From TensorFlow Legacy to Evaluation-Centric AI Systems
Re-Architecting Enterprise Threat Intelligence for the Foundation Model Era Chandra Pendyala Working Paper v5 14,000+Enterprise workloads processed ~60%Inference cost reduction per request Tens of thousandsProduction requests measured Executive Summary Dimension Result / Position Modernization trigger TensorFlow-era infrastructure cost pressure driving a migration of an existing production platform Production scale 14,000+ enterprise workloads across IaC, IAM, […]
Bridging the Agentic Reliability Gap:
From Pilot to Production February 2026 —- Chandra Pendyala Abstract: Do autonomous agents work? Yes — that question is settled. A better question is how well, and under what conditions. The most important question is how to create enterprise value today, not after AGI. This paper answers all three. We quantify where agents succeed and […]
Next Year in Enterprise AI: Pragmatic AI and Calculated Offense
As Yogi Berra said, “Predictions—especially about the future—are a tricky business.” So forget predictions. The only question that matters in 2026 is how enterprises spend AI money. One bucket is efficiency.LLMs are a new component —useful for drafting, search, summarization, and conversational interfaces.Paired with mature event-driven architectures, productized data, and reliable ML for speech, vision, […]
Open Weights Models – Interesting Applications.
by Chandra Pendyala The two most obvious use cases are now commonplace. Did we do anything else interesting? Yes we did! First the two obvious ones: 1, Availability of Hardware and Kernels that allow for cost efficient deployment of these models: Inference costs go down by upto 99%, finetuning costs go down by upto 50x..Depends […]