Node-level truth about every workflow you run.
A workflow is only as good as its worst node. Workflow Intelligence breaks every pipeline into per-node statistics — latency percentiles, failure contribution, cost share, retry behavior — so optimization targets the actual bottleneck instead of the usual suspect.
The problems that pile up quietly.
A pipeline is "slow" but nobody knows which of its fourteen nodes is responsible.
One node contributes 80% of failures and 60% of cost, invisibly.
Optimization effort goes to the node someone guessed, not the node the data indicts.
What changes for your team.
Bottlenecks named, not guessed
Node-level latency percentiles show precisely where time goes. The "slow AI" turns out to be one HTTP node with a 9-second p95 — fixed in an afternoon.
Failure hotspots isolated
Each node’s failure contribution is tracked over time, so hardening effort concentrates where breaks actually originate.
Cost hot paths exposed
See which nodes consume the token budget. Often one enrichment step costs more than the rest of the workflow combined.
Live in minutes, not sprints.
Decompose the workflow
Auto-discovery maps every node, branch, and dependency in each workflow.
Measure per node
Every execution contributes per-node timing, outcome, and cost data, aggregated into live statistics.
Surface the outliers
Nodes that dominate latency, failures, or spend are flagged with their trend — and feed straight into the Recommendations feed.
Questions
Obsivara collects p50, p95, and p99 latency, failure rate and failure contribution, token consumption and cost per call, and retry frequency — all aggregated per node and updated with every execution.
Yes. Obsivara auto-discovers and maps every node in connected LangChain chains, n8n workflows, and other frameworks, providing per-node statistics for each step in the pipeline.
Workflow Intelligence surfaces the nodes that dominate latency, failures, or cost as outliers, and feeds those findings directly into the Recommendations feed with the specific change to make — so prioritization is data-driven rather than guesswork.
See it on your own AI stack.
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