01
Define what was measured
Name the company environment, role, request group, and analysis boundary.
Tribble Research / Repeated AI work
Measure the work your AI stack keeps doing again.
Companies repeatedly pay people and models to resolve questions and procedures the business has already handled. Tribble measures how often that happens and when approved work can be reused without lowering quality.
- System
- Research method
- Focus
- Measure before making a claim.
- State
- Limits attached
- Output
- Supported finding
PRF-810Limits attached
We do not promise a savings percentage. We measure your workload against its current baseline.
The research program groups genuinely similar requests, compares ways to run the same work, measures cost, and checks whether answer quality still meets the required bar.
Every published result names what was measured, how it was compared, and where the conclusion stops. Your workload is measured separately during a Production Proof.
02
Compare the same work
Test different approved execution paths against the same tasks and quality bar.
03
Keep the evidence attached
Record the source, method, limitation, date, and denominator behind every number.
04
Stop where the evidence stops
Do not generalize route performance, quality, or savings beyond the measured workload.
We measure the repeated work before calling it savings.
Every comparison names the workload, quality bar, method, denominator, and limitation.
Family
Normalize
Group exact repeated request patterns within a defined population.
Route
Match
Identify work eligible for controlled route comparison.
Quality
Qualify
Apply evidence and evaluation requirements before cost interpretation.
Evidence
Publish
Release only claims with a named population, comparison, method, scope, and limitation.
Method and evidence
What we measured, and where the conclusion stops.
No generalized savings claim is published yet.
Until the research claim clears approval, these named customer outcomes are the public proof. Each belongs to the application and population that earned it.
Review the research boundary during a production proof engagement.
In one working session, we map the current applications, model spend, repeated work, and the first result worth proving. You keep the map either way.