Enterprise Voice AI Orchestration Guide

TL;DR

Enterprise voice AI procurement joins architecture, integration, governance, security evidence, and operational accountability. Self-serve infrastructure can fit teams able to build and run those layers; managed delivery can consolidate more responsibility in one engagement. Trillet offers cloud, private-cloud, and Docker-based on-premise options where scoped in the applicable agreement.

Large organizations evaluating voice AI face a fundamentally different decision matrix than SMBs or agencies. The questions shift from "does it work?" to "does it meet our security requirements?", "can it integrate with our legacy telephony?", and "who's accountable when it fails?" This guide examines enterprise voice AI architecture, deployment models, compliance requirements, and vendor evaluation frameworks for IT leaders, security teams, and operations executives.

To discuss enterprise voice AI requirements including on-premise deployment, data residency, and custom SLAs, contact the Trillet Enterprise team.

The Enterprise Voice AI Landscape

Enterprise voice AI differs from consumer solutions in deployment model, accountability structure, and integration complexity, not just scale.

The voice AI market has matured rapidly. What began as basic IVR replacement has evolved into sophisticated conversational AI capable of handling complex, multi-turn interactions. However, enterprise adoption has lagged consumer and SMB segments, primarily due to three structural barriers:

Deployment constraints: Hosted, hybrid, and on-premise options create different data paths and operating responsibilities. Internal policy or risk requirements can be stricter than the law itself.
Integration complexity: Enterprise telephony environments involve legacy PBX systems, SIP trunking arrangements, contact center platforms (Genesys, NICE, Avaya), and CRM integrations (Salesforce, ServiceNow) that self-serve platforms aren't designed to accommodate.
Accountability gaps: When voice AI fails in an enterprise context (misrouting emergency calls, exposing PHI, or violating compliance requirements) organizations need contractual accountability, not community forums.

Market Segmentation

Segment	Typical Solution	Deployment	Accountability
SMB	Self-serve SaaS	Cloud only	Best-effort support
Agency/Reseller	White-label platform	Cloud only	Platform ToS
Mid-Market	Managed SaaS	Cloud, some hybrid	SLA-backed
Enterprise	Managed, self-built, or integrated service	Cloud, hybrid, on-premise	Contract- and operating-model-dependent

These are common market patterns, not universal rules; buyers should evaluate the actual deployment and support terms of each product.

Trillet Enterprise occupies the managed-service segment. Trillet can lead design, build, deployment, and ongoing management, while the customer retains governance, approvals, access, infrastructure, and other responsibilities defined in the engagement. SLA commitments apply only where signed.

Deployment Architecture Options

Voice AI deployment models involve trade-offs between control, cost, compliance, and operational complexity.

Cloud Deployment

Architecture: Voice AI processing occurs entirely in vendor-managed cloud infrastructure. Customer data transits to and processes in vendor's cloud environment.

Advantages:

Fastest time to deployment
No infrastructure management
Automatic updates and improvements
Lowest upfront cost

Limitations:

Data leaves organizational boundary
Limited control over processing location
Dependency on vendor's infrastructure decisions
May not satisfy data residency requirements

Best fit: Organizations without strict data residency requirements, those prioritizing speed over control.

Hybrid Deployment

Architecture: Some components (telephony termination, sensitive data processing) remain on-premise while AI inference occurs in cloud. Data redaction happens before cloud transmission.

Advantages:

Selected sensitive data paths can remain on premises
Reduced latency for local components
Satisfies some compliance frameworks
Balances control with managed services

Limitations:

More complex architecture
Requires some on-premise infrastructure
Split responsibility model
Higher implementation cost than pure cloud

Best fit: Organizations with partial data residency requirements or specific fields requiring local processing.

On-Premise Deployment

Architecture: The agreed voice application and selected processing components run within customer-controlled infrastructure. A complete local stack can be scoped, but buyers must verify whether telephony, models, support, analytics, or updates create external paths.

Advantages:

Direct control over the agreed deployment boundary
Can support stringent internal policies
Can reduce external data transmission
Full infrastructure control

Limitations:

Higher infrastructure requirements
Longer implementation timeline
Requires more internal expertise (unless managed)
Update cycles tied to deployment schedule

Best fit: Regulated industries, government, organizations with air-gapped requirements.

