Trusted by many, across their companies and within their products
LLUMO AI is powered by Eval360™
Eval360™ is a purpose-built SLM that evaluates and debugs agentic AI workflows at an atomic level to catch failures before they reach production.
LLUMO AI solutions
Why Observe?
360°
Workflow Visibility
Trace the full agent execution graph from Query to Tool Selector to response and pinpoint exactly where failures occur.
50+ KPIs
Signals to Observe
Monitor real-time reliability signals, hallucinations, incorrect outputs, tool instability, retrieval quality, latency, cost, and behavioral drifts.
1 click
Insights & Alerts
Get instant alerts on workflow degradation and trace failures across prompts, agent decisions, MCP tools, and retrieval layers.
The one solution for Production LLM Applications
Seamlessly integrate and enhance LLMs performance, irrespective of language models or RAG setup.
Complete Workflow Monitoring
- Select from multiple projects and observe workflow health individually.
- Track performance per environment without losing production context.
Overall Workflow Health Score
Instantly understand the current reliability state of your entire workflow through a single consolidated health indicator.
Trend Tracking Over Time
Identify whether system behavior is improving, degrading, or remaining stable by analyzing performance movement across selected time ranges.
Production Metrics & Failure.
- Automatically surface patterns across repeated failures.
- See what’s breaking most often and prioritize fixes effectively.
Core Production Metrics
Continuously measure real-time execution signals like latency, cost, tool stability, hallucinations, output accuracy, and retrieval quality to understand true production performance.
Pattern-Based Failure Clustering
Automatically organize recurring issues by failure type and workflow stage so you can prioritize the most impactful problems first.
Actionable Production Intelligence
- Built-In Failure Explanations: Understand why instability occurs, not just that it occurred.
- Contextual RCA: Get clarity on hallucinations, latency spikes, tool misfires, and output errors.
Seamless MCP Integration
Connect observe with MCP servers, tools, and production systems easily. Monitor agent workflows across environments for stable and consistent execution.
Instant Alerts on Instability
Receive real-time notifications the moment workflow performance degrades, enabling your team to quickly investigate and resolve issues before they escalate.
Proactive Issue Identification
Detect emerging risks and recurring instability early, allowing you to address potential failures before they affect users, business operations, or production environments.
Wall of love
Testimonials
Don't just take our word for it - see what actual users of our service have to say about their experience.
Nida
We used to spend hours digging through logs to trace where the agent went wrong. With the debugger, the flow diagram shows errors instantly, along with reasons and next steps.
Jazz Prado
Hallucinations in our customer support summaries were slipping through unnoticed. LLUMO’s debugger flagged them in real time, helping us prevent misinformation before it reached clients.
Shikhar Verma
Managing multi-agent workflows was messy, too many moving parts, too many blind spots. The debugger finally gave us clarity on what happened, why, and how to fix it.
Jordan M.
LLUMO felt like a flashlight in the dark. We cleared out hallucinations, boosted speeds, and can trust our pipelines again. It’s exactly what we needed for reliable AI.
Sarah K.
With LLUMO, we tested prompts, fixed hallucinations, and launched weeks early. It seriously leveled up our assistant’s reliability and gave us confidence in going live.
Nida
We used to spend hours digging through logs to trace where the agent went wrong. With the debugger, the flow diagram shows errors instantly, along with reasons and next steps.
Jazz Prado
Hallucinations in our customer support summaries were slipping through unnoticed. LLUMO’s debugger flagged them in real time, helping us prevent misinformation before it reached clients.
Shikhar Verma
Managing multi-agent workflows was messy, too many moving parts, too many blind spots. The debugger finally gave us clarity on what happened, why, and how to fix it.
Jordan M.
LLUMO felt like a flashlight in the dark. We cleared out hallucinations, boosted speeds, and can trust our pipelines again. It’s exactly what we needed for reliable AI.
Sarah K.
