Build deterministic, explainable AI decision systems that teams can trust. EvaluatorDPT delivers governed, auditable decisions with real-time steering and policy-aware controls.
Simple Machine Mind builds decision systems that convert unstructured inputs into governed outcomes—YES / NO / TBD—with explainability metadata so decisions are inspectable, auditable, and repeatable under uncertainty.
No prompt drift, no guessing. Signal-based decisioning produces consistent outcomes every run, with confidence and constraints behind every decision.
Every decision includes explainability metadata—signals, vectors, and reasoning traces that make AI behavior transparent and auditable.
Policy-as-code guardrails ensure compliance and trust. Decision steering shapes outcomes safely within defined boundaries.
Plug-and-play, inference-only Decision AI on Azure. Clean REST integration with enterprise security posture built in.
No data retention, no PHI/PII/PCI stored, no open weights. Your data stays yours, with audit trails and accountability.
Transparent metrics you can validate. Current macro-F1: 82.4% on multi-class, multi-label evaluation tasks.
A governed decision layer that sits between AI systems and real-world actions, ensuring decisions are policy-aligned, explainable, auditable, and subject to human oversight when needed.
Built for teams that need AI they can trust in production—with complete governance, explainability, and intelligent escalation.
Understanding EvaluatorDPT's decision framework, explainability, and real-world applications
Any decision governed by policies, rules, or organizational standards
Designed to support future validation in domains such as:
Note: Deployment in regulated or safety-critical environments requires domain-specific validation and certification.
From data pipelines to deployed APIs, we deliver production-grade AI systems with governance, explainability, and accountability built in.
We conduct foundational and applied research in artificial intelligence, focusing on reasoning, governed decision-making, cognitive architectures, and explainable intelligent systems.
Explore Our ResearchFounder & CEO, AI Researcher
Sankar is a people-first leader and hands-on builder focused on decision-first AI. His work sits at the intersection of data analytics, governance, and AI product development—taking real-world ambiguity and turning it into deterministic, explainable, policy-aligned decisions that teams can trust. He created EvaluatorDPT, a decision engine designed for enterprise security posture, low latency, and auditable outcomes. Sankar holds a provisional patent in Cognitive Modeling and leads technology-focused engagements spanning model development, evaluation, governance, LLM integration, and secure MLOps on Azure.
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