Ssehgalnamitinarticles.namitsehgal.com·2h ago · 6 min readThe Tabletop Playbook Illusion: Why Compliance Drills and Ignored AI Realities Leave Enterprises VulnerableEvery year, enterprise organizations gather executives, legal counsel, risk officers, and PR leads for a two-hour "cybersecurity tabletop exercise". A hypothetical breach is presented on a slide, cont00
Ssehgalnamitinarticles.namitsehgal.com·Aug 14 · 5 min readDemystifying Enterprise GenAI Architecture: Neuro-Symbolic Systems, Knowledge Graphs, and Pluggable Domain EnginesExecutive Summary The generative AI landscape is undergoing a fundamental structural transition. For the past several years, enterprise AI engineering focused on building monolithic wrappers—wiring cu10
Ssehgalnamitinarticles.namitsehgal.com·Aug 13 · 10 min readDemystifying Legal GenAI Engineering: Neuro-Symbolic Architectures, Knowledge Graphs, and Singapore Common LawWhen legal tech vendors pitch their AI platforms, the marketing narrative often sounds remarkably polished: "Our platform uses a domain-specific neural network trained on millions of legal judgments t00
Ssehgalnamitinarticles.namitsehgal.com·Aug 13 · 9 min readDemystifying GenAI Engineering: What to Train, How to Build, and Real Healthcare ArchitecturesWhen embarking on a Generative AI journey, many organizations quickly find themselves asking fundamental questions: Should we train our own AI model? Do we need a network of autonomous agents? How can30
Ssehgalnamitinarticles.namitsehgal.com·Aug 13 · 10 min readEnterprise Multi-Agent Architecture: Model-Agnostic Token Optimization, State Machines, and GovOps ControlsTransitioning enterprise AI from isolated prompt-response models to autonomous multi-agent networks introduces a critical engineering challenge: quadratic token compounding. In a distributed agent gra00
