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
Trace vs journey
A trace tells you what the model did. A journey tells you whether the business process worked.
A model can return a technically successful response while the larger workflow still fails. The user may edit the answer, reject it, abandon the task, escalate it to a person, or use it in a decision that creates downstream rework.
DataFramer connects the AI response to what happened before and next, so you can answer questions such as:
- Are people using and trusting the AI?
- Is the workflow completing successfully?
- Where is AI causing edits, escalations, or rework?
- Is AI reducing cycle time and operating cost?
- Which accuracy problems have the greatest business impact?
- Is the workflow delivering measurable value?
Outcomes
Measure the outcomes that matter.
DataFramer helps teams measure AI in the context of the work it is supposed to improve.
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?
One timeline
See the full workflow and trace every outcome back to AI.
DataFramer connects every user action, AI interaction, human decision, and business outcome in one chronological journey. Start with the final result and trace it back through:
- 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
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.
Dashboards
From one journey to every journey.
Build custom dashboards across users, signals, and journeys. Track counts and trends, measure funnel conversion and cycle time, and attach cost or value to workflow steps.
Filter by events, errors, models, or properties, then open the journeys behind any metric to find exactly where AI is helping or hurting the workflow.
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.