Connect the work
Group relevant coding-agent activity with available outcome evidence.
Your AI bill shows who consumed more. TraceYield helps explain why.
TraceYield connects coding-agent usage with verified engineering outcomes, compares the trajectories behind comparable completed work, and turns observed workflow differences into practical management recommendations.
Currently selecting organizations for controlled founding pilots. Product screens and figures are illustrative.
✓Initial focus on Codex, with Claude Code and GitHub Copilot planned✓Data minimization by design✓Security review before each pilot
Illustrative product example
AI Coding Usage & Outcome Review
Fictional example using comparable completed work episodes
Pilot review
Not customer data
Lower observed
Compared with higher-usage episode
Reduced
Measured during pilot review
Verified
Contextual review
Before Workflow ChangeReview
- AI Prompting Style:Repeated similar requests
- Usage pattern:Higher observed
- Extra Manual Fixes:Follow-up needed
After Workflow ChangeImproved episode
- AI Prompting Style:Diagnostic evidence added
- Usage pattern:Lower observed
- Extra Manual Fixes:Less follow-up
TraceYield connects coding-agent usage with verified engineering outcomes, compares the trajectories behind comparable completed work, and turns observed workflow differences into practical management recommendations.
Comparable completed work (1 / 5)
Fictional example for product illustration. TraceYield evaluates work episodes in context and does not treat higher usage as poor performance by default.
How TraceYield Works
From coding-agent activity to management evidence.
Compare in context
Review comparable completed work and identify meaningful workflow differences.
Recommend and verify
Turn findings into practical interventions and measure later change.
What management can learn
The useful questions sit between raw usage and completed work.
TraceYield turns concrete work episodes into management questions that can be discussed, tested, and revisited without reducing the story to a developer ranking.
Repeated failure loops
Did the workflow continue after repeated failures without introducing new evidence or a revised hypothesis?
Review whether retries meaningfully changed the approach.
Context and model usage
Was the amount of context and model capability appropriate for the work being attempted?
Higher usage may be justified for complex work and must be evaluated in context.
Post-agent rework
How much human correction, testing, or cleanup was required after the agent reported completion?
Agent completion does not always mean engineering completion.
Management report
The commercial output is a management-ready finding, not a token chart.
TraceYield separates what was observed, what it may mean, what to try next, and how later work should be reviewed.
Illustrative management report
Similar requests continued after the same failure without meaningful new diagnostic evidence.
Repeated unsuccessful attempts were followed by minimal change in the next instruction.
High confidence within the reviewed work episode.
Require a revised root-cause hypothesis after repeated failure before additional code changes.
Review the next five comparable work episodes.
Controlled pilot and trust
Structured enough for management. Controlled enough for early validation.
TraceYield is not an automated disciplinary system or individual productivity score. Findings are reviewed in the context of comparable work.
Apply for the Founding PilotWhat the pilot produces
- Management-ready findings
- Practical workflow interventions
- Supporting evidence and caveats
- Before-and-after review
- Recommended next steps
How the pilot is controlled
- Agreed management question and team scope
- Data minimization
- Defined access
- Configurable retention
- Security review before deployment
- No simplistic productivity ranking
Compact FAQ
The questions enterprise buyers ask first.
Short answers on availability, supported workflows, data access, developer trust, and pilot controls.
Is TraceYield generally available?
No. TraceYield is currently preparing a limited number of controlled founding pilots and is not yet generally available.
Which coding agents are initially supported?
Initial development focuses on Codex workflows, with Claude Code and GitHub Copilot planned as additional integrations. Public support claims will stay limited until implementation and validation are complete.
What information does TraceYield analyze, and does it require source-code access?
TraceYield reviews high-level coding-agent activity, prompts, retries, context use, model use, tool activity, available outcome evidence, and follow-up work where a pilot agreement allows it. Data requirements and any source-code access are agreed before deployment.
Does TraceYield rank developers or treat higher usage as poor performance?
No. Work episodes are reviewed contextually, and legitimate complexity is not automatically treated as inefficiency. The product is not intended as a simplistic productivity or disciplinary score.
What does a founding pilot produce, and how are privacy and security requirements handled?
A pilot produces management-ready findings, recommendations, later measurement, and caveats. Scope, access, retention, data handling, and security requirements are agreed before deployment.
Conversion
Evaluate AI coding usage with evidence, not assumptions.
TraceYield is selecting a limited number of organizations for controlled founding pilots and later enterprise validation.
Pilot requests are reviewed directly by the TraceYield team. Submit the form to send your details securely. After submission, you will receive a confirmation email with a summary of your request.