Product

One operating layer for production AI.

Refario connects observability, cost intelligence, guardrails, and operational analytics for AI runs, agents, tools, and workflows.

Live run trace · support-resolution
healthy
Agent
router
120ms
Model
gpt-5.3
2.9k tok
Tool
mcp.notion
228ms
Guardrail
sensitive-data
pass
Response
status
success
Cost / run
€0.04
+4.9%
P95 latency
1.92s
-180ms
Policy coverage
6 rules
100%
Primary use
Run visibility
Trace agents, models, tools, and policies together.
Primary insight
Cost context
Understand spend by workflow, provider, and model.
Primary action
Operational control
Guardrails, alerts, reports, and integrations stay connected.
Operating narrative

The product is organized around how teams actually run AI.

Refario is not a log sink and it is not just a dashboard. It is a runtime operating view that starts with signal, moves into diagnosis, and ends in control.

From alert to answer
Move from anomaly to exact runtime cause without losing context.
01
Observe the signal
Overview, Engineering, and Finance show the KPI shift, incident, or spend anomaly that needs attention.
02
Inspect the path
Runs, workflows, model calls, and tools expose the exact span, retry, fallback, or guardrail event that changed behavior.
03
Operate the response
Guardrails, reports, integrations, and budgets live in the same surface so teams act from shared evidence.
Observe
Observe production AI with context.

Use shared dashboards to see reliability, latency, anomalies, and cost shifts without splitting engineering and finance views.

Investigate
Investigate production AI with context.

Trace one workflow from router to model to tool call to policy check with the full execution still intact.

Operate
Operate production AI with context.

Turn operational insight into rollout control with policy coverage, alerts, scheduled reporting, and integration confidence.

Product surfaces

Every major operational surface is already in the product.

The same product layer can answer failure analysis, workflow health, provider cost attribution, MCP tool reliability, and policy review.

Runs and traces
Runs and traces

Inspect every run and span with timing, status, model, tool, and policy context.

  • Run status and error codes
  • Span-level latency and retries
  • Model + tool execution timeline
Cost intelligence
Cost intelligence

Connect tokens and spend directly to workflows, providers, models, and anomalies.

  • Token analytics
  • Cost per run and workflow
  • Provider and model attribution
Guardrails
Guardrails

Monitor rule coverage, trigger rates, and violations in the same runtime layer as the execution path.

  • Policy coverage
  • Violation logs
  • Rule performance over time
MCP tool observability
MCP tool observability

Track tool reliability, transport health, and failure concentration for MCP and internal systems, with alerts when new tools appear or reliability drops.

  • Tool call success and latency
  • Transport-level diagnostics
  • New tool detected + reliability-drop alerts
Workflows and agents
Workflows and agents

See how routing, models, and tools affect production outcomes across multi-step agent systems.

  • Workflow-level trends
  • Failure clustering
  • Release-over-release comparisons
Dashboards and reporting
Dashboards and reporting

Share engineering, finance, and operational context through role-aware dashboards and scheduled reports.

  • Engineering dashboards
  • Finance dashboards
  • Scheduled email and webhook reports
Decisions supported

The product is built to answer operational questions quickly.

Refario is valuable because it shortens the time between a signal, the real runtime explanation, and a decision the team can trust.

OpenAIAnthropicLangChainMCP ToolsInternal APIsCustom SDKsSlackNotion
Reliability review
Which routing change caused the latency spike in refund-policy?
Runs, workflows, and span traces show the exact branch and retry path.
Budget review
Which provider fallback is increasing cost per run this week?
Finance views tie model and provider mix back to workflow behavior and anomalies.
Rollout review
Are guardrails catching the regression before users feel it?
Coverage, trigger rate, and incident context stay visible in the same surface as the run timeline.
Ready to operate production AI

Get end-to-end visibility across runs, spend, and guardrails.

Start free to connect your first project, then book a demo for rollout planning with your stack.