Switching from PolyAI: Migration guide and risk mitigation
Switching from PolyAI requires a 12 week migration roadmap covering data extraction, Context Graph build, and phased go live.
TL;DR: Leaving PolyAI doesn't mean starting from zero. You can port your conversation logs, intent lists, and utterance data directly into a governed, auditable platform. This guide covers a migration roadmap across four steps: data extraction, Agent Context Graph construction, stack integration, and a gradual go-live ramp. The core shift moves you from black-box AI to GetVocal's transparent Context Graph architecture, combining deterministic governance with generative AI capabilities, an architectural upgrade that directly addresses EU AI Act transparency and interpretability requirements taking effect through 2027.
The EU AI Act is making black-box AI a board-level liability. Article 13 requires that high-risk AI systems be designed with sufficient transparency to enable deployers to interpret system outputs and use them appropriately, and Article 14 mandates effective human oversight for high-risk systems. If your compliance team cannot audit your PolyAI deployment's decision logic today, you carry regulatory exposure that compounds with every conversation. This guide treats migration as a governance upgrade that eliminates that exposure, not a vendor swap.
#Why CX leaders are migrating from PolyAI to hybrid platforms
PolyAI built its reputation on voice naturalness. For early deployments where deflection rate was the only KPI, that was sufficient. Enterprise contact centers in 2026 need something different.
They need to prove AI decision logic is auditable, interpretable, and aligned with policy. Black-box AI models, where decision logic is embedded inside trained systems you cannot inspect, create direct exposure under Articles 13 and 14. The penalty for non-compliance runs up to €35 million or 7% of global annual turnover for serious violations, with enforcement already active since February 2025 and full applicability for most high-risk AI systems from August 2026. For enterprises in telecom, banking, insurance, healthcare, retail, ecommerce, and hospitality, your compliance team is already tracking these deadlines.
Beyond regulation, the architectural gap is practical. Where PolyAI offers autonomous voice characters with opaque logic, GetVocal's Hybrid Workforce Platform combines deterministic Context Graph with generative AI, giving operators configurable decision boundaries, a real-time Control Center, and a full audit trail per interaction.
#Pre-migration risk assessment: Data, compliance, and TCO
Before you schedule the exit, run a structured risk assessment across three dimensions.
#Data sovereignty
Identify where PolyAI processes and stores your conversation data today. If your deployment is cloud-hosted in US infrastructure, GDPR requires that transfers of EU personal data outside the EEA are covered by adequate safeguards, such as Standard Contractual Clauses or an adequacy decision, and that the receiving environment provides a level of protection equivalent to EU standards. Where those transfer mechanisms are under legal challenge or your vendor cannot demonstrate equivalent protection, you carry the compliance exposure. GetVocal supports on-premise deployment, keeping customer data behind your firewall and out of third-party infrastructure. This matters directly for banking and healthcare use cases where cloud-only vendors cannot meet data sovereignty requirements.
#The TCO reality
The sunk-cost argument for staying ("we spent six months training PolyAI") ignores the ongoing costs of maintaining a black-box system. The table below breaks down what those costs actually look like across the decision criteria that matter:
| Cost category | Staying with PolyAI | Migrating to GetVocal |
|---|---|---|
| Compliance audit overhead | High (manual, recurring) | Low (automated audit trails) |
| Operator configuration effort | High (model retraining, vendor-managed process) | Lower (Agent Builder node updates) |
| EU AI Act exposure | €35M+ fine risk for non-compliant high-risk systems | Reduced by design (Article 13/14 alignment) |
| Integration maintenance | Vendor-specific API connectors (custom integration work) | Integration support including Genesys, Salesforce, Dynamics, and more |
| Vendor lock-in | Proprietary NLU weights non-exportable | Portable Context Graph, exportable conversation data |
| Migration investment (one-time) | None | Implementation partnership, 12-week roadmap |
GetVocal requires an implementation partnership and a minimum 12-month commitment, so factor that into your migration budget alongside the one-time integration and onboarding costs.
