Hybrid workforce platform evaluation: Key features and criteria for regulated industries
Hybrid workforce platform evaluation requires a weighted framework prioritizing EU AI Act compliance, integration, and governance.
TL;DR: Evaluating a hybrid workforce platform in a regulated industry requires a weighted framework. This guide recommends the following weightings: EU AI Act compliance (25%), integration depth (20%), proven results in regulated sectors (20%), transparent governance (15%), true 24-month TCO (10%), and vendor viability (10%). Contact centers in regulated industries and faster-moving verticals alike must demand documented audit trails, on-premise deployment options, and an active human-in-the-loop command layer to satisfy EU AI Act Articles 13, 14, and 50. Any vendor that cannot produce a SOC 2 Type II report, a GDPR data processing agreement template, and a glass-box architecture diagram should fail your evaluation before the demo.
Most CX leaders obsess over deflection rates while ignoring the black-box architecture that causes their legal team to shut the pilot down. In regulated contact centers, the most common AI deployment failure isn't poor conversational performance. It's the inability to explain why the AI said what it said. When compliance asks for a decision audit trail and the vendor can't produce one, the deployment stops. The AI's deflection rate becomes irrelevant at that point.
Your CFO demands measurable cost reduction while your compliance team won't approve any AI system it can't audit. This guide provides a weighted evaluation framework to assess hybrid workforce platforms. The six criteria that matter: compliance architecture, integration depth, proven outcomes, transparent governance, total cost of ownership, and vendor staying power.
#Frameworks: Ensuring regulated AI compliance
You must give compliance the heaviest weight in your evaluation model because the penalty for getting it wrong isn't a poor CSAT score, it's a regulatory fine that ends careers and makes headlines. The EU AI Act penalties for prohibited AI uses reach up to €35 million or 7% of global annual turnover, whichever is higher.
#EU AI Act fines: Banking & insurance
The EU AI Act explicitly classifies AI systems that influence credit decisions, insurance underwriting, or healthcare eligibility as high-risk under Annex III. For high-risk violations, fines reach up to €15 million or 3% of annual turnover, whichever is higher. For a European bank with €5 billion in annual revenue, 3% of turnover equals €150 million, a figure that dwarfs any cost savings from automation and sits entirely within the regulator's enforcement range. As GetVocal's conversational AI guide for regulated industries details, the compliance burden is not incidental to the AI deployment decision. It is the decision.
#High cost of poor platform fit
Rip-and-replace failures waste budget, destroy internal political capital for future AI proposals, and force your team to re-engineer every conversation flow already built. A vendor migration means rebuilding Context Graph logic from scratch, retraining agents, and revalidating every compliance artifact. Choosing wrong the first time doubles your total cost and adds months to your timeline.
#Due diligence for regulated AI
Before any demo, require these four artifacts from every vendor:
- SOC 2 Type II report covering a minimum six-month operating effectiveness period. A Type II report attests to how controls performed over time, not just that they exist. A Type I report is insufficient for regulated procurement. Industry practice treats SOC 2 Type II reports as valid for 12 months from issuance.
- GDPR data processing agreement (DPA) satisfying GDPR Article 28(3) minimum content requirements, including subject matter, duration, nature, purpose, and categories of personal data processed.
- EU AI Act compliance mapping showing which platform features satisfy Articles 13, 14, and 50 requirements.
- Transparent architecture diagram showing every AI decision path, escalation triggers, and audit logging at each node.
If a vendor cannot produce all four artifacts on request during the evaluation phase, remove them from your shortlist.
#Applying the weighted vendor selection model
Score every vendor using the six criteria below with consistent weighting. Rate each vendor on a scale of 1–10 for each criterion, then multiply that score by the criterion's percentage weight to produce a weighted score. Sum the weighted scores across all six criteria to calculate the vendor's total score out of 10. This weighted framework prevents the common failure mode where vendors excel at demos but fail during compliance review or production deployment.
