Customer-answer regression checks
Re-test approved knowledge questions after a configuration change and compare unsupported claims or missed escalations.
Capability catalogue
ANNA OS powered ASAMIA AI · Intelligence
Model evaluation records
Keep the evidence behind an intelligence decision, so later reviews can see what passed, what failed and why.
Documented capability
Purpose and business value
Evaluation records connect a model configuration to a dated set of business tasks and reviewer findings. They capture the test inputs, expected outputs, scoring criteria and significant failures. Repeating the same cases after a change helps identify regressions that a general benchmark may miss.
A repeatable evidence trail supports accountable deployment decisions and makes quality changes easier to investigate.
Business use cases
Document-work quality review
Track extraction errors and reviewer corrections against a stable set of authorised sample documents.
Governance evidence
Give a release reviewer the test scope, exceptions and sign-off rationale instead of an unexplained pass label.
Technical requirements
- 01
Versioned cases, expected outputs and scoring definitions
- 02
Configuration identifiers, timestamps and reviewer ownership
- 03
Access and retention rules for test data and findings
- 04
Approved model modality and endpoint access
Functional specification
An evaluation record should preserve the configuration, dataset version, rubric, results, exceptions and review decision. Evidence must remain distinguishable from untested assumptions.
Operating controls
The demo does not execute live benchmark runs or establish independently verified performance results.
Model choice & ANNA LLM
Proprietary base-model policy
ASAMIA model catalogue review
Reasoning model selection
Vision model selection
Image model selection
Video model selection
Cost and region evaluation
Data retention review
ASAMIA AI access controls
Human-approved model changes