Trillet's On-Premise Capability

Trillet supports deployment of its voice application layer via Docker as a scoped enterprise option. This distinction matters:

The label "on-premise" can describe a local connector, a local application layer, or a complete local processing path. A Trillet engagement can define which of the following components run within customer infrastructure:

Speech-to-text processing
LLM-based conversation handling
Text-to-speech synthesis
Call routing and management
Analytics and reporting

The target environment, model path, network requirements, update process, and any air-gap constraints must be validated during architecture review and written into the applicable agreement. For detailed guidance, see On-Premise Voice AI Deployment via Docker and Choosing Between Cloud, Hybrid, and On-Premise Voice AI.

Data Residency and Privacy Controls

Enterprise voice AI must address where data is processed, how long it's retained, and what controls exist for sensitive information.

Configurable Data Residency

Trillet Enterprise can support configurable data residency across APAC, North America, and EMEA, subject to the selected deployment and agreement:

Region	Example location objective	Contract items to confirm
APAC	Australian or regional processing	Storage, inference, support access, subprocessors, transfers
North America	US or Canadian processing	HIPAA/BAA where applicable, state/provincial rules, subprocessors
EMEA	EEA or UK processing	Controller/processor roles, transfer mechanism, subprocessors

Data residency configuration determines where:

Call recordings are stored (if retained)
Transcripts are processed and stored
AI model inference occurs
Analytics data resides

For multinational deployments, region-specific routing can be scoped. The agreement should distinguish storage location from every processing and support path rather than treating a region label as a complete compliance answer. For detailed regional requirements, see Voice AI Data Residency Requirements by Region and Configurable Data Residency for Voice AI: APAC, EMEA, and North America Options.

PII and PHI Handling

Voice conversations can contain sensitive information. Trillet Enterprise can provide the following handling options where selected and documented in the engagement:

Option 1: No Storage Configure the agreed application path not to persist recordings or transcripts after the interaction. Confirm logs, model providers, telephony, backups, and other subprocessors separately before describing the whole system as zero storage.

Use case: Maximum privacy posture, scenarios where retention creates liability.

Option 2: Redacted Storage Where enabled, conversations can be transcribed and stored with configured PII/PHI detection and redaction before persistence. Detection is not perfect, so test the required entities and pair redaction with minimization, access, and retention controls. Profiles can cover:

Names and identifiers
Phone numbers
Email addresses
Account numbers
Social Security Numbers
Medical record numbers
Credit card numbers
Dates of birth

Use case: Need conversation records for quality assurance without storing identifying information.

Option 3: Full Storage with Access Controls Complete recordings and transcripts retained with role-based access controls, encryption at rest, and audit logging.

Use case: Compliance requirements mandating call retention, dispute resolution needs.

Option 4: Customer-Managed Storage Where agreed, the application can route recordings or transcripts to customer-controlled storage. Validate the destination, format, encryption, metadata, retry behavior, and whether any other copy persists.

Use case: Organizations requiring direct control over the agreed storage destination and access model.

For detailed guidance on data handling, see Voice AI PII and PHI Handling Best Practices and Voice AI Data Redaction and Privacy Controls.

Data Isolation

For organizations requiring strict tenant isolation, Trillet Enterprise can scope controls such as:

Dedicated infrastructure: Separate compute instances per customer
Network isolation: Customer-specific VPCs/subnets
Encryption key management: Customer-managed keys (BYOK)
Database isolation: Dedicated database instances

Compliance and Security

Enterprise voice AI must satisfy regulatory frameworks, pass security audits, and integrate with existing governance structures.

Compliance Certifications

Trillet holds SOC 2 Type II and ISO 27001. Other frameworks below describe customer obligations, assessment paths, or contractual controls rather than additional Trillet certifications:

United States:

SOC 2 Type II: Trillet holds a current report; confirm scope and access during diligence
HIPAA: A BAA is available on Agency and Enterprise after the standard process, and PHI processing also requires the applicable Order Form
GLBA: Financial institutions must assess the proposed system and contracts against their own Safeguards Rule duties

Australia:

APRA CPS 234: APRA-regulated entities remain accountable for information-security controls and service-provider risk
IRAP: Assessors can assess a system against applicable Australian Government controls; IRAP does not certify, accredit, endorse, or grant authority to operate
Privacy Act 1988: The customer must assess Australian Privacy Principle obligations for its workflow and disclosures

International:

GDPR: Controller/processor roles, legal basis, transparency, minimization, retention, and transfer safeguards must be addressed for the workflow
ISO 27001: Trillet holds information-security management certification

For compliance deep-dives, see HIPAA Compliant Voice AI for Healthcare Enterprises, Voice AI for Financial Services Compliance: SOC 2 and GLBA, Voice AI for Australian Enterprises: APRA CPS 234 and IRAP Compliance, and Voice AI for Regulated Industries.

