Private Inference API
AI Studio

Make every AI decision traceable

Nebul AI Observer gives you end-to-end visibility into LLMs, RAG pipelines, and agents — so you can prove compliance (e.g., EU AI Act), improve reliability, and understand exactly why a model produced an answer.

01

No transparency across organization

As AI adoption grows, teams lose track of who is using AI, where it’s deployed, and what data is being processed. That makes governance and accountability nearly impossible.

02

Debugging is slow and expensive

LLM systems are probabilistic and multi-component. When users report wrong answers, hallucinations, or latency spikes, traditional monitoring can’t explain the chain of events.

03

RAG and agent workflows lack trust

Retrieval-augmented generation and tool-using agents introduce new failure modes: bad retrieval, missing context, tool errors, and inconsistent reasoning. Without traces, you can’t improve it.

04

Compliance risk increases over time

Regulations and internal policies require transparency, auditability, and documentation. Without observability, you can’t demonstrate that your AI systems behave responsibly.

Full-stack AI
observability

AI Observer captures telemetry across the entire AI stack, from user interaction to retrieval, tool usage, model execution, and output, keeping systems measurable and explainable in production.

Compliance
by design

AI Observer tracks AI usage, data inputs, and decision chains to support governance requirements such as transparency and documentation, and to enable audit-ready reporting.

Reliability you
can prove

By tracing every LLM call and the data that influenced it, AI Observer helps teams understand why a model behaved the way it did, explain its outputs, and improve reliability over time.
01

Organization-wide usage tracking

Build a clear picture of AI adoption and risk across teams and products by tracking AI usage across applications, environments, and teams. Usage can be attributed to specific services, model versions, and users where applicable, while trends in cost, latency, and quality signals are continuously monitored. This makes it possible to detect shadow usage and unexpected integration patterns early.

02

End-to-end tracing for LLM + non-LLM calls

Understand exactly what happened in every request, even across complex pipelines. Prompt-to-model-to-output flows are traced with detailed timing and metadata, including retries, fallbacks, and orchestration decisions. Tool calls, API calls, embedding generation, and retrieval are linked into a single timeline, while multi-turn sessions and conversational context are fully captured.

03

RAG & agent transparency

Make retrieval and agent behavior explainable rather than opaque. The system traces which data was retrieved, from where, and with which relevance scores, including integration with Private Inference API and AI Data Retrieval. Document snippets and metadata that influenced the response are stored, while agent steps such as tool usage, memory, intermediate decisions, and outcomes are tracked. This makes it possible to identify failures like missing context, poor retrieval, or tool errors.

04

Quality signals & improvement

Turn production behavior into measurable and improvable outcomes by defining clear quality indicators such as grounding, relevance, refusal rates, and user feedback. Drift, regressions, and changes across model and prompt versions are tracked over time. Evaluations can be run using custom metrics, LLM-based judges, and human review, enabling the creation of quality gates for releases from development through staging to production.

Compliance
& transparency

Support EU AI Act-style transparency expectations with traceability, documentation, and audit-ready reporting.

Faster
debugging

Pinpoint whether issues came from prompt construction, retrieval quality, model behavior, or tool execution.

Reliable AI
experiences

Improve answer quality by tracking what influenced model response and validating outputs against measurable signals.

Cost & performance control

Attribute token usage, latency, and spend to models, endpoints, teams, and features — and spot cost overruns early.

Agent-ready
observability

Track multi-step workflows and tool usage to safely scale agentic systems across production environments.

AI Observer

Transparency isn’t optional anymore

As AI becomes embedded in business-critical workflows, organizations need the ability to answer: What data was used? Which model responded? What tools were called? Why did it produce this output? AI Observer delivers this transparency by default — enabling better trust, governance, and customer confidence.

Fully managed
by Nebul

With secure storage, role-based access controls, and scalable ingestion — designed for high-volume enterprise AI workloads.

Composable
& integrable

Works with Nebul Agents, Chat, and Guardrails — or integrates into existing AI stacks.

API-first &
automation-ready

Export traces and metrics into your internal monitoring, security, and BI systems for centralized analysis and reporting.

Always
up-to-date

Built for evolving model providers, orchestration frameworks, and agent patterns.

Enterprise-grade
SLA

Operational guarantees and full observability for mission-critical AI applications running in production.

Deploy AI and get results without the risks

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