Skip to main content
Once the browser SDK and server library are wired in, DataFramer does the collecting and stitching for you. This page explains what happens behind the scenes: there’s nothing you need to build here.

​
Collect

Two independent streams land on DataFramer:
  • Signals: track/identify/group events from the browser SDK, ingested through /api/signals/*.
  • Traces: your AI calls, either pulled from a connected observability tool (Langfuse or LangSmith today), or auto-tagged as they’re created if you’re on Langfuse v2 and using dataframer-journey.
Both carry the same journey_id, generated by the browser SDK and propagated by the server library.

​
Correlate

DataFramer matches signals to traces on two keys:
  1. journey_id: the primary match. Any signal and any trace sharing a journey id are grouped into the same journey timeline.
  2. Direct datapoint id: some signals (like a reviewer’s edit) point at one exact trace explicitly, independent of journey id. This is resolved immediately at ingest, so it’s exact even if the journey correlation is looser.
The result is a single timeline per journey: product events, interleaved with the AI traces that happened inside it, even if those traces came from different agents or tools.

​
Next steps

View & Filter Journeys

See the stitched result in the Journeys UI