What to Monitor After an LLM Application Goes Live
Uptime dashboards will not tell you that answers have quietly become worse. A monitoring plan for quality, grounding, safety, cost and user behaviour.
Insights
Insights: page 2
Practical frameworks, evaluation methods and implementation guidance from the AARHIT Systems team. Written to be useful to technology leaders, practitioners and decision-makers.
Edge or Cloud? Deciding Where AI Inference Should Run
Latency, connectivity, privacy, model size and fleet management all pull the decision in different directions. A structured way to weigh them.
Prioritising AI Use Cases: A Scoring Approach That Survives Reality
Workshops produce long lists of AI ideas. A transparent scoring model balances value, feasibility, risk and readiness so the first projects are the right ones.
Five Questions to Ask Before Any AI Project
Before choosing models or vendors, five plain questions reveal whether an AI idea is ready, what it needs and how it could go wrong.
Measuring Document AI Accuracy at Field Level
An extraction system can be 95 percent accurate and still get the invoice amount wrong. Why field-level measurement drives better automation decisions.
Next step
Have a question these articles do not answer?
Our team is glad to discuss your specific situation, whether it concerns agents, evaluation, automation, data or governance.