Security Architecture

Treat security architecture as a diligence checklist, not a set of default entitlements. Confirm and document the controls required for the proposed deployment:

Encryption and keys: protocols for data in transit and at rest, key ownership, rotation, and any customer-managed-key requirement
Authentication and access: SSO protocol, MFA, RBAC, privileged access, IP restrictions, and access-review cadence
Network security: private connectivity or VPN needs, segmentation, firewalling, DDoS controls, and administrative access paths
Audit and monitoring: event coverage, retention, SIEM export, alerting, incident response, and customer access to evidence

Exact controls, versions, integrations, and responsibilities are engagement-specific and should appear in the architecture, security addendum, Order Form, or SOW.

Security Audit Preparation

Trillet holds SOC 2 Type II and ISO 27001. Access to reports, penetration-test evidence, questionnaires, architecture diagrams, data-flow documentation, and dedicated security support is provided where agreed and may be subject to confidentiality terms. Penetration testing, including any CREST requirement, must be confirmed for the engagement rather than assumed as a default entitlement. For a comprehensive guide to audit readiness, see Enterprise Voice AI Security Audit Preparation.

Integration Architecture

Enterprise voice AI must connect with existing telephony infrastructure, business systems, and workflow tools.

Telephony Integration

SIP trunking can connect the voice application to an approved carrier or enterprise edge. Confirm codec, authentication, number ownership, media path, failover, emergency-call handling, and support boundaries for the chosen carrier.

PBX integration may use SIP trunks, CTI adapters, or platform-specific interfaces. Common estates to evaluate include Cisco CUCM, Avaya, Mitel, Asterisk/ViciDial, Microsoft Teams, RingCentral, and 8x8. Inclusion in this list is not a promise of a prebuilt connector; compatibility and custom work must be verified for the proposed versions and topology.

Contact-center integration may support patterns such as call deflection before queue, AI-to-human handoff, agent assist, and post-call automation. If the estate includes Genesys, NICE, Five9, Amazon Connect, Talkdesk, or another CCaaS, require a design showing the exact APIs, media path, context transfer, failure modes, and operating owner.

Business System Integration

Business-system integrations are scoped to the interfaces, permissions, licensing, workflow, and security rules available to the customer. Common targets include CRM and service platforms such as Salesforce, Dynamics 365, and ServiceNow; Microsoft or Google calendars; and healthcare systems such as Epic or Oracle Health using approved APIs or HL7/FHIR where available. These are examples to evaluate, not universal native connectors.

REST APIs, webhooks, middleware, file exchange, or other approved patterns can connect proprietary systems. The SOW should identify each integration, direction of data flow, least-privilege access, error handling, testing, ownership, and maintenance.

Legacy System Integration

Many enterprises operate legacy systems without modern APIs. Depending on risk and available interfaces, a scoped design may use approved database access, file exchange, middleware, robotic interaction, or a CTI bridge. These methods are not default entitlements and some may be inappropriate under the customer's security policy. Trillet can lead agreed integration work, while the customer provides system owners, access, approvals, test data, and change control. For integration approaches, see Voice AI Legacy System Integration Approaches and Voice AI Integration with Legacy CRM and Telephony Systems.

Managed Service Model

Trillet Enterprise operates as a fully managed service, distinct from self-serve platforms that require internal engineering resources.

What "Managed" Means

Responsibility	Self-Serve Platform	Trillet Enterprise
Solution design	Customer	Trillet
Integration development	Customer	Trillet
Agent configuration	Customer	Trillet
Testing and QA	Customer	Trillet
Deployment	Customer	Trillet
Monitoring	Customer	Trillet
Optimization	Customer	Trillet
Incident response	Customer	Trillet

Vendor-led technical delivery: Trillet's solution architects can design the implementation, build agreed integrations, and manage ongoing performance. The customer still supplies business owners, security and legal review, system access, approvals, testing input, and escalation contacts. Learn more: Zero Engineering Lift Voice AI Implementation and Managed vs Self-Serve Voice AI Platforms Comparison.

