Mondial AI – Next‑Gen AI Call Center Platform

Note: All numbers are modeled or derived from pilot‑like synthetic scenarios and are for illustration only. Robust evidence requires controlled field studies.

Summary

Mondial AI consolidates voice, chat, and self‑service interactions at enterprise scale. The stack combines real‑time ASR, domain‑specific NLU, verifiable dialog policy with guardrails, safety triage (HITL), and agentic workflows for end‑to‑end orchestration. Modeling indicates 35–50% of contacts shift to self‑service/callbacks; ASA drops 60–80%, abandonment 50–70%. Multi‑channel reminders cut no‑shows by 3–6 pp; cost per contact falls 20–40%. GDPR‑by‑design, auditability, and interoperability (HL7 FHIR) enable safe integration.

1) Introduction

Rising expectations for 24/7 reachability and high FCR collide with staffing shortages and peaks. Mondial AI addresses this with a low‑latency pipeline, domain intelligence, and agentic orchestration that executes processes end‑to‑end (e.g., booking, document delivery, callbacks, follow‑ups).

Objectives

Reduce ASA/abandonment; increase FCR; raise self‑service and callbacks; quality & safety via structured capture, triage and HITL; GDPR/ISO 27001; optimize TCO via edge inference and smart routing.

2) Related Work & Positioning

IVR/voicebots 1.0: rigid menus; generic LLM bots: flexible yet risky without guardrails. Mondial AI combines provable rules + calibrated models, terminology mapping (e.g., SNOMED/ICD in healthcare), HITL, and functional tool‑calling with audit trails.

3) System Architecture

Components: telecom gateway (SIP/WebRTC), streaming ASR (< 250 ms), domain NLU, dialog/policy manager with guardrails, triage/safety engine, agentic orchestration (saga/outbox/idempotency), backend integration (FHIR/REST/GraphQL), security/privacy, observability.

Latency targets: ASR ≤ 250 ms; NLU+policy ≤ 120 ms; triage ≤ 80 ms; backend I/O ≈ 100–150 ms; end‑to‑end < 600 ms.

4) Methods

Automatic Speech Recognition (ASR)

Streaming transducer with rescoring and endpointing < 300 ms; robustness via RNNoise/NSNet2, accent adaptation, SpecAugment/speed perturbation; metrics: WER, RTF < 1.0.

Natural Language Understanding (NLU)

Intents via transformer cross‑encoder (focal loss); slots via sequence tagging; terminology mapping; confidence calibration (Platt/Isotonic); HITL thresholds.

Dialog Policy & Guardrails

Structured capture; safe tool‑calling (e.g., createAppointment/sendConfirmation); safety kernel with allow/deny lists and domain constraints.

Triage & Safety

Red flags with immediate escalation; risk models (logistic regression/GBTs); goal: high sensitivity, PPV via HITL; adaptive thresholds to avoid alarm fatigue.

Agentic Workflows

Example: intake → triage → eligibility → slot search → booking → confirmation → reminder → follow‑up; transactions via saga + idempotency keys; delivery via outbox/event sourcing.

Interoperability

Healthcare: HL7 FHIR (Patient, Appointment, Schedule, Slot, Communication, Coverage); CRM/ERP: REST/GraphQL, webhooks, CDC.

5) Queueing & Capacity Modeling

M/M/c with Prioritization

ρ = λ/(cμ); with self‑service s and callback k: λ' = λ (1−s−k). Example: λ=150/h, μ=40/h, c=4 ⇒ ρ=0.94 (critical). With s=0.35, k=0.25 ⇒ λ'=60 ⇒ ρ'=0.375; ASA −72%, abandonment −61%.

6) Security, Privacy, Compliance

Data minimization; pseudonymization; TLS 1.3/AES‑256; RBAC/ABAC; audit trails; DPIA, incident response ≤ 72 h; fairness monitoring.

7) Evaluation Design & KPIs

Service: ASA, AHT, FCR, abandonment. Quality: WER, intent/slot F1, triage sens/spec. Economics: cost/contact, utilization, no‑show, ROI. Experience: CSAT, NPS, PES. Study designs: stepped‑wedge cluster RCT or DiD (with power examples).

8) Results (Simulation, Example)

| Szenario | ASA (s) | Abandon (%) | FCR (%) | No‑Show (%) | Cost/Contact (€) | Jahres‑ROI |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| Basis (ohne System) | 210 | 25 | 55 | 12 | 4,40 | — |
| Mondial AI (S=35 %, K=25 %) | 60 | 10 | 77 | 9 | 3,10 | 1,6× |
| Mondial AI + adaptive Reminder | 52 | 8 | 80 | 7 | 2,95 | 1,9× |

ROI formula: (benefit − cost) / cost. Example arithmetic yields ~1.7× annual ROI.

9) Reliability (SRE) & TCO

SLOs: 99.9% availability, P95 < 800 ms, booking errors < 0.5%; autoscaling; observability with OpenTelemetry; TCO levers: edge inference, FHIR caching, audio compression, model tiering.

10) Limits & Risks

Quality loss at 8 kHz; NLU bias; legacy integrations; regulatory constraints. Mitigations: 16 kHz paths, fairness metrics, adapters/canaries/rollbacks, transparency and human fallback.

11) Rollout Roadmap

1) Process discovery & KPI baseline → 2) Integration phase 1 (read‑only) & monitoring → 3) Pilot (low‑risk) → 4) Expansion (auto‑booking, safe triage with HITL) → 5) Formal evaluation (RCT/DiD) & security audit → 6) Scale.

Conclusion

Mondial AI unifies speed, safety, and orchestration in an auditable platform, simultaneously improving access, safety, experience, and economics.

Appendix A: KPI Definitions

ASA, AHT, FCR, abandonment, WER, CSAT/NPS/PES, ROI/cost‑per‑contact.

Appendix B: Pseudocode (Safety/Triage)

if red_flag_detected(utterance, slots):
    escalate_to_human(within_seconds=10)
    mark_case(priority="critical")
else:
    risk = triage_model.predict(slots)
    if risk > tau_high:
        escalate_to_human()
    elif tau_low < risk <= tau_high:
        proceed_with_booking_and_confirmation()
    else:
        provide_self_service_information()

Appendix C: Standards & Interfaces

Healthcare: HL7 FHIR (Patient, Appointment, Schedule, Slot, Communication, Coverage). Auth: OAuth2/SMART on FHIR, RBAC/ABAC. Telephony: SIP, WebRTC, DTMF fallback. Observability: OpenTelemetry, structured logs, metrics/tracing.