Research, playbooks and opinion from the engineers, researchers and customers building with Arshan.
From agentic workflows to small, specialised models, these are the shifts that will shape how businesses build with AI this year. We break down each trend, what it means for your roadmap and where to start.
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What changes when every employee has a capable AI assistant, and how to roll one out without losing control of your data.
Agents are only as good as their tools and guardrails. Here is the architecture we use with customers, step by step.
A decision framework for when to train on your data and when to ground a model in it, with cost and accuracy trade-offs.
How a modern feature pipeline turns clickstreams and transactions into forecasts your teams will actually use.
Bias testing, explainability, human review and audit trails: the controls we recommend before any model reaches customers.
The best results come from pairing people and models well. We look at the roles, rituals and metrics that make it work.
Latency, cost and data residency all pull in different directions. Here is how to place each workload in the right place.
Classic RPA breaks when screens change. Pairing bots with language models makes automations that adapt instead of failing.
The handful of metrics finance teams want to see before they sign off on an AI programme, and how to measure them.
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