It's Monday morning at 8am. Your contact centre agents start their shifts. Three hundred calls are already queuing. A significant portion — the exact number varies by industry, but research consistently puts it between 50 and 80% — are entirely predictable. Address changes. Status queries. Appointment bookings. Policy confirmations. Claim status checks.

These calls do not require human judgment. They require system access and accurate execution. An agent navigating four screens to book an assessor appointment is not doing knowledge work. They are doing data coordination — connecting a caller to a system the caller could, in theory, reach directly, if only the interface existed.

The interface now exists.


Monday 8am — incoming call queue breakdown
Monday morning call queue: 65% automatable, 35% needs human judgment TOTAL INCOMING QUEUE: 300 CALLS ~65% SKYLA handles autonomously (voice + chat) ~35% need judgment AUTOMATABLE — ROUTINE WORKFLOWS 📍 Address Changes 📊 Status Queries 📅 Appointment Booking 🛡 Policy Confirm 📋 Claim Status NEEDS JUDGMENT ⚖️ Disputes 🔥 Escalations 🧩 Edge Cases ✓ Resolved autonomously · 0 hold · <200ms per step · GER audit record · Agent queue cleared by 65% → Transferred with full context pre-loaded

What SKYLA does with those calls

SKYLA intercepts the predictable calls — the ones where the workflow is known, the steps are defined, and the correct outcome is deterministic — and handles them autonomously across both voice and chat.

SKYLA handles autonomously
Routine workflows — voice & chat
  • Address changes and record updates
  • Order status, claim status, account queries
  • Appointment booking, rescheduling, cancellation
  • Policy confirmations and coverage checks
  • Payment confirmations and direct debit updates
Escalated to human — with full context
Judgment-required cases
  • Complex disputes and contested decisions
  • Emotionally escalated interactions
  • Novel edge cases outside policy scope
  • Cases requiring regulatory discretion

The caller speaks naturally. SKYLA resolves the intent, executes across the relevant enterprise systems via REST/OAuth2, and confirms the outcome before the call ends. No hold. No transfer for the routine part. No agent in the loop for the workflows that don't need one.

"When SKYLA reaches a case that genuinely requires human judgment, it transfers with full context pre-loaded. The agent receives the call knowing everything that was said and everything that was done. The caller never repeats themselves."

The escalation model
🤝
The handoff — seamless for agent and caller
When a call requires human judgment, SKYLA transfers with the full session context: what was said, what was verified, what was attempted, what succeeded. The receiving agent sees everything on their screen before they say a word. The caller doesn't repeat themselves. The call continues — not restarts.

This is not theory

The live demonstration is a complete FNOL motor claims intake — first notice of loss for a motor insurance claim. Five steps: identity verification, accident detail capture, mandatory fraud check, claim file creation, assessor booking. All autonomous. All in one call.

FNOL is the hardest version of this problem: emotionally charged, compliance-critical, multi-system. If SKYLA handles it correctly, it handles address changes, status queries, and appointment bookings with room to spare.

The same architecture runs in healthcare back-office workflows, European telco customer service, and any high-volume process where the workflow is rules-driven and the execution is currently manual. Voice and chat. One policy layer. One audit trail.

Where it works

🏎
Insurance — FNOL & claims
🏥
Healthcare back-office
📡
Telecom CX
🏦
Financial services
🏛
Public services
🛍
Retail & e-commerce

The Monday morning problem is solvable. 50–80% of the queue doesn't need to be there — not because the work is unimportant, but because the work is deterministic. System access and accurate execution. That's SKYLA.