Implementation Process

Discovery

Business requirements gathering
Technical architecture assessment
Integration requirements mapping
Compliance requirements documentation
Success metrics definition

Design

Solution architecture design
Integration specifications
Agent conversation design
Testing plan development
Security review

Build

Integration development
Agent configuration
Test environment deployment
Internal testing

Deploy

Production deployment
Integration testing
User acceptance testing
Go-live preparation
Cutover execution

Ongoing: Operate

Monitoring on the coverage schedule agreed in the engagement
Performance optimization
Regular business reviews
Continuous improvement

Complex implementations are commonly planned around six to eight weeks where that delivery window is approved for the engagement. Security review, access, integrations, data migration, and customer approvals can change it materially.

Support Model

Support coverage: Onshore Australian monitoring and 24/7 coverage can be included where specified in the signed support schedule.

Account management: A named account manager can be included for strategic discussions, reviews, and escalation where specified.

Technical account support: Complex deployments can include a dedicated technical resource where specified.

Dedicated support and response times: Enterprise includes dedicated support. SLAs and response times are agreed per engagement and written into the signed agreement; Trillet does not publish standard response or resolution targets.

Service Level Agreements

Enterprise deployments require contractual commitments, not marketing promises.

Uptime Guarantee

Trillet Enterprise can provide a financially backed 99.99% uptime SLA where included in the signed agreement.

Mathematically, 99.99% availability corresponds to about 52.6 minutes of annual unavailability before contractual exclusions. The signed SLA must define the measurement period, service boundary, dependencies, maintenance, exclusions, reporting, credits, and other remedies; none of those specifics should be inferred from the percentage alone.

The agreement must define the measured service, exclusions, dependencies, maintenance, reporting period, calculation, and remedy. Do not assume a service-credit schedule that is not in the signed SLA.

For detailed SLA requirements and expectations, see Voice AI 99.99% Uptime SLA Requirements.

Performance SLAs

Beyond availability, buyers can negotiate measurable targets for latency, task accuracy, call completion, handoff success, and integration health. Define test conditions, percentile, language, audio quality, exclusions, reporting, and remedies in the agreement rather than treating generic benchmarks as universal commitments.

Contract Structure

Enterprise contracts are negotiated per engagement and may include:

Term: negotiated duration and renewal terms
Pricing: Custom schedule based on the agreed scope and usage model
SLAs: Customized based on requirements
Support level: Tiered based on criticality
Exit provisions: Data export, transition assistance

Vendor Evaluation Framework

Selecting enterprise voice AI requires structured evaluation across technical, operational, and commercial dimensions.

Technical Evaluation Criteria

1. Deployment Flexibility

Cloud-only vs. hybrid vs. on-premise options
Data residency configurations
Infrastructure requirements for each model

2. Integration Capabilities

Native integrations with your existing systems
API completeness and documentation
Custom integration approach and timeline

3. AI Quality

Speech recognition accuracy (test with your audio samples)
Conversation handling sophistication
Voice synthesis naturalness
Language and accent support

4. Security Architecture

Encryption standards
Authentication options
Network security capabilities
Audit logging completeness

Operational Evaluation Criteria

1. Implementation Approach

Self-serve vs. managed vs. hybrid
Implementation timeline
Resource requirements (internal vs. vendor)
Training and enablement

2. Support Model

Support hours and coverage
Escalation procedures
Response time commitments
Proactive vs. reactive support

3. Ongoing Management

Monitoring capabilities
Optimization approach
Update and maintenance process
Business review cadence

Commercial Evaluation Criteria

1. Pricing Model

Per-minute vs. per-seat vs. flat fee
Committed vs. usage-based
Overage handling
Price protection over term

2. Contract Terms

Minimum commitment
Termination provisions
Data portability
SLA enforceability

3. Total Cost of Ownership

Vendor fees
Internal resource requirements
Integration costs
Ongoing operational costs

Evaluation Process

Phase 1: Requirements Definition Document technical requirements, compliance needs, integration scope, and success metrics before vendor engagement.

Phase 2: RFI/RFP Structured information gathering from shortlisted vendors. Include specific scenarios and use cases.

