COMPARE
Observability tools measure the model for developers. Nebuly measures the value of the AI for the business, and can read the data those tools already collect.
Book a demo
LLM observability tools like Langfuse, Arize, and LangSmith help developers see whether a model is working: traces, tokens, latency, evaluation scores. Nebuly works one layer up. It analyzes 100% of the conversations with your AI agents and copilots and measures whether the AI is delivering value to the business, time saved, revenue influenced, and AI proficiency, plus risk. Observability answers "is the model behaving." Nebuly answers "is the AI worth it." The two are complementary, and Nebuly can ingest observability data as one of its sources.
TWO DIFFERENT LAYERS
Measuring enterprise AI splits into two layers
The first question is technical: is the model working. That is the observability layer, traces, spans, tokens, latency, evaluations, built for developers and ML teams. Langfuse, Arize, and LangSmith live here.
The second is about outcomes: is the AI delivering value. Model telemetry cannot answer that. Whether a task succeeded, how much time it saved, whether it influenced revenue, whether it created risk, those live in the conversation. That is Nebuly's layer.
What Nebuly does
Nebuly reads the conversations between users and your AI agents and copilots and turns every interaction into structured evidence of value.
It ingests conversations through a vendor-agnostic Interaction API and from the systems enterprises already run, including Microsoft 365 Copilot, ChatGPT Enterprise, Claude, Gemini Enterprise, and Langfuse. From each conversation it extracts intent, topics, sentiment, emotion, implicit and explicit feedback, failures such as hallucinations and unhandled requests, and business risks like PII exposure or policy violations. Those signals roll up into the three measures leadership tracks:
Time saved — task completion valued against your own benchmarks for the equivalent manual work.
Revenue influenced — commercial intent and retention risk surfaced from customer conversations.
AI proficiency — how effectively teams and regions actually use AI, and where enablement is needed.
Feedback in most AI products covers fewer than 1% of interactions and skews to the very happy and the very frustrated. Nebuly analyzes all of them, so the picture reflects reality rather than a self-selected sample.
Where observability ends, Nebuly begins
Observability and Nebuly are not competitors. You keep observability to keep models healthy. You add Nebuly to prove and improve the value that healthy model produces.
Nebuly can even take observability data as an input, for example by ingesting Langfuse traces, and turn it into business-level analysis. One tells your engineers the model is running. The other tells leadership what it returned.
Nebuly is used by Global 2000 and Fortune 500 organizations across financial services, insurance, telecom, industrial and automotive, healthcare, and retail. It is built for how large enterprises run AI:
Privacy-first and self-hosted. A self-hosted deployment runs all analysis inside your own environment, so conversation data never leaves your perimeter. PII is stripped before analysis.
Enterprise-grade security and compliance. SOC 2, ISO 27001, and ISO 42001.
Vendor-agnostic. One Interaction API ingests conversations from any agent, copilot, or assistant, internal or customer-facing.
Governed visibility. Analysis is broken down by department and geography, with controls from full analytics to aggregate-only or opt out.
Is Nebuly an observability tool?
No. Observability tools measure whether a model is working, for developers. Nebuly measures whether the AI is delivering business value, for the organization, by analyzing the conversations. They operate at different layers.
Do I still need observability if I use Nebuly?
Usually yes. Observability keeps your models and applications healthy at the technical layer. Nebuly measures the business value of what those applications produce. Most enterprises run both.
What does Nebuly measure that observability tools don't?
Whether tasks succeeded for users, time saved, revenue influenced, AI proficiency, user sentiment, failures, and compliance risk, all drawn from the conversation rather than from model telemetry.
Can Nebuly use data from my observability stack?
Yes. Nebuly ingests conversations through a vendor-agnostic Interaction API and integrates with sources like Langfuse, so it can build on data you already collect.
