Refario
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Solutions

Different teams. One runtime source of truth.

Engineering traces failures, product reviews behavior, finance understands spend, and operations manages policy coverage from the same production evidence.

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Shared incident context
refund-policy assistant latency degradation

One workflow regression becomes a reliability, release, cost, and guardrail question at the same time.

investigating
Engineering
Where did it fail?
Trace shows the retrieval tool retry path and the added latency.
Product
What changed?
Workflow version 24.3 increased retries after a prompt-router update.
Finance
What did it cost?
Fallback to a higher-cost model lifted spend per run by 18 percent.
Operations
Was policy coverage intact?
Violation rate stayed stable, but alerting triggered after the latency threshold.
Shared operating model

One production event can answer four different questions at once.

Refario keeps the run, trace, cost, and guardrail context intact so teams stop passing screenshots, guesses, and partial explanations between functions.

From signal to decision
The same runtime evidence can drive engineering investigation, release review, budget analysis, and incident response.
01
Start with a shared signal
A degraded workflow, cost spike, or policy incident appears once, with the same execution context visible to every team.
02
Different teams ask different questions
Engineering looks for the failed span, product checks the release effect, finance reviews provider mix, and ops confirms guardrail behavior.
03
Move to a coordinated response
Because Refario keeps the path intact, teams can make rollout, budget, and reliability decisions without breaking context.
Engineering
Which span introduced the latency regression?
Run and trace views isolate the exact tool retry, provider fallback, or failed step.
Product
Did the release improve output quality or just increase retries?
Workflow trends and release comparisons connect runtime behavior to feature outcomes.
Finance
Which routing path is driving cost per run up this week?
Provider, model, and workflow attribution show where spend changed and why.
Operations
Are guardrails catching the regression before customers feel it?
Policy coverage, incident history, and violation detail stay attached to the same execution path.
By responsibility

Purpose-built for the teams accountable for production AI.

Each surface is tuned to a real operating responsibility, but the evidence stays shared across the organization.

AI engineering
Debug failures without stitching providers, traces, and tools together manually.

Investigate runs with model, span, and MCP tool context intact so incidents close faster and regressions are easier to explain.

  • Inspect span timing, retries, and error paths
  • Trace models and tools in one execution graph
  • Compare workflow health before and after releases
Shorten time from incident to root cause.
Product and feature owners
Review AI behavior with production evidence instead of anecdotal feedback.

Connect launch decisions to real workflow outcomes, latency shifts, and incident patterns so roadmap tradeoffs are grounded in what shipped.

  • Measure workflow success by release and environment
  • Track behavioral changes after routing updates
  • Understand where degraded quality affects users
Make rollout decisions with runtime proof.
Finance and cost owners
Understand where AI spend grows before budgets drift out of control.

Refario ties token usage and provider mix back to the workflows, customers, and fallback paths creating spend.

  • Attribute cost by provider, model, and workflow
  • Detect spend spikes linked to runtime behavior
  • Share scheduled reports with accountable owners
Turn spend review into an operational practice.
Platform, ops, and MCP systems
Operate tools, policies, and workflow infrastructure from one command surface.

Monitor MCP transports, guardrail performance, and workflow reliability without losing the connection back to the affected run.

  • Track tool reliability and transport health
  • Alert on newly detected tools and reliability drops
  • Review policy coverage and violation rate
  • See incident context across workflows and environments
Keep operational controls tied to real runtime behavior.
Operating cadences

Use the same product in the reviews that matter.

The value is not just observability. It is that the same operational context shows up in incident review, launch review, and cost review.

Incident review
Incident review stays grounded in the runtime path.

Move from alert to exact span, failing tool call, and policy context without switching systems.

  • Trace the affected run path
  • Confirm customer impact by workflow
  • Coordinate fixes with shared evidence
Launch review
Launch review stays grounded in the runtime path.

Compare release behavior with workflow reliability, latency, and violation changes before broad rollout.

  • Review release-over-release workflow health
  • Catch regressions before expansion
  • Decide with engineering and product aligned
Cost review
Cost review stays grounded in the runtime path.

Connect weekly spend changes to provider routing, fallback behavior, and workflow demand in the same discussion.

  • See provider and model cost mix
  • Spot expensive fallback patterns
  • Share finance-ready reporting without losing runtime detail
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.

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