Use case - Executives, PM, Engineering, Ops
Know whether AI is improving the business workflow.
Connect AI behavior to the user actions and business outcomes that follow. See where AI is being adopted, where it creates rework or delays, and whether each workflow is becoming faster, more accurate, and more valuable.
Measured across every journey
USER JOURNEY
User Journey measures whether the business process worked.
Even when work spans multiple agents, applications, and human steps, you can see exactly how it happened, distinguish an isolated model issue from a broader workflow problem, and prioritize fixes by business impact.
- The user and product actions that shaped the workflow
- The exact AI traces, models, and agents involved
- The prompts, retrieved context, and tool calls behind each response
- The human reviews, edits, corrections, and escalations that followed
- The workflow events and final business outcome
Outcomes
Measure the outcomes that matter.
Build custom dashboards across users, events, AI traces, and journeys.
Adoption
Are people engaging with the AI and using its output?
Completion
Does the AI-assisted journey reach the intended outcome?
Accuracy and trust
Is the output accepted, corrected, rejected, or escalated?
Cycle time
Is AI helping the workflow complete faster?
Human effort and rework
Where are people repeatedly correcting or redoing AI-generated work?
Cost and value
What does each journey cost, and what value does a completed workflow create?
Who it's for
One workflow, three views.
Product & automation teams
Understand where AI is helping users complete work, where it creates friction, and which improvements will have the greatest effect on adoption, completion, and cycle time.
Engineering & AI teams
Connect poor outcomes to the exact trace, model, prompt, retrieval step, or tool involved. Prioritize technical fixes based on frequency and business impact.
Business & operational leaders
See whether AI-powered workflows are completing more work, reducing cycle time and human effort, controlling cost, and delivering measurable value without increasing unacceptable risk.
Integrations
Works with the stack you already have.
Connect traces from Langfuse or LangSmith, collect user and product signals through DataFramer's browser SDK, and propagate journey context through backend services with DataFramer's server instrumentation. No observability replacement is required.
Stop measuring AI in isolation.
See whether it is improving the workflow.