Technical articles on Data Engineering, AI/ML, and Cloud Platforms. Straight from real client work in logistics and fintech — no fluff.
Most RAG hallucination complaints are retrieval failures in disguise. Covers chunking strategies for mixed documents, hybrid search with BM25, reranking tradeoffs, and an evaluation framework that goes beyond it seems to work.
How logistics and manufacturing companies lose $100K+ per year to manual Excel processes, and the exact cloud architecture that replaces them with automated, auditable pipelines on Databricks, Snowflake, or BigQuery.
LangGraph state management breaks when parallel nodes write to the same key. Covers the fix, retry logic patterns, and testing strategies for non-deterministic agents in production.
Most RAG hallucination complaints are retrieval failures in disguise. Covers chunking strategies for mixed documents, hybrid search with BM25, reranking tradeoffs, and an evaluation framework that goes beyond it seems to work.
How logistics and manufacturing companies lose $100K+ per year to manual Excel processes, and the exact cloud architecture that replaces them with automated, auditable pipelines on Databricks, Snowflake, or BigQuery.
Dirty data breaks AI agents in specific, expensive ways: wrong RAG retrieval, hallucinations from bad context, garbage extraction outputs. Here is what data readiness for AI actually looks like, with a concrete checklist.
AI agents are processing invoices, triaging support tickets, and catching supply chain anomalies at mid-market companies right now. Here are 6 production patterns, the data infrastructure behind them, and the results teams are reporting.
After deploying Bronze/Silver/Gold across 15+ enterprise lakehouses on Databricks and Azure, here are the failure modes, anti-patterns, and Delta Lake patterns that actually hold up.
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