Research questions for practical business AI.
Anova AI Labs is a research track under AnovaGrowth for defining how smaller models, scoped agents, and deployment choices should be evaluated before anyone recommends them for a business workflow.
Quick answer: This page documents proposed questions, methods, and review gates for lightweight models, task-specific agents, and deployment patterns. It does not present a finished model or client outcome.
Questions before a model becomes a recommendation.
What decision should improve?
What evidence would count?
Where must a person decide?
Concept work is not presented as a validated client outcome.
Questions this research track needs to answer.
Smaller models
Which compact model can be tested against a defined task, cost boundary, and acceptable failure mode?
Agent workflows
Which permissions, source context, and approval steps are required before a tool-using agent can act?
Self-hosted inference
When do local machines, workstations, or private environments provide a useful operating tradeoff?
Evaluation loops
What evidence, baselines, and review gates are needed before a model is recommended?
Useful only when the evidence is specific.
The research track records the task, evaluation conditions, operating limits, and review boundary a model would need before it can inform a client recommendation.
Read research methodsTask fit
A recommendation starts with a bounded task, not an abstract model comparison.
Operating limits
Privacy, cost, uptime, and hardware conditions stay visible in the method.
Review boundary
Sources, actions, and exceptions remain visible to the person responsible.
Clear labels
Research concepts stay separate from validated client evidence.