Applied AI
Measuring ROI in Agentic Workflows: Reducing Cost Per Shipment for CFOs
Agentic workflows deliver measurable ROI by reducing the incremental cost per shipment (CPS) through end-to-end automation and governance.
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Technical Advisory & Systems Research
Engineering Perspectives
Deep dives into Agentic Workflows, distributed systems, and the architectural rigor required to move AI from experimentation to enterprise-grade production.
Applied AI
Memory bandwidth and local agent reasoning speed: design patterns for production AI
In modern AI deployments, local agents operate at the edge of data and decisions. They interact with knowledge sources, run planning loops, and fetch tool outputs in real time.
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Applied AI
Memory leak testing for ML inference pipelines
Memory leaks in ML inference services can quietly erode throughput, spike latency, and drive cloud costs. This article provides a practical, production-grade approach to detect, quantify, and remediate memory leaks in live ML inference stacks, with an emphasis on governance, observability, and repeatable test patterns.
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Applied AI
Mentoring junior PMs with AI agents in practice
AI agents can serve as scalable mentors for junior product managers, offering structured onboarding, scenario-based coaching, and governance-aligned feedback.
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Applied AI
Mentoring junior Product Managers with AI agents in production-focused programs
In production-focused AI programs, mentoring junior Product Managers (PMs) requires more than slides and checklists. Mentorship must be a repeatable, governance-backed workflow that scales with teams and preserves traceability.
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Applied AI
Metadata filtering and validation for production AI
Metadata filtering and validation explains practical architecture, governance, observability, and implementation trade-offs for reliable production systems.
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Applied AI
Metaverse-Driven Sim-to-Real Testing for Manufacturing
If you are tasked with deploying autonomous workflows on the factory floor, the practical route is a metaverse that tightly couples digital twins, simulation, and live control with rigorous governance.
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Architecture
Migrate MDM rules to a graph database: a practical, production-focused approach
Master data management (MDM) rules define how critical data objects relate, transform, and govern usage across systems.
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Applied AI
Mitigating AI Bias in Agile Cycles with Practices
Mitigating AI bias in agile cycles is essential for trustworthy production AI. In modern enterprises, bias surfaces across data pipelines, model evaluation, and agent-driven decision loops that span teams and services.
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