Simor Consulting
Category: Operations
AI systems fail differently than traditional software. A traditional software bug produces incorrect output deterministically -- the same input always produces the same wrong output, and a fix elimina
The pipeline runs every night at 2 a.m. Nobody fully understands it. The original author left in 2019. It is part SAS, part shell, part stored procedures, and part a spreadsheet someone emails in. It
Professional services firms sell judgment, billed by the hour or by the matter. That makes them both the biggest winners and the most cautious adopters of AI. The upside is real: every firm carries ho
A head of ML at a 120-person company told us recently that his team had spent nine months trying to stand up a "proper MLOps platform." They had evaluated three orchestration tools, designed a feature
Most RAG systems are evaluated with vibes. An engineer runs ten queries, eyeballs the results, and declares the system "working." Three months later, a customer reports that the system confidently ret
The first Head of AI Engineering at a company inherits one of three situations. Situation one: there is no AI team, no AI infrastructure, and the mandate is to build from scratch. Situation two: there
Software rollbacks are well-understood. You deploy a new version, detect an issue, and roll back to the previous version. The rollback is atomic: the entire application reverts to the previous state.
On a Tuesday at 2:14 PM, a major model provider began returning elevated error rates for a specific model endpoint. By 2:31 PM, a customer support platform that depended on that endpoint was producing
Organizations that skip readiness assessment before investing in AI tend to discover their gaps expensively. A financial services firm spent four months building a customer churn prediction model only
Bias in AI systems is not a theoretical risk. It is a measurable property that can be detected, quantified, and mitigated at every stage of the pipeline. The teams that treat bias as an audit problem
The case for streaming is straightforward: data that arrives in minutes instead of hours enables decisions that were previously impossible. Fraud detection catches transactions before they clear. Pers
LLM inference costs follow a pattern that catches teams off guard. The first prototype costs almost nothing -- a few hundred dollars a month during development. The pilot scales to a few thousand. Pro
Most data quality initiatives fail not because teams lack tools, but because they measure the wrong things. Teams track hundreds of data quality metrics, generate dashboards full of green indicators,
Prompt management in most AI teams starts the same way. One engineer writes a prompt, it works well enough, and the prompt gets committed to a config file. Three months later, there are forty prompts
Every AI infrastructure team eventually faces the same argument. One faction wants to build a custom solution because the commercial options do not handle their specific requirements. The other factio
Most vector database selection failures come down to one mistake: picking the technology before mapping the workload. Teams benchmark embedding search speed on a curated dataset, pick the fastest opti