With LLUMO, we tested prompts, fixed hallucinations, and launched weeks early. It seriously leveled up our assistant’s reliability and gave us confidence in going live.
Nida
We used to spend hours digging through logs to trace where the agent went wrong. With the debugger, the flow diagram shows errors instantly, along with reasons and next steps.
Jazz Prado
Hallucinations in our customer support summaries were slipping through unnoticed. LLUMO’s debugger flagged them in real time, helping us prevent misinformation before it reached clients.
Shikhar Verma
Managing multi-agent workflows was messy, too many moving parts, too many blind spots. The debugger finally gave us clarity on what happened, why, and how to fix it.
Jordan M.
LLUMO felt like a flashlight in the dark. We cleared out hallucinations, boosted speeds, and can trust our pipelines again. It’s exactly what we needed for reliable AI.
Sarah K.
With LLUMO, we tested prompts, fixed hallucinations, and launched weeks early. It seriously leveled up our assistant’s reliability and gave us confidence in going live.
Mike L.
Integration was surprisingly quick, took less than 30 minutes. Now every agent run automatically and logs into the debugger, so we catch failures before they cascade.
Ryan
Before LLUMO, debugging meant replaying the entire workflow manually. With the SDK hooked in, we see real-time insights without changing how we build.
Sonia
Before LLUMO, we were stuck waiting on test cycles. Now, we can go from an idea to a working feature in a day. It’s been a huge boost for our AI product.
Amit Pathak
Our pipelines were growing complex fast. LLUMO brought clarity, reduced hallucinations, and sped up our inference, making our workflows feel rock solid.
Michael S.
I wasn’t sure if LLUMO would fit, but it clicked immediately. Debugging and evaluation became straightforward, and now it’s a key part of our stack.
Priya Rathore
Evaluating models used to be a guessing game. LLUMO’s EvalLM made it clear and structured, helping us improve models confidently without hidden surprises.
Mike L.
Integration was surprisingly quick, took less than 30 minutes. Now every agent run automatically and logs into the debugger, so we catch failures before they cascade.
Ryan
Before LLUMO, debugging meant replaying the entire workflow manually. With the SDK hooked in, we see real-time insights without changing how we build.
Sonia
Before LLUMO, we were stuck waiting on test cycles. Now, we can go from an idea to a working feature in a day. It’s been a huge boost for our AI product.
Amit Pathak
Our pipelines were growing complex fast. LLUMO brought clarity, reduced hallucinations, and sped up our inference, making our workflows feel rock solid.
Michael S.
I wasn’t sure if LLUMO would fit, but it clicked immediately. Debugging and evaluation became straightforward, and now it’s a key part of our stack.
Priya Rathore
Evaluating models used to be a guessing game. LLUMO’s EvalLM made it clear and structured, helping us improve models confidently without hidden surprises.
Mike L.
Integration was surprisingly quick, took less than 30 minutes. Now every agent run automatically and logs into the debugger, so we catch failures before they cascade.
Ryan
Before LLUMO, debugging meant replaying the entire workflow manually. With the SDK hooked in, we see real-time insights without changing how we build.
Sonia
Before LLUMO, we were stuck waiting on test cycles. Now, we can go from an idea to a working feature in a day. It’s been a huge boost for our AI product.
Amit Pathak
Our pipelines were growing complex fast. LLUMO brought clarity, reduced hallucinations, and sped up our inference, making our workflows feel rock solid.
Michael S.
I wasn’t sure if LLUMO would fit, but it clicked immediately. Debugging and evaluation became straightforward, and now it’s a key part of our stack.
Priya Rathore
Evaluating models used to be a guessing game. LLUMO’s EvalLM made it clear and structured, helping us improve models confidently without hidden surprises.
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FAQs
01 Can I try LLUMO AI for free?
02 Is LLUMO AI secure?
03 What models does LLUMO AI support?
04 Is LLUMO compatible with all LLMs and RAG frameworks?
05 Can I use LLUMO with custom-hosted LLMs?