyping. - NoAgeBiasScorer
Checks for age-related prejudice in AI responses. - CulturalSensitivityScorer
Ensures the AI respects cultural norms and avoids offensive phrasing.
6. Structured Output & Format Scorers
- IsJSONScorer
Validates whether responses follow correct JSON formatting. - IsEmailScorer
Checks for valid email generation structure. - ValidLinksScorer
Ensures URLs in the output are properly formatted and valid.
7. Multi-Judge & Deep Reasoning Scorers
- PanelOfJudgesScorer
Uses multiple LLM 'judges' to provide averaged evaluations for robust scoring. - SpecializedPanelScorer
Domain-specific multi-judge scorer for technical or expert evaluations. - GEvalScorer
A generalized evaluation approach measuring reasoning, structure, and coherence. - AgentScorers
Bundle of agent-specific evaluations optimized for multi-step AI workflows.
8. Pipeline & System-Level Scorers
- QueryProcessingEvaluator
Evaluates how accurately the system interprets and processes user queries. - RetrievalStageEvaluator
Scores the retrieval portion of a RAG pipeline. - RerankingEvaluator
Evaluates how well documents are reranked for relevance. - RAGPipelineEvaluator
Full end-to-end scoring for retrieval + ranking + generation. - PipelineCoordinationScorer
Evaluates coordination of pipeline components across stages.
9. Latency, Resource & Error Scorers
- LatencyAnalysisScorer
Measures latency and identifies bottlenecks across the AI execution path. - ResourceUtilizationScorer
Assesses token efficiency, compute usage, and cost impact. - ErrorPropagationScorer
Detects how errors propagate across multi-step agent workflows.
10. Knowledge & Memory Scorers
- KnowledgeRetentionScorer
Measures whether an AI preserves key information across turns or steps.
Noveum's Auto-Fixing Engine
After evaluating agents using all scorers above, Noveum automatically analyzes failures and runs improvement experiments such as:
- prompt optimization
- model selection for better cost-performance
- parameter tuning
- workflow adjustments
- tool-call corrections
The system then provides a full recommendation report or can automatically generate pull requests to update your agent logic.
AI Cost Optimization With Noveum
Noveum identifies token-heavy prompts, inefficient retrieval patterns, expensive models, and redundant workflow steps. It analyzes cost per generation step and suggests cheaper, higher-performing alternatives.
Companies typically see 20-50% reduction in AI operational cost after implementing Noveum's recommendations.
Noveum.ai — The Future of AI Agent Reliability
As companies scale AI Agents across functions, Noveum provides the necessary foundation to ensure accuracy, reliability, compliance, performance, and cost efficiency. We provide the tooling needed to maintain AI Agents, prevent drift, and keep behavior trustworthy — automatically.