Integration maintenance in a black-box environment requires custom work for every policy change because each change means retraining a model you cannot directly inspect. With a deterministic Context Graph, you update a node and the change is live immediately.
A structured migration checklist covering data extraction requirements, integration testing procedures, and go-live validation steps ensures no critical dependencies are missed during the transition.
#Integration audit
You need to document every system that currently connects to your PolyAI deployment before you give notice, so you're not reverse-engineering integrations under contract pressure. List each one:
- Telephony: Genesys Cloud CX, Five9, NICE CXone (SIP trunk configuration)
- CRM: Salesforce Service Cloud or Dynamics 365 (data read/write endpoints)
- Knowledge base: Confluence, SharePoint, or internal wiki (source of truth for AI responses)
- WFM: NICE IEX, Verint (scheduling and forecasting data)
- QA: Call recording and quality monitoring platforms
You'll remap each of these in Step 3.
#The migration roadmap: From black box to glass box
The roadmap runs across four steps. None of them require a hard cutover. Traffic shifts gradually, and you maintain full rollback capability throughout.
#Step 1: Executing the PolyAI exit and data extraction
#What you can export
Proprietary NLU model weights, trained configurations, and platform-specific dialogue logic typically are not portable across vendors. The underlying models remain vendor IP. Based on standard conversational AI platform practices, you can generally export the following assets before terminating your contract:
- Conversation logs: CSV and JSON formats, covering historical interactions with timestamps, intent classifications, and resolution outcomes.
- Intent lists and utterance data: Training utterances in CSV format, giving you the vocabulary and phrase patterns your customers actually use.
- Call transcripts: Text transcripts in TXT or CSV format if stored within the platform.
- Analytics and reporting data: KPI exports including containment rates, escalation reasons, and CSAT scores per intent.
Request a bulk historical export via your account team before you serve formal notice. Some vendors process bulk extraction requests faster while the commercial relationship is still active.
#Building your golden dataset
Raw conversation logs from any production AI system contain noise: misclassified intents, edge-case utterances, and interactions that fell outside your designed flows. You clean this data before handing it to your migration team using three steps:
- Filter by resolution: Keep interactions where the customer confirmed resolution or did not escalate within 24 hours. These are your highest-quality examples of correct behavior.
- Tag by intent: Organize the cleaned set by use case (billing inquiry, password reset, order status) so they map directly to Context Graph nodes.
- Remove PII: Sanitize for GDPR compliance before the data moves to any new environment, stripping names, account numbers, and payment details from transcript text.
You'll use this cleaned dataset as your "golden set," the foundation for defining trigger patterns and utterance coverage in GetVocal's Agent Builder.
#Contractual overlap planning
Your PolyAI contract typically includes a notice period ranging from 30 to 90 days. Build your timeline so Step 3 integration testing completes before that window closes, giving you a clean handover date and no period where you're paying for both platforms at full capacity. Pre-migration contract reviews often surface early termination clauses and data extraction fees that can create unexpected costs if not addressed before serving notice.
#Step 2: Mapping "Characters" to GetVocal's Context Graph
#Understanding the concept translation
PolyAI's "Character" model encodes persona, dialogue logic, and response generation inside a trained generative model. You configure tone and intent coverage at a high level, but the actual decision logic sits inside model weights you cannot inspect. That opacity is the core problem you're migrating away from.
GetVocal's Context Graph, the platform's protocol-driven architecture, makes every decision node visible, modifiable, and auditable. It functions like GPS navigation for conversations: before the AI handles a single customer call, you see every path it might take, every escalation trigger, and every data point it accesses. You verify and adjust the route before traffic starts.
You run the translation process in four steps:
- Intent mapping: Intent mapping typically involves taking each intent from your PolyAI export and creating a corresponding entry point node in GetVocal's Agent Builder.