#How the six criteria work together
| Criterion | Weight | Max weighted score | What it measures |
|---|---|---|---|
| EU AI Act compliance | 25% | 2.5 | Audit trails, Article 13/14/50 mapping, GDPR DPA |
| Integration with existing stack | 20% | 2.0 | CCaaS, CRM, telephony live POC within 30 days |
| Proven results in regulated sectors | 20% | 2.0 | Documented deflection and FCR in telecom, banking |
| Transparent governance model | 15% | 1.5 | Glass-box decision paths, human escalation architecture |
| Total cost of ownership | 10% | 1.0 | 24-month TCO including all professional services |
| Vendor viability and EU support | 10% | 1.0 | Funding, European HQ, SLA commitments |
Ask every vendor whether their governance layer can manage AI agents from other providers under the same interface, so use cases already running with another vendor do not need to be rebuilt from scratch during a platform transition.
#Optimizing weights for business goals
Adjust weights based on your primary business driver. If your board's primary concern is EU AI Act audit readiness ahead of the 2 August 2026 enforcement deadline, consider increasing compliance weight to 30% (max 3.0 weighted points) and reducing TCO weight to 5% (max 0.5 weighted points). If your primary driver is cost reduction, consider increasing proven results weight to 25% (max 2.5 weighted points) and reducing vendor viability to 5% (max 0.5 weighted points). Maintain governance as a meaningful factor regardless of your primary business driver. EU AI Act Articles 13 and 14 require transparent decision logic and active human oversight for high-risk systems, making governance weighting critical for any deployment in scope. The total of all weights must equal 100%, ensuring vendor scores remain on the 0–10 scale.
#Platform pass/fail criteria
These are absolute dealbreakers. A vendor that fails any criterion is disqualified regardless of their score across other dimensions:
- No audit trail for individual AI decisions: Without per-interaction decision logs, you cannot demonstrate how the AI reached a specific output to a regulator, an auditor, or your legal team. Under the EU AI Act, the absence of an audit trail makes it effectively impossible to disprove allegations of malfunction or negligence during regulatory examination.
- Cloud-only hosting with no on-premise option: GDPR does not prohibit cloud-only deployments, but it does require vendors to demonstrate appropriate safeguards for any data processed or transferred outside your infrastructure. For banking, healthcare, and government-adjacent contact centers, a cloud-only vendor without documented Standard Contractual Clauses, a validated GDPR data processing agreement, and a clear data residency policy creates material compliance risk. If a vendor cannot produce this documentation during the evaluation phase, on-premise or hybrid deployment options become the lower-risk path to data sovereignty compliance under GDPR Chapter V.
- Pure LLM architecture with no deterministic governance: Creates significant risk of hallucination and fails to provide the human oversight architecture required for high-risk AI systems.
- No documented human escalation workflow: Without a defined escalation path showing how the AI hands off to a human, including triggers, authority levels, and documentation requirements, your compliance team has no mechanism to verify that human oversight is operational rather than theoretical. This creates material risk for compliance sign-off in regulated environments, particularly where internal governance frameworks or sector-specific regulators expect evidence of structured intervention protocols.
#EU AI Act compliance & audit readiness
This section carries 25 points in your evaluation model. Evidence requirements are non-negotiable. GetVocal's Cognigy alternatives guide outlines why compliance architecture is the primary differentiator between platforms that survive regulated procurement and those that don't.
#AI Act Articles 13, 14, 50 mapping
Article 13 requires that high-risk AI systems are sufficiently transparent, with clear documentation of performance characteristics, capabilities, and limitations, so deployers can understand and interpret outputs. In practice, your vendor must provide a glass-box view of every conversation path with documented data access points and decision logic at each node.
Article 14 requires that high-risk systems allow humans to monitor, interpret, and override AI outputs during operation, with oversight measures proportional to the system's risk. A passive monitoring dashboard does not satisfy this. You need an active operational command layer where humans can intervene mid-conversation where required.
Article 50 introduces disclosure obligations requiring that customers know when they are interacting with an AI system. Account for potential opt-out rates in your deflection ROI model, as actual rates vary based on your customer base, channel mix, and how AI disclosure is implemented in the conversation flow.