Phase 3: Technical Proof of Concept Hands-on evaluation with production-representative scenarios. Test integrations, measure performance, validate security controls.

Phase 4: Reference Checks Speak with existing customers in similar industries with comparable requirements.

Phase 5: Commercial Negotiation Negotiate terms, SLAs, and pricing based on evaluation findings.

For a comprehensive evaluation framework, see Enterprise Voice AI Vendor Evaluation Framework.

Industry Applications

Enterprise voice AI applications vary by industry vertical, each with specific requirements and use cases.

Healthcare

Use Cases:

Patient appointment scheduling and reminders
Prescription refill requests
Insurance verification
Post-discharge follow-up
Administrative intake and routing to approved clinical or nurse-line staff; no AI clinical triage or advice

Requirements:

Executed BAA and applicable Order Form for HIPAA-regulated processing
EHR integration (Epic, Cerner)
PHI handling controls
After-hours coverage for clinical concerns

Learn more: HIPAA Compliant Voice AI for Healthcare Enterprises

Legal

Use Cases:

After-hours new-matter intake
Client and caller screening for conflicts
Appointment and consultation scheduling
Status updates on active matters
Overflow coverage during peak call periods

Requirements:

Audit trails for the events and interactions required by the agreed control design
Role-based access to matter and client data
Data residency and confidentiality controls
Integration with practice-management systems

Learn more: Trillet for legal teams

Financial Services

Use Cases:

Account balance and transaction inquiries
Payment processing and reminders
Fraud alert verification
Loan application status
Branch appointment scheduling

Requirements:

SOC 2 Type II, GLBA compliance
Core banking integration
PCI DSS for payment handling
Call recording and retention

Learn more: Voice AI for Financial Services Compliance: SOC 2 and GLBA

Government

Use Cases:

Citizen service inquiries
Appointment scheduling for services
Status updates on applications
Approved service-status information and immediate routing of emergency or life-safety calls to people
Multi-language support

Requirements:

The applicable government security authorization or assessment path; IRAP itself is not certification or authority to operate
On-premise deployment capability
Accessibility compliance (Section 508)
Data sovereignty

Utilities

Use Cases:

Outage reporting and status updates
Billing and account inquiries
Service scheduling and connections
Handling seasonal call-volume spikes
After-hours emergency line coverage

Requirements:

Concurrency to absorb spike-driven volume
Integration with billing and CIS platforms
Data residency and audit controls
High-availability SLAs

Learn more: Trillet for utilities

Automotive

Use Cases:

Service and appointment scheduling
Parts and service department inquiries
Lead capture for dealership groups
OEM and fleet coordination
After-hours and overflow answering

Requirements:

Multi-location and franchise support
Integration with DMS and CRM systems
Call routing across departments
Reporting across locations

Learn more: Trillet for automotive

Contact Centers

Use Cases:

Call deflection for routine inquiries
AI-assisted agent support
After-hours automation
Callback scheduling
Quality assurance automation

Requirements:

Contact center platform integration
Real-time agent handoff
Skills-based routing
Workforce management integration

Learn more: Call Center AI Automation Managed Services and Voice AI in Customer Service: Transforming the Contact Center Experience

Frequently Asked Questions

What makes enterprise voice AI different from SMB solutions?

Enterprise solutions differ in three fundamental ways: deployment flexibility (including on-premise options), integration depth (connecting with legacy systems and enterprise platforms), and accountability structure (contractual SLAs with financial guarantees rather than best-effort support). The technology may be similar, but the implementation model, support structure, and commercial terms are designed for enterprise requirements.

How do I get started with enterprise voice AI?

Contact the Trillet Enterprise team to discuss your requirements. The team will assess infrastructure, compliance needs, integrations, and deployment preferences. Complex deployments are commonly planned around six to eight weeks where that window is approved; the actual schedule depends on scope and customer readiness.

How does on-premise deployment work technically?

Trillet's on-premise option uses Docker containers to run the agreed voice application and selected processing components within customer infrastructure. The architecture can include speech-to-text, model inference, text-to-speech, and call management, but the signed design must identify what is local and what remains external. Infrastructure and update procedures are sized to the approved scope.

What's the typical implementation timeline?

Complex enterprise deployments are commonly planned around six to eight weeks where that window is approved. Discovery, security review, access, integrations, testing, and customer approvals can move the date. Trillet leads the technical delivery, while the customer still provides owners, access, review, decisions, and testing input.

