TraceYield
AI-assisted work intelligence

See what happened
between the task
and the result.

TraceYield reconstructs the moments behind AI-assisted work and helps explain why similar tasks can lead to different outcomes, effort and AI spend.

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Task
/

Fix the login bug

Session trajectory reconstructed
01First approach
Token handling changed
02Tests still failing
Change of direction
WHY DID THE APPROACH CHANGE?

We first increased the timeout, but importing a large CSV still crashed. The logs showed the app was loading the entire file into memory, so we changed the importer to process it row by row instead.

High confidence
03Working solution found
Tests passing
TraceYield Intelligence
·
AI cost in context

The bill shows how much.
TraceYield shows what happened along the way.

The same feature can reach a similar result through a very different development path.

Session A

Product search with filters

32k tokens
€0.71
Filter approach defined
Query parameters added
Filtering moved into the API
Empty states handled
Feature working
Direct pathFew major changes
Session B

Same feature

76k tokens
€1.69
Filtering started in the frontend
Large datasets caused problems
Filtering moved into the API
Query logic changed
Pagination had to be reworked
Feature working
More changesSeveral iterations
Why the difference?+44k tokens along the way

Both sessions ended with a working feature. In Session B, the implementation changed direction several times — filtering started in the frontend, moved to the API, and pagination had to be reworked before the feature was complete.

TraceYield shows where the implementation changed, where extra iterations appeared, and how those moments contributed to the difference in AI usage.

APPLIED SCENARIOS

Different work. Different questions.
The same missing layer.

TraceYield makes the work between task and result visible — but the useful question depends on the context.

Education

Teachers are preparing the next generation of developers — in a world where AI can produce an answer before the student understands the problem.

A finished assignment no longer tells the whole learning story.

Student → Project → Context·Observed

WHY did the student arrive at this answer?

TraceYield keeps the working process available for review: what the student tried, where the approach changed and how AI was used along the way.

Teaching the next generation →
Engineering

AI can make good software work faster. It can make a wrong assumption travel faster too.

A finished ticket rarely shows how the developer and AI reached the result.

Developer → Project → Context·Observed

WHY did this task consume 3× more AI?

TraceYield shows where the implementation changed direction, where rework appeared and what happened before the final result.

For engineering →
APPLIED SCENARIOS / PART 2

The same trajectory can answer different questions.

What matters changes by context. The underlying work history does not.

Recruitment

A finished coding assignment says less about a candidate when AI can produce much of the final result.

The important question is how the candidate approached the task and worked with AI.

WHY did the candidate choose this approach?

TraceYield keeps the route to the solution available for review, giving the technical conversation more context.

For recruitment →
CODING ASSIGNMENT
Add product filtering
01

First approach

Filter results in the frontend

02

AI suggestion

Move filtering to the API

03

Candidate decision

Changed the query design instead

04

Result

Working implementation

WHAT BECAME VISIBLE

The candidate challenged the suggested approach and chose a different implementation.

Training / AI Adoption

Completing AI training is easy. Changing day-to-day work is harder.

See whether new working habits show up in real sessions afterwards.

WHY are post-training sessions different?

TraceYield shows how working patterns change across real sessions, rather than relying only on course completion or self-reporting.

For training & AI adoption →
BEFORE TRAINING
1
Prompt
2
Accept
3
Fix
4
Retry
5
Result

AI output accepted immediately

4 corrections afterwards

AFTER TRAINING
1
Plan
2
Generate
3
Review
4
Adjust
5
Result

AI output reviewed before implementation

1 correction afterwards

WORKING PATTERN CHANGED

Review moved earlier in the development process.

Patterns across sessions

One journey answers a question.
More journeys reveal what keeps happening.

Observed across 5 recent projectsTimeline →
P-01
Focused start
P-02
Focused start
P-03
Broad start
P-04
Focused start
P-05
Focused start
Recognized pattern· Observed in 5 of 7 relevant projects

“Strong starts after clear context.”

In recent projects, sessions that begin with explicit constraints reach a focused direction earlier.

Context: These sessions also tend to use AI more selectively early on.

WHY?

Clear constraints reduce the number of competing directions explored early in the work.

Explore pattern →Patterns across work

Not another AI dashboard.

See the journey.Ask why.Recognize what keeps repeating.

With the evidence behind it.

Go deeper when needed. For selected work, TraceYield can connect AI conversations with relevant documents, project outputs, tests or repository activity.

Get started

Ask better questions
about how AI-assisted work actually happened.

Start with one real session.

Request a Demo View an example