Short, practical notes on AI and technology—written for people shipping inside real businesses. We share what we are seeing in the field and how to apply it without the hype.
Showing sample posts until published insights are available in the database.
AI workflow·
Treat the model as a junior analyst, not a policy engine
The most reliable setups keep humans on approvals while models draft, classify, and gather context. Narrow the task, define the inputs, and measure quality on real cases.
In practice
Pilot: inbound requests → model draft + checklist → human sends. Track edit rate before expanding scope.
Data & governance·
Your data contract matters more than the model name
Teams ship faster when they agree on what “source of truth” means, how fields update, and who can override. A thin, explicit schema beats a heavy integration roadmap.
In practice
Pick one workflow (e.g. CRM notes) and document fields, owners, and retention before wiring any automation.
Product & UX·
Latency budgets are a product decision
Interactive assistants feel broken when answers arrive in unpredictable bursts. Set a target response envelope and design UI states around it—including partial results and graceful fallbacks.
In practice
Define p95 latency for “first token” and “complete answer,” then align prompts, retrieval size, and UI copy to those numbers.
Security·
Security reviews should start with access paths, not buzzwords
Map who can reach which systems, what tokens exist, and where logs go. Most incidents are familiar mistakes—over‑permissioned keys, shared accounts, and missing audit trails.
In practice
Produce a one-page access map for each vendor integration; rotate keys and remove unused service accounts quarterly.