- Flow reconstruction: For each intent, map the "happy path" (successful resolution without escalation) as a sequence of Context Graph nodes, using your golden dataset conversations as the reference.
- Decision boundary configuration: Define exactly where the AI escalates, which may include sentiment threshold drops, policy exceptions requiring multi-party approval, and complex cases requiring CRM data the AI cannot access in context.
- Utterance coverage: Load the cleaned utterance data from your export to train recognition patterns at each node entry point.
#The glass-box advantage in practice
One common cause of AI pilot failure in production is an interaction pattern the model was never designed for, generating a response that contradicts policy and reaches a customer before any human sees it. The Context Graph eliminates this category of risk. The AI cannot generate a response outside the defined node paths. The Context Graph combines deterministic governance for auditable decision paths with generative AI for natural, contextual responses. You configure the boundaries between the two in the Agent Builder. If a customer asks about a refund policy edge case that's not in your Context Graph, the AI escalates instead of inventing an answer.
This architecture directly addresses Article 13's transparency requirements and Article 12's record-keeping requirements for tamper-resistant logs covering every automated decision. Your compliance team gets an audit trail showing exactly what logic the AI applied, what data it accessed, and when.
#Step 3: Integrating the stack
#Telephony handoffs: SIP trunk reconfiguration
If your PolyAI deployment currently handles inbound voice via SIP trunking through Genesys Cloud CX, the reconfiguration follows a structured sequence. Genesys supports BYOC (Bring Your Own Carrier) external trunks that you configure with protocol settings, listen port, inbound number plan, and termination identifier. You complete the reconfiguration in four steps:
- Provision the new trunk: Configure the GetVocal SIP endpoint in Genesys with protocol settings and termination identifier.
- Configure number routing: Set up number plan routing through Telephony > Sites > Number Plans so inbound DIDs resolve to the correct Context Graph entry node.
- Whitelist IP ranges: Whitelist GetVocal's signaling and media IP ranges in your SIP access control settings.
- Test parallel routing: Run parallel routing at 0% live traffic during integration testing to validate call flow without affecting production.
Keep the PolyAI SIP configuration active but deprioritized until integration testing validates your go-live readiness. This is your rollback path if integration testing surfaces issues.
#CRM bi-directional sync
For Salesforce Service Cloud, GetVocal can integrate via REST API with bidirectional sync capabilities, reading customer account data at conversation start and writing case notes, resolution status, and sentiment scores at conversation close.
For Dynamics 365, integration capabilities include reading and writing contact and account data, supporting both insert and update operations. New interaction records create as cases, and existing records update with conversation outcomes. During configuration, confirm that your Dynamics environment has the appropriate connector licensed and that API credentials are scoped to the specific entities the AI agent needs: Account, Contact, Case, and Interaction.
#Testing the integration pipes
Before you route any live traffic to GetVocal, verify each integration point:
- Telephony: Place test calls through the new SIP trunk and confirm call routing reaches the correct Context Graph entry node.
- CRM read: Trigger a test conversation and confirm the AI pulls correct account data (balance, tier, open cases) from Salesforce or Dynamics.
- CRM write: Confirm post-conversation records appear in the CRM with correct intent classification and resolution status.
- Escalation path: Trigger test escalations at different decision boundaries and confirm context transfers correctly, both for full handoffs to human agents and for validation requests where the AI continues after receiving human input.
- Audit log: Confirm every test interaction generates a complete decision trail in the Control Center.
#Step 4: Hybrid training and go-live
#Control Center configuration
The Control Center is GetVocal's operational command layer, where human judgment actively directs AI-driven conversations rather than passively observing them from a dashboard. Before go-live, configure both layers of access:
Configuration layer: Teams use this interface to define what the AI can and cannot do autonomously. Set decision boundaries, configure escalation triggers, and validate the Context Graph paths built in Step 2 against the golden dataset.
