Legal Entity
Confirm the contracting entity, ownership, jurisdiction, office locations and authorized signatories.
A practical framework for evaluating whether a Voice AI vendor can support real enterprise operations, custom integrations, regulated workflows and long-term accountability—not merely deliver a polished demonstration.
Voice AI sits between callers, telephone systems, business rules, staff teams and systems of record. A vendor can sound impressive while still being poorly equipped to manage production integrations, sensitive data, failure recovery, call transfers, audit requirements or ongoing operational change.
Effective due diligence examines the full delivery model: who owns each component, what is subcontracted, how workflows are built, how incidents are handled, where data moves, how changes are approved and how the organization can exit without losing control of its numbers, logic or records.
This framework complements Peak Demand’s Voice AI procurement and RFP requirements resources.
A strong technical demonstration does not replace basic corporate, financial and contractual diligence.
Confirm the contracting entity, ownership, jurisdiction, office locations and authorized signatories.
Assess funding, revenue stability, insurance, concentration risk and ability to support the expected contract term.
Request customers with comparable call volume, workflow complexity, integrations and operating risk.
Identify platforms, carriers, model providers, consultants and processors involved in delivery.
Clarify ownership of prompts, workflow logic, middleware, reports, training materials and custom code.
Review relevant coverage, contractual limitations, indemnities and remedies for material failure.
Require vendors to label what exists today, what requires custom development and what depends on another provider.
Features that are production-ready, documented and supported under the proposed agreement.
Capabilities requiring middleware, APIs, bespoke logic, engineering or customer-owned infrastructure.
Future capabilities that should not be treated as committed unless contractually defined.
Speech, language models, telephony, hosting, analytics and integrations supplied by others.
Concurrency, languages, transfer types, latency, tool limits, context limits and unsupported workflows.
What happens when an upstream API, model, carrier, connector or authentication service changes.
The vendor should provide an architecture diagram showing telephony entry, call routing, orchestration, speech services, language models, middleware, databases, integrations, logs, reporting and external dependencies.
The review should identify responsibility boundaries, environment separation, regional dependencies, capacity assumptions, observability, failover and the behaviour of the service when one component is unavailable.
Use Peak Demand’s enterprise Voice AI infrastructure framework to structure the technical review.
Integration diligence should go beyond a list of logos. Ask how the vendor authenticates, reads, writes, validates, retries, prevents duplicates, reconciles failures and tests against real customer systems.
Confirm whether integrations are native, partner-built, customer-built or delivered through custom infrastructure. Require a clear division of responsibility for API changes, credentials, monitoring, support and data mapping.
Do not accept broad assurances without understanding which systems, services and subcontractors are actually covered.
Review the supporting Voice AI security, audit logs and traceability, and incident response and continuity frameworks.
The vendor should be able to explain the complete lifecycle of transcripts, recordings, identifiers, prompts, system responses and operational logs.
Data elements captured, purposes, optional fields and prohibited content.
Systems, providers, regions and subprocessors that receive or process information.
Default periods, customer controls, backups, logs and deletion timelines.
Training, product improvement, analytics and any use beyond delivering the contracted service.
Use the Voice AI privacy page as a companion review guide. Requirements should be tailored to the organization’s actual legal, contractual and operational obligations.
Production Voice AI requires defined ownership, approval paths, version control, testing, rollback and human intervention. Ask how the vendor prevents unreviewed changes from reaching callers and how material decisions can be reconstructed later.
Confirm who may change prompts, policies, knowledge, integrations, routing, voices and escalation rules. Ask which sensitive actions require human review and how overrides are recorded.
Quality assurance should cover workflow outcomes, policy compliance, system actions, edge cases and caller experience.
Normal, ambiguous, incomplete, adversarial and exception scenarios using representative data.
Authentication, field mapping, retries, duplicate prevention, timeouts and reconciliation.
Audio conditions, interruptions, corrections, transfers, pacing, accents and caller behaviour.
Repeatable tests after prompt, workflow, integration, provider or model changes.
Representative speech differences, cognitive load, hearing conditions and human alternatives.
Measurable pass conditions, defect severity, remediation and sign-off responsibilities.
The operating model should explain who watches the system, who responds and what happens outside normal business hours.
Availability, latency, failed calls, integration errors, queue health and unusual outcomes.
Hours, channels, severity definitions, response targets, escalation paths and named ownership.
Detection, containment, communication, recovery, root cause and corrective action.
Fallback routing, transfer alternatives, manual procedures, backups and recovery expectations.
Sampling, scorecards, error trends, policy adherence, caller experience and remediation.
Requests, approvals, testing, release notes, rollback and post-release verification.
Due diligence should explore pacing, repetition, interruption, correction, speech differences, hearing and audio conditions, language support, cognitive load and alternate service channels.
Ask how callers reach a person when the automated path is unsuitable. Review Peak Demand’s Voice AI accessibility and inclusive design framework for detailed evaluation areas.
Low usage pricing can hide implementation, telephony, integration, support, storage and change costs.
Exit planning should be completed before contract signature. Confirm whether the organization controls telephone numbers, recordings, transcripts, analytics, prompts, workflow definitions, knowledge, integration code, credentials and operational documentation.
Require practical export formats, transition assistance, deletion confirmation and timelines that allow service continuity during a vendor change.
Weighting should reflect the organization’s operating risk, integration complexity and service expectations.
Current capabilities, limitations and workflow alignment.
Technical design, scalability, resilience and dependencies.
Controls, data handling, evidence and responsibility boundaries.
Engineering competence, testing and long-term maintenance.
Monitoring, support, incident response, continuity and QA.
Human oversight, approvals, traceability and change control.
Total cost, flexibility, scalability and contractual fairness.
References, financial capacity, team quality and transparency.
Warning signs include refusal to disclose dependencies, vague ownership, universal capability claims, no architecture diagram, no support escalation, weak failure handling and pricing that cannot be tied to expected use.
A vendor should be willing to describe limitations, explain where custom engineering is required and identify what happens when the service or an upstream dependency fails.
Peak Demand helps regulated-industry, public-sector and enterprise teams assess Voice AI vendors, infrastructure, implementation risk and long-term operating fit.