We can compress prompts, which helps save on tokens, making interactions more cost-effective, and reducing your LLM bills by up to 80% while making your LLM perform better.
Compressed prompts combined with effective caching can streamline processing and reduce latency, meaning the model can generate responses faster.
A more concise prompt can focus on essential details, reducing the chance for the model to hallucinate or overthink the prompt.
Scale your AI without breaking the bank. With our cost optimization techniques, you’ll use the same prompt and model—and get the same output—but at a significantly lower cost.
We combine effective token compression with intelligent model routing and smart caching to cut costs, reduce hallucinations, and speed up response times.
We compress prompts to their essential components, prompt compression reduces ambiguity, resulting in more consistent and accurate responses for your queries.
RAG compression helps save AI costs by using fewer tokens and speeding up responses. It makes sure only the important data gets processed, making AI more affordable and efficient
Eliminate guesswork with real-time cost and performance monitoring to pinpoint which model work, which doesn’t, and how much it costs you. Use data-driven insights to make your LLMs more effective, faster, and cost-efficient.
We go beyond monitoring—our insights come with specific, actionable recommendations on how to refine your prompts, model, or workflow to keep your LLMs consistently performing at the least cost.
It takes 5 minutes to easily integrate our API to smartly compress your prompt, save on your LLM cost, and boost your performance. Make everything effortless with a simple API integration.
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.