What it actually takes to trust AI to act — not just listen. Architecture, compliance, and real-world workflow automation across voice and chat channels.
The voice AI industry has spent a decade making AI sound more human. None of it closes the gap that actually matters: AI that can be trusted to act. SKYLA wraps neural intelligence inside deterministic policy enforcement — to explore how configured policies can constrain a workflow before execution.
Read article →First Notice of Loss is multi-system, compliance-critical, and emotionally complex. If an agentic AI handles it autonomously — fraud check mandatory, claim filed, appointment booked — it can handle almost any enterprise workflow.
Read article →Most enterprise AI deployments hit the same wall. The model works in the demo. Then someone asks: how do we prove it to the regulator? The answer starts with compliance as architecture — not as a checkbox.
Read article →It's Monday 8am. Three hundred calls are queuing. A significant portion are entirely predictable — status queries, bookings, confirmations. These don't need judgment. They need system access and accurate execution. The interface now exists.
Read article →SKYLA's Agentic Runtime Fabric (ARF) is channel-agnostic. The same policy enforcement, the same deterministic execution, the same immutable audit trail — whether the workflow arrives as a voice call or a chat message.
FNOL is the primary voice use case. But the architecture runs across any regulated, rules-driven workflow — in any channel.
The FNOL demo is Atsky’s own demonstration. Start a browser voice session with SKYLA to explore a complete motor claims intake — identity verification, accident details, mandatory fraud check, claim creation, assessor booking. Use fictional claim details.