How does Trillet handle legacy system integration?

Trillet's managed service approach includes custom integration development. For legacy systems lacking modern APIs, we implement screen scraping, database integration, file-based integration, or custom middleware as needed. Integration complexity is absorbed by Trillet's engineering team rather than requiring internal resources.

What happens if the AI makes a critical error?

Enterprise deployments include multiple safeguards: confidence thresholds for automated handling, mandatory transfers for specific scenarios, real-time monitoring with alerting, and human escalation paths. When issues occur, they're covered by contractual SLAs with defined response times. Post-incident, root cause analysis and remediation are managed by Trillet's operations team.

Can we start with cloud and move to on-premise later?

Potentially. A cloud-to-hybrid or on-premise path must be designed for the selected telephony, models, integrations, data, and controls. Portability and migration work should be stated in the proposal and SOW rather than assumed to be included.

Conclusion

Enterprise voice AI deployment requires a fundamentally different approach than SMB solutions, one that addresses deployment flexibility, integration complexity, compliance requirements, and contractual accountability.

Trillet Enterprise offers managed delivery, Docker-based on-premise and private-cloud options, configurable residency, SOC 2 Type II and ISO 27001, and contract-scoped SLA and security commitments. Customer governance and implementation responsibilities remain part of the operating model.

The decision isn't whether voice AI can handle enterprise use cases; the technology has matured to that point. The decision is which vendor can deploy it within your constraints, integrate it with your systems, and stand behind it with contractual commitments.

Ready to evaluate Trillet Enterprise for your organization? Contact Trillet Enterprise to discuss your requirements, request architecture documentation, or schedule a technical deep-dive with our solution architects.

Updated for July 2026: Corrected the enterprise contact URL to /contact-sales (the /enterprise path now redirects there), fixed the ISO 27001 certification status (held, not in progress), removed the deprecated HubSpot CRM integration, and expanded Industry Applications to cover all six enterprise flagship verticals (adding legal, utilities, and automotive).

Updated for September 2026: removed unsupported exclusivity and universal entitlement claims; qualified residency, on-premise boundaries, timelines, support, security, HIPAA, APRA, IRAP, and SLA terms by deployment and contract; replaced the illustrative support response-time table with the rule that SLAs and response times are agreed per engagement.

Voice AI by Sector

The Industry Applications section above summarizes each vertical. These guides go deeper on one sector at a time: the real call types, the systems that have to integrate, and the compliance regime each industry answers to.

Voice AI for automotive dealership groups and service networks covers DMS and CRM integration, multi-location routing, and after-hours service scheduling across franchises.
The government agencies and councils deployment guide walks through IRAP, data sovereignty, accessibility, and on-premise options for citizen services.
Voice AI for law firms and legal operations details after-hours matter intake, conflict screening, and the audit trails legal work demands.
Voice AI for utilities, energy, and water providers addresses outage-driven volume spikes, billing and CIS integration, and high-availability coverage.

Compliance and Channel Decisions

Two questions cut across every enterprise deployment regardless of sector: which regulatory obligations the platform itself has to carry, and whether voice is even the right channel for a given interaction.

Voice AI and APRA CPS 230 explains what the operational-resilience standard expects from third-party AI vendors, which matters for any APRA-regulated buyer.
Voice AI vs text AI in customer service lays out where each channel wins, so you route interactions to the one that fits.

Contact Centre and Existing Telephony Estate

Most enterprise deployments do not start from a blank slate. There is already a PBX, a dialler, a queue structure, and a set of reported metrics that the business runs on. These guides cover what it takes to put voice AI in front of that estate rather than replacing it.

Start with managed voice AI for contact centres, which explains why a managed rollout skips the six-month implementation that self-serve platforms push onto your team.
The decision underneath it is covered in enterprise voice AI build vs buy, a real cost comparison of assembling your own stack against buying a managed one, and the eight contact-centre platforms compared if you are running a formal vendor shortlist.
For the integration work itself: Avaya PBX integration, Cisco CUCM deployment, and ViciDial integration for call centres each walk through the SIP, routing, and failover specifics for that platform.
Once live, contact-centre KPIs for voice AI defines how handle time, CSAT, and first-call resolution should actually be measured so the numbers survive scrutiny.

Last updated: September 2026