Conceptual
Logically or theoretically grounded, but not tested in practice.
BLUETRIX Research
Evidence
What we observe, what we can measure and what still requires validation.
Understanding evidence
What we mean by evidence.
Not every product capability is scientific evidence. Not every positive observation confirms a general theory.
Implemented
Technically implemented and functionally testable.
Observed
Observed in tests or applications, but not yet sufficiently controlled or generalizable.
Measured
Measured against defined metrics using a documented method.
Validated
Repeatedly confirmed under traceable conditions.
Independently Validated
Reviewed or reproduced by independent external parties.
No specific BLUETRIX claim is assigned a higher evidence level without actual documentation.
Evidence register
A schema instead of fictional entries.
The register will be populated only with documented, authoritative entries.
- Claim ID
- Claim
- Evidence level
- Model
- Data source
- Measurement method
- Sample or study scope
- Result
- Uncertainty
- Limitations
- Responsible area
- Date and version
- Next validation measure
Types of evidence
Different support for different claims.
Architecture evidence
Assessment follows a documented method with clear boundaries.
Functional evidence
Assessment follows a documented method with clear boundaries.
Process evidence
Assessment follows a documented method with clear boundaries.
Usage evidence
Assessment follows a documented method with clear boundaries.
Outcome evidence
Assessment follows a documented method with clear boundaries.
AI evidence
Assessment follows a documented method with clear boundaries.
Metrics
Measurement using clear categories.
Speed
first response time, acceptance time, processing time, handover time, time between process stages
Quality
information completeness, corrections required, error rate, successful handovers, commitments met
Business outcome
qualification rate, conversion, response to offers, opportunities, service closure, reactivation
Process stability
abandonment, escalations, failed automations, manual intervention, repeated processing
AI performance
latency, cost, acceptance or approval, correction rate, consistency, impact on subsequent processes
Transparency
No invented metrics.
Permitted are measurement frameworks, hypotheses, data categories, ongoing or planned validations and known limitations.
no invented percentages
no unsupported efficiency promises
no fictional customer results
no statistically unsupported generalizations
no scientific labels without a basis
Open research questions
Questions that require testable answers.
Which contextual information measurably improves recommendations?
How does cross-channel continuity affect abandonment and handovers?
Which signals are relevant to opportunity prioritization?
When should an AI agent act, recommend or hand over?
How can commitments be reliably identified?
Which metrics describe the quality of an interaction?
How can the benefits, costs and risks of Interaction Intelligence be assessed together?
AI transparency
Document AI outputs traceably.
Model and version
Input context
Data sources
Agent task
Output structure
Limitations
Approvals
Cost and latency
Evaluation method
Research status
Current development and research status.
BLUETRIX Research develops and tests conceptual and technical models for business interactions. The published frameworks represent the current state of development and research. They should not automatically be understood as scientifically validated theories or universally applicable evidence of effectiveness.
Transparency is part of product quality.
Follow the hypotheses, measurement methods, results and open validation questions behind BLUETRIX.