Insights
Practical thinking on production AI deployment — what works, what doesn't, and what it actually takes to ship AI systems into regulated environments.
A look at where AI is genuinely reducing pressure on referral pathways — and what separates the deployments that work from those that stall.
Using MCP, guarded backend tools, and grounded reasoning across plans, inspections, records, and regulations for Fire Safety.
LLMs don't do Big Data. But with RAG and the Prompt, the LLMs gets a tiny fraction to make it work.
How an OntosLab Frontline Deployed AI Engineer — embedded directly inside a large energy operator to radically improve grid security.
Scope creep, unclear success criteria and the wrong problem statement are the top three reasons AI projects miss their delivery dates. Here is how to get it right upfront.
The gap between a promising demo and a live system is where most AI initiatives die. Here is what causes it and what forward deployed engineers do differently.
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