User analytics for conversational AI agents means knowing what people ask, whether they got an answer, and where they gave up. Here's what most teams are missing.

Nebuly's 8B model removes PII from conversation logs on a single self-hosted GPU, matching a frontier model's leak-free rate at 8 to 20 times lower cost.

ROI from an AI agent isn't about which vendor you pick. Here are the three things that actually determine returns: time saved, revenue influenced, and AI proficiency.

How to measure AI proficiency without surveys: score it from the conversations themselves, by department and role, and track what training moves.

Model selection is a resource-allocation decision made thousands of times a month. Task-to-model fit is how you match a model's cost and capability to what each task actually requires, and route the rest to a cheaper model that still clears the quality bar.

Nebuly is the user analytics platform for AI agents. It reads the conversations between users and your AI agents at scale and connects each interaction to a business outcome: time saved, revenue influenced, and how proficiently teams use the AI. LLM observability tools like Langfuse, Arize, and LangSmith measure whether the system is working: traces, latency, token cost, errors, and evaluations. Nebuly measures whether that working system is delivering value to the people using it. Most enterprises running AI agents in production keep both.