Simor

Data Infrastructure for Production AI

Practical writing on AI data engineering, feature stores, and the infrastructure choices that determine whether AI systems work in production.

Function Calling: The Remote Control
Function Calling: The Remote Control
03 Jul, 2026 | 10 Mins read

You press the power button on your remote. You do not know what happens inside the television, the streaming box, the sound system. You do not need to know. The remote sends a command. The devices res

Graph databases for AI: Neo4j vs Amazon Neptune vs ArangoDB
Graph databases for AI: Neo4j vs Amazon Neptune vs ArangoDB
02 Jul, 2026 | 05 Mins read

Graph databases went from niche to essential as AI applications discovered that relationships matter. RAG applications that only search by vector similarity miss the connections between entities. Reco

Prompt Versioning in Git: Prompts as Code, Not Configuration
Prompt Versioning in Git: Prompts as Code, Not Configuration
01 Jul, 2026 | 10 Mins read

Prompts are the most frequently changed component of an AI application. They are updated to fix edge cases, improve output quality, accommodate new use cases, and adapt to model behavior changes. Desp

Consolidating 47 data sources into one knowledge layer
Consolidating 47 data sources into one knowledge layer
01 Jul, 2026 | 05 Mins read

A global professional services firm with 8,000 consultants maintained institutional knowledge across forty-seven separate systems. Project proposals lived in a document management system. Client engag

AI Middleware: The Missing Abstraction Between Your App and the Model
AI Middleware: The Missing Abstraction Between Your App and the Model
30 Jun, 2026 | 09 Mins read

When web applications needed to talk to databases, the industry created ORMs and connection pools. When microservices needed to talk to each other, the industry created API gateways and service meshes

Real-time fraud detection: from proof-of-concept to production in 90 days
Real-time fraud detection: from proof-of-concept to production in 90 days
30 Jun, 2026 | 05 Mins read

A payment processor handling twelve million transactions per day had a fraud detection system that was accurate but slow. The system reviewed transactions in batch, four times per day. A fraudulent tr

OpenAI vs Anthropic vs Google: Model Provider Failover Strategies
OpenAI vs Anthropic vs Google: Model Provider Failover Strategies
29 Jun, 2026 | 10 Mins read

Every major model provider has had outages. OpenAI has gone down during peak hours. Anthropic has experienced degraded performance. Google Gemini has had API issues. If your application depends on a s

Why I stopped chasing the latest AI framework
Why I stopped chasing the latest AI framework
29 Jun, 2026 | 04 Mins read

In 2023, I rewrote a data pipeline three times because the framework landscape kept shifting. First it was built on LangChain. Then the team wanted to switch to LlamaIndex because it handled retrieval

LLM gateway comparison: LiteLLM, Portkey, Martian
LLM gateway comparison: LiteLLM, Portkey, Martian
29 Jun, 2026 | 07 Mins read

A production AI application calls multiple LLM providers. The primary model is GPT-4o for complex reasoning, but simple classification tasks use Claude Haiku for cost savings, and the fallback for rat