Eval360™ is a purpose-built SLM that evaluates and debugs agentic AI workflows at an atomic level to catch failures before they reach production.
Your CRO ignores benchmark scores. LLUMO evaluates financial AI against 50+ metrics that actually matter, accuracy, compliance, precision.
Eval360™ works as your personal evaluator, higher accuracy, lower cost, so reliability never becomes a budget problem.
Pipeline breaks used to mean days of log-digging. LLUMO traces every agent handoff and surfaces root cause in minutes.

Trace every multi-agent step, data ingestion to decision to customer communication, in one view. Know exactly why it failed.

Run the same financial task across multiple LLMs simultaneously. Get instant scores across accuracy, safety, and compliance, and ship only what clears the bar.


Automatically detect when AI outputs expose sensitive financial data across multi-agent pipelines. Prevent incidents before they're reported, not after.

Catch agents exceeding their defined role, support bots initiating transactions, analysis agents accessing unauthorized data. Instantly.


Real-time AI health, failure rates, policy violations, and evaluation trends, built for CROs, CISOs, and compliance leads. No engineering needed.
Only authorized personnel access evaluation data and logs. Fully configurable to your compliance framework and hierarchy.
SOC 2 Type 2 certified. Zero sensitive data stored. On-premise and private cloud available for strict data residency needs.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Before LLUMO, debugging meant replaying the entire workflow manually. With the SDK hooked in, we see real-time insights without changing how we build.
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.
Our pipelines were growing complex fast. LLUMO brought clarity, reduced hallucinations, and sped up our inference, making our workflows feel rock solid.
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.
Evaluating models used to be a guessing game. LLUMO’s EvalLM made it clear and structured, helping us improve models confidently without hidden surprises.
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.
Before LLUMO, debugging meant replaying the entire workflow manually. With the SDK hooked in, we see real-time insights without changing how we build.
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.
Our pipelines were growing complex fast. LLUMO brought clarity, reduced hallucinations, and sped up our inference, making our workflows feel rock solid.
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.
Evaluating models used to be a guessing game. LLUMO’s EvalLM made it clear and structured, helping us improve models confidently without hidden surprises.
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.
Before LLUMO, debugging meant replaying the entire workflow manually. With the SDK hooked in, we see real-time insights without changing how we build.
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.
Our pipelines were growing complex fast. LLUMO brought clarity, reduced hallucinations, and sped up our inference, making our workflows feel rock solid.
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.
Evaluating models used to be a guessing game. LLUMO’s EvalLM made it clear and structured, helping us improve models confidently without hidden surprises.