Replay the complete path from prompt to outcome. Whyrail connects the evidence that ordinary logs leave scattered.
Whyrail shows the path from user action to retrieval, tool calls, API responses, quality signals, and the fix your team ships next.
Replay suggested a source freshness check before final answer generation.
Designed for teams shipping intelligent features into real product workflows
Prompt, retrieval, tools, APIs, and user actions stay connected.
Quality signals sit next to the trace, not in a separate spreadsheet.
Teams can inspect the failing path before context disappears.
Built for evidence, oversight, and data-conscious product teams.
A bad answer might start with a prompt change, weak retrieval, a stale API response, a tool loop, or a user action the system did not understand. Traditional logs show fragments. Whyrail connects the path.
Whyrail turns production AI behavior into traces your team can inspect, scores your team can trust, and replayable failures your team can fix.
Keep prompts, retrieval, tool calls, API responses, quality signals, and the next debugging decision in one readable workflow record.
Explore developer resourcesPrompt, user action, and session evidence.
Retrieval, tools, and APIs in sequence.
The evidence a reviewer needs to decide next.
A trace can show what happened. Whyrail goes further by connecting each step to quality signals, user outcomes, and debugging actions.
Track quality, cost, latency, adoption signals, and user outcomes so AI features can be managed like real product surfaces.
Make roadmap decisions with production evidence.View solutionWhyrail shows what the user asked, what the system retrieved, which tools ran, how the answer scored, and what changed after debugging.
Testimonials from product, platform, and support leaders working through production reliability issues.
"Whyrail gives our product and engineering teams the same evidence. We can go from a customer complaint to the exact retrieval and tool path in minutes."
Marta LewandowskaVP Product, Northstar SaaS"The replay view changed how we debug agents. We are no longer guessing whether the issue was prompt, retrieval, or tool behavior."
Jonas KellerAI Platform Lead, OrbitOps"Support finally has a way to explain why an AI answer failed. That context shortens escalations and helps us fix the underlying workflow."
Elena RossiHead of Support Operations, LumaDeskFor early teams shipping their first production AI workflows.
50k trace spans Quality scorecards 7-day retention Email supportChoose LaunchFor growing SaaS and platform teams standardizing observability.
500k trace spans Replay debugging Review queues 30-day retentionChoose ScaleFor governance, advanced controls, SSO, retention, and support.
Custom volume SSO and roles EU controls Dedicated supportChoose EnterpriseBuilt for teams that care about data control, workflow evidence, and practical oversight.
Preserve the path across prompts, retrieval, tools, APIs, and outputs.
Give reviewers the details needed to intervene, escalate, or approve a fix.
Support access, retention, and monitoring patterns serious AI teams expect.
Whyrail tracks AI workflow events across prompts, retrieval, model calls, tools, APIs, user actions, outputs, quality scores, and debugging sessions.
No. Whyrail is designed for AI workflows, including RAG systems, agents, AI support assistants, copilots, and platform-level AI features.
Logs captures what happened, Quality measures how well it worked, and Debug helps your team replay and fix the failure path.
Yes. Whyrail should provide support-friendly views that connect AI behavior to tickets, escalations, failed deflections, and customer outcomes.
Mar 12, 2026
Whyrail is funded by Gama VC as it builds AI observability for complex operating environments.
Trace, evaluate, and debug the workflows behind your production experiences.
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