Migrating from Gradient Labs to multi-channel conversational AI: Switching strategy
Migrating from Gradient Labs to multi-channel AI requires four phases: audit logic, map Context Graphs, integrate voice and chat.
TL;DR: Migrating to a fully governed, omnichannel platform requires structured phases: audit your existing conversation logic and implicit knowledge, map those rules into deterministic Context Graphs, remap CCaaS and CRM integrations for voice and chat, and execute a phased rollout starting with one controlled use case. The hardest part is often preserving the accumulated knowledge your team has built. A graph-based platform lets you carry that logic into voice and chat while meeting EU AI Act requirements throughout the transition. Core use case deployment runs 4 to 8 weeks with pre-built integrations.
Most enterprise CX teams build capable AI automation on one platform and then hit a wall when compliance, channel coverage, or governance requirements outgrow what the platform was designed to do. Gradient Labs delivers conversational AI for financial services use cases, but enterprise contact centers operating across European markets and multiple industry verticals increasingly need glass-box auditability, on-premise deployment options for data sovereignty, and full omnichannel coverage across voice, chat, email, and WhatsApp. Platforms built for a single industry vertical are not designed to meet those requirements at scale.
This guide gives you the exact technical and operational steps to migrate without disrupting customer operations, preserve the conversation logic you have already built, and deploy compliant voice and chat AI. Core use case deployment runs 4 to 8 weeks with pre-built integrations.
#Why enterprise CX teams outgrow industry-specialized AI platforms
The deeper problem with outgrowing any AI platform is what we call the knowledge distribution problem: the implicit knowledge your agents taught the system through corrections, escalation decisions, and resolved edge cases is locked inside a single architecture. When you expand to new channels or new markets, that knowledge does not transfer automatically. You rebuild from scratch on each new channel, or you accept inconsistent resolution quality across your portfolio.
Enterprise contact centers in telecom, banking, insurance, healthcare, retail and ecommerce, and hospitality and tourism require real-time resolution across voice, chat, and WhatsApp, channels where probabilistic LLM outputs create unacceptable compliance risk and where hallucinated policy answers can trigger regulatory scrutiny.
#Channel breadth gaps
Enterprise customers increasingly expect consistent resolution across every channel they use, not just the channels your current platform supports. When email and WhatsApp coverage is absent, customers on those channels either go unserved or generate inbound volume on voice and chat that your human agents absorb. That is where deflection gains on your existing channels fail to reduce total contact volume. LLM-native agents handle 5 to 10% of CX, covering FAQ and basic Q&A. The remaining complex transactional interactions require deterministic governance to resolve without regulatory risk, across all channels your customers use, not a subset of them.
#Managing voice and chat volume spikes
Voice is synchronous. A customer on hold cannot wait for a probabilistic response that may or may not align with your refund policy. Chat interactions require deterministic rule enforcement to avoid contradicting policy your Legal team approved last quarter. Without AI coverage on these channels, your agents handle demand surges manually, putting pressure on service levels when customers are most frustrated.
#Data migration best practices: Pre-migration audit
Before you decommission anything, extract and document everything that currently works. The goal is to convert implicit knowledge into explicit, portable logic that can be encoded into Context Graphs. Core use case deployment runs 4 to 8 weeks with pre-built integrations.
#Implementation steps: Documenting your current logic
For each automated workflow in your current platform, document the following before beginning any technical migration work:
- Trigger conditions: What customer input or metadata initiates the workflow?
- Decision logic: What rules govern each branch of the conversation?
- Data dependencies: Which CRM fields, knowledge base articles, or backend system calls does the workflow require?
- Escalation thresholds: At what point does the system transfer to a human, and what context does it pass?
- Resolution outcomes: What constitutes a successful resolution for this use case?
This documentation becomes the raw input for your Context Graph design. Every rule that lives implicitly in a prompt today needs to become an explicit, testable node in the new architecture. Use our Cognigy migration checklist to structure this inventory. Cognigy is a low-code development platform rather than an Enterprise AI Agent Platform, but the pre-migration audit process of documenting trigger conditions, decision logic, data dependencies, escalation thresholds, and resolution outcomes is consistent across platform migrations.
#Identifying legacy system dependencies
Map every API call your current platform makes to external systems. Common dependencies include CRM record reads from Salesforce or Dynamics, ticket creation in JIRA or ServiceNow, and authentication calls to identity providers. Any integration your current AI relies on will need remapping to support voice and chat channels where latency requirements are stricter and session management is more complex.
Identify where your current system relies on implicit knowledge (patterns learned through LLM fine-tuning) versus hardcoded rules. Implicit knowledge can be harder to port because it lives in model weights, not exportable logic. Your pre-migration audit must make that implicit logic explicit before you switch platforms.
#Measuring existing CX ROI
Establish your baseline metrics before migration begins. Capture current deflection rate by use case, average handle time for escalated interactions, cost per contact, first contact resolution rate, and repeat contact rate within 7 days on the same issue. These numbers become your proof of ROI at the 90-day mark post-launch.
#Designing your multi-channel AI architecture
Our ContextGraphOS is the technical foundation for the new architecture. It encodes your business logic directly into graph-based conversation protocols where every decision path is visible, testable, and auditable before a single customer interaction takes place. Business logic is structure, LLMs handle natural language, and neither can override the other. The architecture ensures that every AI decision is visible, structured, and traceable, which allows organizations to gradually delegate more responsibility to AI agents without losing governance.
#GetVocal AI integration overview
Our platform orchestrates conversation flow while preserving your current infrastructure investment. For Genesys Cloud CX integration, follow four steps:
- Configure the SIP trunk and define the external trunk settings in your Genesys Cloud CX environment.
