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Decision Systems
Architectures for making decisions under latency and scale constraints. Systems that must act with incomplete information, bounded compute, and real-time requirements.
Research Lab
Decision systems. Hybrid reasoning. Operational ML.
Protocogni Labs builds intelligent systems designed for real operational environments—where latency matters, data is noisy, and failure modes are consequential.
We work at the intersection of machine learning, classical reasoning, and systems engineering. Our focus is not on benchmark performance but on systems that function reliably when deployed: decision architectures that handle adversarial inputs, reasoning systems that combine learned and symbolic components, and infrastructure that operates at scale without sacrificing interpretability.
Core technical problems we work on
Architectures for making decisions under latency and scale constraints. Systems that must act with incomplete information, bounded compute, and real-time requirements.
Methods for synthesizing information from noisy, biased, or adversarial sources. Robust inference when input quality cannot be guaranteed.
Systems combining rules, learning, and retrieval. Architectures where symbolic constraints and learned components cooperate rather than compete.
Agent-like systems designed for real environments. Autonomy bounded by operational requirements, not open-ended optimization.
Search, ranking, and recommendation reframed as cognitive systems. Information retrieval as a form of machine reasoning.
ML systems built for production reality: monitoring, debugging, graceful degradation, and interpretable failure modes.