Engineering essays from the bench.
War stories, architecture decisions, and the occasional tirade. Written by the engineers who shipped it.
Zenith AI: Spam Filtering and Lead Scoring for Web Forms
Zenith AI screens every web form submission: eight anti-abuse layers, a spam score, lead tier, sentiment, urgency and a draft reply. How it works.
RAG Architecture: How to Build a Production RAG Pipeline
How to build a production RAG pipeline: parsing, chunking strategies, hybrid retrieval, reranking and evaluation, with a working Python example.
How to Build AI Agents for Production: A Practical Guide
How to build AI agents that hold up in production: the agent loop, tool design, state and memory, hard limits, evaluation, and the failures to plan for.
Enterprise Generative AI Strategy: A Practical Framework
A practical enterprise generative AI strategy: choosing use cases, build vs buy, a shared platform, cost modeling, governance and measuring real ROI.
What Is MLOps? A Practical Introduction for Engineers
What MLOps is, how it differs from DevOps, and a minimal working setup: versioned data, tracked experiments, CI evaluation gates, safe rollout, monitoring.
The Modern Data Engineering Stack: From Ingestion to Analytics
The modern data stack: ELT pipelines, dbt, lakehouses, orchestration with Dagster and Airflow, and real-time streaming.
Building Production AI Pipelines in Python, Step by Step
How to build a production AI pipeline in Python: validated ingestion, point-in-time features, tracked training, evaluation gates, serving and drift checks.
NLP for Business: Transformer Use Cases and How to Ship Them
Practical NLP with transformers: classification, document extraction, search, sentiment and summarization, and when a small model beats an LLM.
Computer Vision Quality Control: A Manufacturing Build Guide
How to build computer vision quality control for a production line: imaging, defect data, model choice, part-level evaluation, PLC integration and drift.
Building AI Customer Support That Actually Resolves Issues
How to build AI customer support that resolves issues: grounded answers, policy-checked actions, clean human handoff, and metrics beyond deflection.
