📰 Insights
The Kotik AI blog explores the intersection of machine learning engineering, product design, and governance with practical examples and clear guidance. We write about experiments that were decisive, tests that revealed important constraints, and operational patterns that kept systems reliable. Expect case-based writing on MLOps, evaluation frameworks, human-in-the-loop flows, generative content safeguards, and product UX for model-driven features. Our audience includes engineers, product managers, and designers who need concrete practices and trade-offs rather than academic abstraction. Each piece tries to make the path from hypothesis to production visible and reproducible. We publish short technical notes, medium-length how-tos, and occasional deep-dives that document reproducible experiments, monitoring strategies, and governance checklists. The goal is to share knowledge that helps teams move from uncertainty to trustworthy, measurable AI in their products.
This article outlines how we validated retrieval sources, designed confidence routing to humans, and instrumented metrics to detect regressions in production. It covers experiments, ablations, and the operational playbook used after launch.
A case study describing feature design, privacy-preserving techniques, and monitoring required to deliver a recommendation engine that improved task completion while avoiding harmful feedback loops.
An operational playbook for generative systems that includes provenance logging, review queues, and criteria for safe publication. Includes examples of lightweight evaluation metrics and rollout strategies.
The blog focuses on practical learnings and reproducible experiments. Each post explains the problem context, the hypotheses we tested, the evaluation metrics we used, and the operational decisions we applied when a prototype showed signal. We prioritize clarity: experiment setups are described so readers can reproduce or adapt them, and trade-offs are called out explicitly. Posts cover technical steps, user-facing design, and governance considerations. We avoid speculation without evidence and prefer concise case studies that include outcomes. When writing about generative systems or user-facing assistants, we include safety considerations, fallback strategies, and monitoring ideas so teams can adopt patterns responsibly. Occasionally we publish short technical notes with code snippets and evaluation recipes; longer pieces provide reproducible experiments and a governance checklist for product teams to follow. If you'd like to propose a guest post, share a case study, or request a deeper write-up on a topic, contact us and we can coordinate a walkthrough and potential collaboration.
