What is AI observability?
AI observability is the ability to see what your AI is actually doing in production — every prompt, token, tool call, latency, cost and outcome — and turn that telemetry into dashboards anyone can read. Without it, you're running AI blind; with it, you can explain and improve every result.
What is LLM observability and how is it different from monitoring?
Monitoring tells you something broke; observability tells you why. LLM observability captures the full trace of a model interaction — inputs, retrieved context, reasoning steps, tool calls and final output — so you can debug accuracy issues, replay any conversation, and catch drift before users do.
What is AI governance and why does it matter for enterprises?
AI governance is the set of rules and controls that decide what your AI is allowed to say and do, who approves sensitive actions, and how it's all recorded. For enterprises it's critical: it's how you manage risk, meet compliance obligations, and prove to auditors and customers that your AI is safe and accountable.
What is AI telemetry?
Telemetry is the raw signal Snooz collects from every AI interaction — cost, latency, token usage, accuracy scores, tool calls and outcomes. It's the data foundation that makes observability and governance possible, captured automatically from the first request.
How does observability reduce AI costs?
A large share of AI spend is wasted on retries, oversized prompts and dead conversations that no one is watching. By tracking cost per outcome down to the token and flagging waste automatically, Snooz typically helps teams cut spend significantly while improving accuracy.
What does an AI governance framework include?
Typically: policy guardrails (topic limits, PII redaction, tone and safety rules), approval gates for high-value or risky actions, and immutable, audit-ready logs of every decision. Snooz lets you set these rules once and enforces them automatically across every bot and agent.
Does this help with compliance and regulations like GDPR?
Yes. With PII redaction, enforced policies, and a complete exportable audit trail of every action, Snooz gives you the evidence and controls needed to support data-protection requirements such as GDPR and CCPA. Always pair it with proper implementation and review for your specific obligations.
How is observability different from regular application monitoring?
Traditional monitoring watches infrastructure — uptime, errors, CPU. AI observability watches the behavior of the model itself: what it was asked, what context it used, how it reasoned, what it answered, and whether that answer was right. It's purpose-built for the things that go wrong with AI specifically.
What is model drift, and how does Snooz catch it?
Drift is when an AI's accuracy quietly degrades over time as real-world inputs change. Because Snooz scores accuracy continuously on live traffic, it alerts you the moment quality starts slipping — instead of you finding out from an angry customer months later.
Can we set our own rules for what the AI is allowed to do?
Yes — that's the core of governance. You define the policies once: allowed topics, what data is redacted, which actions need human approval, and spending limits. Snooz enforces them automatically across every bot and agent, and logs every enforcement for audit.