GetVocal engineers the platform to align with all three articles: the Context Graph makes every AI decision visible, structured, and traceable, supporting the transparency intent of Article 13, the Control Tower Supervisor View gives supervisors real-time visibility into active conversations with the ability to intervene, redirect, or take over at any point, supporting the kind of active human oversight Article 14 requires for high-risk systems, and conversation flows support Article 50 disclosure requirements.
#Mandatory GDPR DPA elements
Every DPA must include the subject matter and duration of data processing, the nature and purpose of processing, the type of personal data processed, and the categories of data subjects. Per GDPR Article 28, you cannot use a sub-processor, including an AI vendor, without a compliant DPA in place. Request the DPA template before your shortlist stage, not during contract negotiation.
#Validating vendor SOC 2 Type II
Ask for the audit report date, the auditing firm's name, and the specific trust service criteria covered. A SOC 2 Type II report tests controls over a sustained period, typically three months or more, not just documented at a point in time. While many organizations start with Type I reports, regulated enterprise procurement typically requires Type II attestation.
#On-premise for GDPR & data sovereignty
For banking, healthcare, and government-adjacent contact centers, cloud-only deployment creates additional compliance complexity. GDPR Chapter V restricts transfers of personal data outside the EU to countries with adequate protections. On-premise deployment significantly reduces this risk: customer data stays within your infrastructure, removing the need for international transfer safeguards that cloud-only deployments require. GetVocal offers on-premise deployment as a standard option alongside EU-hosted cloud and hybrid configurations, directly addressing data residency requirements that cloud-only vendors require extensive additional safeguards to meet.
#Prevent regulatory fines: Audit trails
GetVocal's Context Graph logs every decision path taken during a customer interaction, including which data nodes were accessed, what logic was applied at each step, and what escalation trigger fired if a human took over. Your compliance team will present this audit trail to the regulator.
#Integrating your existing tech stack (20% weight)
The AI platform you choose should orchestrate your existing systems, not replace them. GetVocal's Cognigy vs. GetVocal comparison shows how integration architecture determines whether a platform accelerates your operation or adds another system for agents to manage.
#CCaaS for unified agent desktop
Your CCaaS platform (including Genesys Cloud, Five9, Avaya and more) handles call routing and telephony. Your AI vendor must integrate at the API level to enable real-time context transfer when escalation occurs. A unified desktop that surfaces telephony, CRM context, and AI conversation history in a single interface eliminates context-switching overhead.
#Agent efficiency via CRM & KB
Bidirectional sync with Salesforce Service Cloud, Microsoft Dynamics, and more delivers:
- AI pulls live customer data during interactions
- AI writes resolved outcomes back to the CRM without agent intervention
- Knowledge base content (Confluence, SharePoint) feeds AI response logic directly
- AI responses match your current policy, not a six-month-old snapshot
#30-day integration validation plan
Require a live proof of concept demonstrating actual data flowing between your CCaaS and CRM through the AI platform, with a real escalation from AI to human agent showing full context transfer. API documentation describes what the vendor built. A live integration with your specific stack confirms it works in your environment.
Structure the POC to cover three validation areas. First, confirm the API connection between your CCaaS telephony layer and the AI platform with test call routing. Second, run bidirectional CRM sync with a sample of live customer records in a sandboxed environment. Third, complete end-to-end escalation testing showing AI-to-human handoff with full conversation context visible on the agent desktop.
#AI agent desktop: Compliance & speed
A unified interface consolidating your CCaaS, CRM, knowledge base, and AI supervision layer into a single desktop reduces both AHT and operational complexity. Every additional platform context-switch adds handle time and increases the risk of data entry errors during live conversations, reducing both speed and quality for agents managing regulated interactions.
#Beyond API docs: Live integration proof
Ask every vendor to run the escalation scenario live during your demo using a real call scenario from your environment. GetVocal's agent stress testing guide outlines the KPIs to monitor during integration validation to confirm the system performs under production load.
#Real-world impact in compliant sectors
Twenty points in your evaluation model depend on verified outcomes from peers in your industry. GetVocal's PolyAI alternatives guide provides a framework for evaluating competitor proof points critically rather than accepting vendor-provided metrics at face value.