Live intervention layer: Supervisors use this interface to oversee live interactions and step into conversations when the AI reaches a decision boundary. When supervisors intervene, they can provide validation or decisions that allow the AI to continue the conversation, or they can handle the interaction directly and reassign it back to the AI, which resumes with full context. The AI can shadow human interventions to learn for next time. Configure alerts so supervisors see conversations that need attention before the customer has to ask for a human.
#Shadow mode validation
Run GetVocal in shadow mode for the first part of Step 4. In shadow mode, the system processes live conversations in parallel and logs its responses without serving them to customers, letting you validate that your Context Graph produces correct, policy-aligned responses against real traffic before a single customer experiences them.
Monitor intent recognition accuracy, escalation trigger firing rate, and response alignment against your golden dataset during this period. If a Context Graph node generates incorrect outputs consistently across matching interactions, revise the node logic before you open live traffic. Persistent errors signal systemic logic issues rather than edge-case anomalies.
#Go-live ramp
Once shadow mode validates your Context Graph, shift live traffic in four steps:
- 10% of volume for approximately 72 hours, monitoring containment rate, CSAT (customer satisfaction) scores, and escalation rate against your baseline.
- 25% of volume if metrics hold within acceptable range, running for several days.
- 50% of volume once the integration stabilizes under moderate load, watching agent stress testing metrics including AHT (average handle time), queue hold times, and escalation response times simultaneously.
- 100% of volume after a stable period with no compliance incidents.
If you're using Genesys, consider maintaining your previous routing configuration at 0% weight during initial rollout as a potential rollback path.
When Glovo deployed GetVocal, their first agent was live within one week. They scaled to 80 agents in under 12 weeks, achieving a 5x uptime and 35% deflection increase (company-reported). That timeline included integration work, Context Graph creation, agent training, and phased rollout.
#Post-migration: Measuring success and ROI
#KPIs to watch in the first 90 days
Move beyond deflection as the only measure. A glass-box platform gives you metrics a black-box system cannot produce. Track these in the first 90 days:
| KPI | What to track | What it measures |
|---|---|---|
| Containment rate | Week-over-week progress | AI-handled interactions with no human escalation |
| Auditable resolution rate | Track weekly | Resolutions with full Context Graph audit trail |
| Human intervention rate | Baseline, then monitor trend | Supervisor step-ins per 100 AI interactions |
| First Contact Resolution (FCR) | Compare to baseline | Issues resolved in initial interaction with no repeat contact |
| Average Handle Time (AHT) | Reduction vs. documented PolyAI baseline | Combined AI + human time per interaction |
| CSAT | Post-interaction scores | Post-interaction survey scores |
| Compliance incident rate | Reduction vs. baseline | Policy contradictions flagged by QA audit |
GetVocal handles voice, chat, email, and WhatsApp through the same Context Graph architecture, so the KPIs above apply across all channels from day one. Glovo's deployment covered omnichannel across 5 use cases, which means deflection and FCR gains compound faster than single-channel pilots typically project.
GetVocal is a European-focused platform founded in 2023 that is building its independent review base. If analyst validation or G2 peer reviews are part of your vendor evaluation, factor that into your due diligence timeline.
#Agent impact: The retention case
A common risk in AI rollouts is agent attrition accelerating during the transition because frontline teams see automation as a direct threat to their roles. Frame the Control Center correctly with your team from Week 1: agents move from handling repetitive volume to supervising AI conversations, resolving complex escalations, and building skills that command higher compensation bands. The hybrid workforce model keeps human agents central to quality control, not marginal to it.
#Migration assessment and next steps
The most common mistake in platform migrations is underestimating integration complexity until an IT team is already committed. Request a Migration Assessment from the GetVocal solutions team before you commit to a timeline. The assessment maps your specific CRM, telephony, and WFM integrations to GetVocal's pre-built connectors, surfaces any custom configuration requirements, and produces a TCO model comparing your current PolyAI operating costs against a 12-month GetVocal deployment.
