Long-form writing for teams who inherit production AI: what breaks after the demo, what to measure, and how to leave the system operable.
127 reports across 7 domains
Enterprise AI5 min read
Your team is stuck deciding whether a Large Language Model (LLM) or a Small Language Model (SLM) fits the target use case, and that indecision raises cost and delays delivery.
Read reportEnterprise AI6 min read
The next sprint, each product team clones the repo, upgrades the LLM, adds a custom prompt library, and pushes a new container.
Read reportEnterprise AI5 min read
The demo looked fine, but prompts reached a public endpoint in a non-EU data center.
Read reportEnterprise AI7 min read
Define the metrics that matter: latency at peak load, acceptable hallucination rate, and domain-specific accuracy thresholds.
Read reportObservability6 min read
The dashboard shows green p95 latency while a different story unfolds underneath.
Read reportEnterprise AI7 min read
After a demo, the hidden cost is an unguarded model endpoint that can leak proprietary data.
Read reportEnterprise AI6 min read
A silent breach by an AI agent can cost a midsize enterprise $500k before the incident is even detected.
Read reportLLMOps8 min read
Minutes later, on-call gets a ticket: the model returns disallowed content, compliance alerts fire, and legal asks for an audit trail that does not exist.
Read reportShowing 8 of 127 reports