#Evaluating regulated industry references
Ask for customer references in your target industry (telecom, banking, insurance, healthcare, retail, ecommerce, or hospitality) who deployed the platform in a production environment, not a pilot. Ask what compliance artifacts they produced and what went wrong during implementation. A vendor with nothing to hide provides direct access to these references.
GetVocal reports an average query resolution rate of 65% and first-call resolution above 77% across its customer base (company-reported). Industry research shows deflection rates of 20-40% are typical for most contact centers, with high-performing organizations achieving 50% or higher. Individual customer results vary by use case, channel mix, and deployment scope. The Movistar Prosegur Alarmas deployment achieved 42% of callers guided to app self-service, a 30% reduction in median handle time, and 25% fewer repeat calls within seven days on the same issue. These ranges represent strong performance targets for your evaluation.
#Time to first agent deployment
The standard deployment timeline for a core use case runs 4-8 weeks with pre-built integrations. Glovo's first AI agent was delivered within one week, then scaled to 80 agents in under 12 weeks, achieving a five-fold increase in uptime and a 35% increase in deflection rate (company-reported).
"Deploying GetVocal has transformed how we serve our community... results speak for themselves: a five-fold increase in uptime and a 35 percent increase in deflection, in just weeks." - Bruno Machado, Senior Operations Manager at Glovo
#EU AI Act audit documentation
Request a sample compliance documentation package from any vendor claiming EU AI Act readiness before you enter contract negotiations. For EU AI Act readiness, request a compliance documentation package covering the Context Graph decision path export for each deployed use case, a full conversation audit log for a sample of interactions, and the Article 13/14/50 compliance mapping document. Request the SOC 2 Type II report separately as a vendor security credential, produced by an accredited third-party auditing firm and valid for 12 months from issuance.
#Spotting vague success metrics
Reject any vendor proof point that uses percentage improvements without a stated baseline. The metrics that matter for your CFO's cost reduction mandate are cost per contact (industry averages sit around $7.16 per interaction, with benchmarks ranging from $2.70–$5.60 for simpler contact types to $6–$12 for phone support; AI-assisted operations typically achieve 10–25% reductions) and total contact center operating expense divided by total interactions handled quarterly. Hold vendors to these specific, comparable figures with documented baseline measurements.
#Auditable AI decisions & human control
This section carries 15 points in your evaluation model and represents the core of GetVocal's differentiation. The Control Tower operates as an operational command layer where human judgment actively directs AI-driven conversations. For a detailed contrast with Cognigy's low-code development platform approach, see our PolyAI vs. GetVocal comparison.
#Human oversight of AI dialogues
The Control Tower's Supervisor View gives your contact center supervisors real-time visibility into every active AI and human conversation. Supervisors see sentiment trends, escalation triggers, and conversation quality scores as they happen. When sentiment drops in a specific conversation, supervisors act immediately, intervening directly, redirecting the AI's approach, or taking over the conversation entirely without the customer repeating any information. Human in control, not backup.
The Control Tower also governs AI agents from other providers under a single interface. If you have use cases already working with another vendor, you keep them running and gain oversight of those conversations alongside your native GetVocal agents. This eliminates the rebuild cost and timeline risk that forces teams to start from scratch during platform migrations.
#Structured escalation: Bidirectional AI-human handoff
Structured escalation means the human agent who picks up an escalated conversation sees the complete interaction history, the customer's CRM record, sentiment indicators from the AI conversation, and the specific decision boundary that triggered the escalation. The customer does not repeat their name, account number, or issue description. Before escalating, the AI requests validation from supervisors for sensitive actions and asks for guidance on edge cases it cannot resolve, rather than guessing. Supervisors can redirect the AI mid-conversation and return control to it once the edge case is resolved. The AI also shadows human interactions to learn from how agents handle complex cases. Human in control, not backup.