Migration Assessment request to get an architecture review scoped to your Genesys, Salesforce, or Dynamics environment before you give notice.
#Frequently asked questions about migrating from PolyAI
How long does a PolyAI migration typically take?
A full migration roadmap typically runs approximately 12 weeks, covering data extraction, Context Graph build, stack integration testing, and a phased go-live ramp. Core use case deployment with pre-built integrations typically runs 4-8 weeks once integration work is complete, with initial Context Graphs generally live in shadow mode within the first 10 weeks.
Where can I get a detailed migration checklist?
Download the complete PolyAI Migration Checklist covering data extraction formats, CRM integration testing steps, and step-by-step validation procedures. The checklist includes contract review items that pre-migration assessments often flag as cost drivers if not addressed early.
What data can I actually export from PolyAI?
You can export conversation logs (CSV and JSON), intent lists, utterance data (CSV), call transcripts, and analytics data. Proprietary NLU model weights and Character configurations are not portable, as these are the vendor's IP.
Does migration require taking my contact center offline?
No. The phased rollout starts at 10% of traffic and ramps to 100% during the final stage of the roadmap. PolyAI routing remains active as a fallback throughout the ramp, so full rollback requires only a single routing weight change in Genesys.
What EU AI Act requirements are difficult to satisfy with black-box AI architectures?
Article 13 requires transparent system design and clear documentation of a system's capabilities and limitations, so deployers can understand how it operates and use its outputs appropriately (the precise compliance threshold remains unsettled in practice, but the requirement targets system-level transparency, not per-decision explainability), and Article 14 requires effective human oversight for high-risk systems. PolyAI's generative model architecture may present challenges for satisfying both, because the decision logic is embedded in model weights that are difficult to inspect or document.
Does GetVocal work with Dynamics 365 Contact Center?
Yes. The Dynamics 365 Contact Center connector supports bidirectional OData integration for contact, account, and case data, with GetVocal reading customer context at conversation start and writing resolution records on close.
What happens to my Salesforce integration during the switch?
Your Salesforce instance remains the source of truth throughout. GetVocal integrates via REST API, reading and writing case data without replacing your CRM configuration. There is no Salesforce migration, only an API connector setup and permission scoping during Step 3 integration testing.
#Key migration terminology
Context Graph: GetVocal's protocol-driven architecture that breaks conversation flows into visible, auditable decision nodes showing what data the AI accesses, what logic it applies, and when it escalates to a human. You inspect and modify every path before deployment.
Agent Builder: GetVocal's interface for constructing and configuring AI agents using the Context Graph framework, where exported PolyAI intent data maps into defined conversation flows.
Control Center: GetVocal's operational command layer for managing live AI and human agent interactions, where operators configure conversation decision logic and supervisors intervene in real time. Supervisors actively direct conversations through it, not just monitor them.
SIP trunking: A protocol method that delivers inbound and outbound voice calls over the internet to your contact center platform. During migration, SIP trunk reconfiguration in Genesys routes calls from PolyAI's endpoint to GetVocal's with no telephony infrastructure change required.
Black-box AI: An AI system where internal decision logic is not visible or inspectable. You see inputs and outputs but not the reasoning path, creating structural problems for EU AI Act Article 13 compliance.
Glass-box AI: An AI that combines deterministic logic with generative capability, where every decision path is documented, visible, and auditable. GetVocal's Context Graph is the implementation of this model.
Shadow mode: A deployment configuration where the new AI system processes live conversations in parallel with the existing system, logging responses without serving them to customers. Used in Step 4 to validate Context Graph behavior against real traffic before cutover.
BYOC (Bring Your Own Carrier): Genesys Cloud CX's external trunk configuration method, used to provision the SIP connection between GetVocal and your telephony infrastructure during Step 3.