Insights
Hard truths about
platforms, cloud, and AI
Short, direct takes on the problems I see in every engagement. No fluff. No theory. Just signal from real production systems.
Your platform is already failing. You just can't see it.
Most scaling problems are not about capacity. They are about architecture decisions made two years ago that nobody revisited. Here is how to find them before they find you.
Get a Platform Failure MapYour AI project will fail in production.
The model works in a notebook. It fails in production. The gap is not the model. It is the platform underneath: unreliable data, no crash recovery, no prompt versioning, no observability.
Check your AI readinessYou are wasting 30% of your cloud spend.
High-cardinality metrics, over-provisioned infrastructure, unused workloads, and architecture decisions nobody revisited. The waste is hiding in plain sight.
Find the wasteYour observability is lying to you.
Green dashboards, red customers. Single-replica components in critical paths. Misconfigured scaling. I have found 28 hidden issues in a single observability audit.
Audit your observabilitySlow pipelines delay decisions.
When your data pipeline takes an hour, your business runs on stale numbers. ETL redesign, query optimisation, and event-driven architecture can cut that to minutes.
Fix your pipelinesAI does not fix bad systems. It amplifies them.
Most companies think they have an AI problem. They have a platform problem. Unstable systems, runaway costs, poor observability. Bolting AI onto that makes everything worse.
Fix the platform first