This is a two-way collaboration model where AI can request validation from humans before taking sensitive actions, humans can redirect the AI mid-conversation and hand control back when appropriate, and AI shadows human interactions to learn from their approach. When an AI agent reaches its capability boundary, it asks for human guidance rather than guessing. GetVocal builds these handoff paths into the Context Graph before deployment, not as a reactive fallback after the AI fails. Human in control, not backup.
#Transparent AI decision logs: Examples
The Context Graph operates as a living graph of conversation protocols. Each node represents a discrete conversation step with documented data inputs, logic the system applies, and possible outputs. An auditor can pull the decision log for any individual interaction and trace every step the AI took, including which policy document it referenced, what customer data it accessed, and at what point the conversation deviated from the expected flow. GetVocal's head-to-head comparison with Cognigy shows how this glass-box architecture contrasts with low-code development platform approaches.
#Black-box AI: Compliance risks
A pure LLM architecture generates responses probabilistically, meaning the same customer question asked twice can produce different answers. In a regulated contact center, this means the AI might tell one customer their insurance claim is covered and another that it isn't, for identical circumstances. When your compliance team asks why the AI gave a specific answer, a pure LLM vendor cannot tell them, and that is the audit trail failure that triggers regulatory action. GetVocal's deterministic governance layer wraps generative AI capabilities with explicit business logic so every response follows a traceable decision path.
#Calculate your true platform cost
TCO carries 10 points in your evaluation model, but its impact on budget approval is disproportionate. Vendors who hide professional services costs in "contact sales" placeholders are not being transparent. GetVocal's Sierra AI migration guide covers how to account for migration costs when switching platforms mid-contract.
#24-month TCO: Itemized cost analysis
Build a comprehensive 24-month TCO model that includes platform licensing, implementation and professional services, and ongoing optimization and A/B testing. Factor in integration work, Context Graph creation, agent training, and phased rollout when building your business case for CFO approval.
#Evaluating AI licensing fees
#Expert onboarding & configuration
Context Graph creation from your existing scripts, policy documents, and past conversation transcripts represents a significant share of professional services at initial deployment. Vendors who underestimate this phase, or exclude it from initial pricing, are the same vendors whose customers experience implementation overruns well beyond the original timeline.
#Platform performance tuning
Budget for continuous learning infrastructure annually, including A/B testing of conversation flows, human feedback integration, and node-level metric analysis. GetVocal runs automated A/B tests against alternative conversation approaches and rolls out the higher-performing variant without requiring manual engineering work, which prevents the platform degradation that typically hits AI deployments 6-12 months post-launch.
#Red flags: Hidden fee structures
Token-based pricing models are the most dangerous for budgeting. If your AI vendor charges per LLM token consumed, your cost scales with every conversation and every edge case the AI handles inefficiently. A contact center handling 100,000 monthly interactions can see token costs vary dramatically between high-volume and low-volume months, making financial planning unreliable. Demand fixed-fee-per-resolution pricing or capped monthly structures before signing.
#Expert onboarding & configuration
Context Graph creation from your existing scripts, policy documents, and past conversation transcripts represents a significant share of professional services at initial deployment. Vendors who underestimate this phase, or exclude it from initial pricing, are the same vendors whose customers experience implementation overruns well beyond the original timeline.
#Platform performance tuning
Budget for continuous learning infrastructure annually, including A/B testing of conversation flows, human feedback integration, and node-level metric analysis. GetVocal runs automated A/B tests against alternative conversation approaches and rolls out the higher-performing variant without requiring manual engineering work, which prevents the platform degradation that typically hits AI deployments 6-12 months post-launch.
#Red flags: Hidden fee structures
Token-based pricing models are the most dangerous for budgeting. If your AI vendor charges per LLM token consumed, your cost scales with every conversation and every edge case the AI handles inefficiently. A contact center handling 100,000 monthly interactions can see token costs vary dramatically between high-volume and low-volume months, making financial planning unreliable. Demand fixed-fee-per-resolution pricing or capped monthly structures before signing.
#Platform longevity & ongoing support (10% weight)
Choosing an AI vendor is a minimum 12-month commitment with typical enterprise deployments running 24-36 months in practice. The vendor who deploys your platform needs to be financially stable and still investing in European compliance when the EU AI Act enforcement cycle matures. GetVocal's Cognigy pros and cons analysis covers the vendor stability factors that enterprise buyers consistently underweight during initial evaluation.
