Two engines. One standard: auditability.
Important clarification: our dermatology imaging stack and our multi-domain tabular engine are not the same model. We ship a portfolio: (A) interpretable medical imaging and (B) V7 CORE++ for tabular discovery.
Design principle
Traceable decisions
Deployment posture
Budget-aware training
Output
Score + evidence
Medical Imaging (Dermatology)
- Interpretability-first workflow (clinician-facing evidence artifacts)
- Quality controls + reproducibility discipline
- Designed for medical review pipelines
V7 CORE++ (Tabular Discovery)
- High-signal learning on tabular data under distribution shift
- Supports domains such as materials discovery, molecular target selection, digital olfaction, and risk/fraud analytics
- Agent-controlled curriculum and sampling with full decision trace
From a probability to a case file
In high-stakes environments, a model output must be explainable to humans. Our stack produces a risk score and a compact evidence summary that can be expanded into a structured investigation report.
- Human-readable reasons (feature-driven flags / attributions)
- Reproducible run passports (data split integrity + artifact hashing)
- Policy locking for compliant operation
QuantX (patent-safe highlight)
QuantX is an adaptive training meta-controller that learns when to apply curriculum actions (e.g., hard-mining intensity) while remaining bounded by operational constraints.
- Controls discrete curriculum actions (no hyperparameter soup)
- Budget-aware (time / memory / mining ratio constraints)
- Audit trail: every decision is logged and reproducible
Let’s talk
If you want a private technical brief, an evaluation protocol, or a demo tailored to your constraints, contact us.
Email: [email protected]
Website: acuvizionai.com