#Assessing vendor funding rounds
Evaluate the vendor's funding stage and financial runway. GetVocal has raised $30 million in total funding, led by Creandum with participation from Elaia and Speedinvest. Series A funding or above often signals sufficient runway for sustained product development and European support team expansion, though runway duration depends on burn rate, deal pipeline, and growth targets. Early-stage vendors may carry greater execution risk for a multi-year enterprise deployment.
#Assessing EU vendor local commitments
European HQ matters for data sovereignty, regulatory alignment, and support team proximity. A vendor headquartered in the US and retrofitting GDPR compliance into a platform built for American data practices is a structurally different risk profile from one built in Paris for European regulatory requirements from day one. GetVocal's Paris headquarters and 60-person European team serve 23 markets across France, Portugal, the UK, and DACH.
#AI agent operational support SLAs
Require 24/7 support SLAs with documented escalation paths, named customer success managers, and maximum response time commitments for production incidents. A contact center handling thousands of daily interactions cannot wait 48-72 hours for a support ticket response when an AI agent starts misrouting calls.
#Evaluating human-in-the-loop roadmap commitments
Ask every vendor for a documented product roadmap covering their human-in-the-loop capabilities, with specific commitments rather than general intent. The four areas that matter: planned improvements to two-way AI-human collaboration (AI requesting validation mid-conversation, not just after failure), expanded audit trail capabilities aligned with EU AI Act enforcement updates, multilingual governance support for pan-European deployments, and third-party AI agent governance, confirming whether the platform can bring existing agents from other vendors under the same Control Tower interface, with consistent audit logging and human oversight, eliminating the need to rebuild working use cases during migration. Require a named timeline for each item and confirm whether roadmap commitments can be written into your contract.
#Applying weighted criteria for vendor choice
Once you have scored each vendor across all six criteria, the final stage is applying those scores to build your business case and structure your internal approval process.
#Measuring vendor ROI & TCO
The ROI formula your CFO needs: (Deflection rate x Monthly interaction volume x Cost per human-handled contact) minus Monthly platform cost = Monthly savings. At 65% deflection (company-reported GetVocal average), 50,000 monthly interactions, and €10 average cost per human contact, monthly gross savings reach €325,000 before platform costs. At 12 months, that produces €3.9M in gross savings before implementation costs.
#Forming your AI governance team
Your evaluation team must include representation from CX operations (who own deflection and AHT targets), Legal and Compliance (who sign off on EU AI Act and GDPR artifacts), IT and Security (who validate integration feasibility and on-premise architecture), and Finance (who own the TCO model). A vendor evaluation that excludes Legal until contract stage gets blocked at contract stage.
#90-day pilot success metrics
Define pilot success before you start:
- 50%+ deflection rate on a single high-volume use case (password resets, billing inquiries, order status)
- Significant reduction in compliance incidents as defined by your legal team (industry data shows 50-60% reduction in violations is achievable within 90 days)
- First contact resolution above 75% on AI-handled interactions
- Escalation quality test confirming the human agent receives full conversation history, customer CRM data, and the escalation trigger reason before speaking, with no need for the customer to repeat information already provided to the AI
#EU AI Act compliance documents
Before signing any contract, require this complete artifact package:
- SOC 2 Type II audit report dated within 12 months
- GDPR Article 28 compliant DPA template
- EU AI Act Articles 13, 14, and 50 compliance mapping document
- Architecture documentation showing decision paths, escalation triggers, and audit logging
- Sample audit trail documentation from a production deployment
- Transparency disclosure approach for AI interaction disclosure
- For on-premise deployment evaluations, request a technical architecture diagram with data flow documentation showing how customer data is isolated within your infrastructure
#What to know before your hybrid AI rollout
Moving from vendor selection to live deployment surfaces a different set of challenges. The considerations below address the practical factors that most commonly affect rollout outcomes in regulated environments.
#Realistic evaluation timelines
Legal review of GDPR DPAs and EU AI Act compliance documentation typically takes 6-12 weeks at regulated enterprises. Procurement approval adds 4-8 weeks. Build these into your timeline before promising a deployment date to your board. A vendor who tells you they can be live in four weeks without accounting for legal review is misrepresenting your procurement reality.
#What if no vendor scores above 80%?
Score every vendor against your weighted framework before adjusting weights. You can compromise on integration tooling or deployment speed. You cannot compromise on audit trail availability or on-premise hosting if your Legal team requires it. For regulated industries, prioritize compliance documentation over feature breadth when evaluating trade-offs. In regulated industries, a compliance gap on any pass/fail criterion disqualifies a vendor regardless of how they score on features, integration, or proven results. A vendor with cloud-only deployment and no audit trail does not advance to contract, even if their overall weighted score is higher than a compliant alternative.
#Ensuring EU AI Act adherence
The EU AI Act is not a one-time checkbox. Build ongoing compliance reviews into your vendor contract and require updated compliance mapping documentation whenever the platform architecture changes. Each new use case you deploy after the initial pilot should follow the same audit trail approach as your first, applying consistent logging coverage across every conversation flow to support ongoing compliance review and regulatory examination.
Regulated industries don't get to treat compliance as an afterthought. Successful deployments require early involvement from compliance teams, demand proof over promises, and select vendors who can produce documented EU compliance artifacts, not just assertions of compliance readiness.
Request the Glovo case study to see the full implementation timeline, integration approach, and KPI progression across 80 agents deployed in under 12 weeks. Schedule a technical architecture review with the GetVocal solutions team to assess integration feasibility with your specific CCaaS and CRM platforms.
#FAQs
What documentation does GetVocal provide to satisfy EU AI Act Articles 13, 14, and 50?
GetVocal provides a documented EU AI Act compliance mapping artifact linking platform features to regulatory requirements: the Context Graph makes AI decision paths visible, structured, and traceable, in support of Article 13 transparency requirements, the Control Tower Supervisor View enables supervisors to monitor, intervene in, and redirect live AI conversations, supporting the active human oversight intent of Article 14 for high-risk systems, and disclosure protocols support AI interaction transparency. Ask the vendor to provide sample conversation audit logs from production deployments during your evaluation to validate audit trail capability.
How fast can a regulated European enterprise deploy GetVocal in production?
The standard timeline for a core use case with pre-built integrations runs 4-8 weeks. Glovo's first AI agent was delivered within one week, then scaled to 80 agents in under 12 weeks as a proof point of rapid deployment (company-reported).
Does GetVocal offer on-premise deployment for strict GDPR data sovereignty requirements?
Yes. GetVocal offers on-premise deployment as a standard option alongside EU-hosted cloud and hybrid configurations, allowing customer data to remain entirely behind your firewall with no data transfer to external servers. This directly addresses data sovereignty requirements under GDPR Chapter V for banking, healthcare, and government-adjacent contact centers.
#Key terms glossary
Context Graph: GetVocal's protocol-driven conversation architecture that maps AI decision paths as an explicit, auditable graph with documented data access points, business logic, and escalation triggers at each node. Designed to support transparency requirements for high-risk AI systems.
Control Tower: GetVocal's operational command layer for human-AI collaboration, comprising the Operator View (where conversation flows are built and AI behavior boundaries are defined) and the Supervisor View (where supervisors monitor live interactions and intervene in real time). Not a passive monitoring dashboard.
SOC 2 Type II: A third-party security audit conducted by an accredited CPA firm that tests the operating effectiveness of a vendor's security controls over a minimum six-month period. The Type II designation is the standard required for regulated enterprise procurement, as opposed to the weaker Type I point-in-time attestation.
Deflection rate: The percentage of inbound customer interactions resolved by the AI agent without requiring transfer to a human agent. GetVocal reports a platform average query resolution rate of 65% (company-reported). Industry benchmarks show deflection rates of 20-40% are typical for most contact centers, with high-performing organizations achieving 50% or higher. Individual results vary by deployment scope and use case.