- Add and configure DID numbers, assigning each number to the appropriate call flow.
- Create inbound call flows defining routing logic for AI handling versus queue routing.
- Configure the handoff mechanism to transfer calls back to Genesys when escalation to a human agent is triggered, ensuring the conversation context transfers with the call.
This is not a rip-and-replace integration. Your CCaaS stack handles telephony, your CRM holds customer data, and your knowledge base supplies information. GetVocal's Context Graph coordinate conversation flow while your existing systems remain the source of truth. For platforms including Genesys Cloud CX and Salesforce Service Cloud, pre-built integrations support bidirectional sync so relevant customer data is accessible to the Context Graph during interactions across channels.
#Migrating chat AI conversations
Asynchronous conversation logic may need structural adaptation for real-time chat. When you translate existing rules into chat Context Graphs using the Agent Builder, design for natural multi-turn exchanges that allow customers to provide information progressively, and set explicit session timeout parameters that trigger escalation rather than leaving a chat thread unresolved.
#Deliver consistent CX in voice and chat
Consistency across channels requires that the same business rule produces the same outcome regardless of whether a customer contacts you by phone, chat, or WhatsApp. A living graph of conversation protocols makes that possible: every protocol is built once, tested once, and deployed across all channels with channel-specific adaptations for timing and escalation thresholds.
#Converting decision trees into Context Graphs
The translation from existing automation to Context Graphs follows a structured process:
- Take each decision tree and identify the core business rule it enforces (for example, "if customer requests refund and purchase is within 30 days, approve automatically").
- Create a Context Graph node for that rule, specifying data inputs, logic, and output actions.
- Assign LLM handling only to the natural language layer: generating the response text and managing conversational flow.
- Test each node before connecting it to the full conversation graph.
This is what we call deterministic process grounding. The graph enforces the rule with mathematical precision. The LLM makes the conversation sound human. Neither can override the other.
#Channel-specific escalation rules
Voice and chat require different escalation thresholds. Voice interactions typically escalate faster because customers on hold expect immediate resolution, while asynchronous chat allows more time for edge-case handling. Define decision boundaries by channel before go-live so the Control Tower can enforce them automatically. When the AI hits a decision boundary, it requests human guidance the way a junior agent escalates to a supervisor. The human resolves it, and the AI shadows that resolution to improve the relevant Context Graph node for next time.
#Compliance checklist: EU AI Act during migration
Addressing EU AI Act requirements before go-live protects your Legal team and prevents the compliance-driven shutdowns that have derailed previous AI pilots. Some CX use cases may qualify as high-risk AI systems under the Act, depending on the nature of the automated processing involved. Review the requirements below with your Legal team to confirm which apply to your deployment.
#EU AI Act transparency mapping
Map your platform features to specific article requirements before your Legal team reviews the migration plan. For our architecture:
- Article 13 (Transparency): For high-risk AI systems, providers must supply deployers with clear information covering the system's intended purpose, known limitations, and performance characteristics. Our Context Graph documentation is designed to support these requirements, available to your compliance team on demand.
- Article 14 (Human oversight): For high-risk AI systems, the platform must allow human operators to monitor, interpret, and override the system. The Control Tower's Supervisor View enables oversight and intervention in conversations. Escalation paths are built into conversation flows, not bolted on as a fallback.
- Article 50 (Disclosure): Deployers of AI systems that interact with natural persons must inform them they are interacting with AI. Disclosure can be configured into conversation flows at the start of voice and chat interactions.
#Ensuring GDPR data sovereignty
We offer on-premise deployment and EU-hosted cloud options for organizations requiring data residency behind their own firewall. For banking, insurance, healthcare, retail and ecommerce, and hospitality customers subject to GDPR data transfer requirements, the on-premise option is designed to keep customer data within your infrastructure during AI processing. We support GDPR, SOC 2, and HIPAA standards and can provide compliance documentation for procurement and Legal review during the evaluation process.
#Phased rollout in practice
Glovo scaled from 1 to 80 AI agents within weeks, achieving a five-fold increase in uptime and a 35% increase in deflection rate (company-reported). That speed was possible because the rollout was incremental: one use case live first, then expansion across five use cases and 23 markets once the architecture was validated.
#Validate TCO and integration plan
Before committing to deployment, build your 24-month TCO model with your procurement and operations leads. A credible TCO model for a 50 to 300-agent enterprise deployment should account for six cost components.
- Platform fees. Base platform and per-resolution fees are scoped to your deployment volume and use case complexity. Contact sales for a custom quote based on your projected interaction volumes.
- Implementation and professional services. Costs vary by integration complexity, the number of CCaaS and CRM systems involved, and the scope of Context Graph creation required. Simpler deployments with pre-built Genesys or Salesforce connectors cost less than custom integrations with legacy stacks.
- Ongoing optimization and managed services. Budget for post-launch tuning, Context Graph updates as policy changes, and platform support. Engagement level determines cost.
- Internal staff training and change management. Supervisor and operator training on the Control Tower, agent preparation for escalation workflows, and IT resource allocation for integration maintenance all carry internal costs that are easy to underestimate.
- Compliance and procurement review. SOC 2 Type II reports, GDPR data processing agreement review, and EU AI Act documentation review require Legal and procurement time. Factor that into your timeline, not just your budget.
- Baseline measurement. Before you can establish a payback period, you need a clean baseline: current cost per contact, deflection rate by use case, and average handle time for escalated interactions. If that data does not exist, add time and cost for the pre-migration audit phase.
Present your TCO model alongside projected cost per contact reduction and your 90-day deflection targets to give your CFO a measurable payback period before requesting budget approval.
#Controlled first AI go-live
Deploy a single, high-volume, policy-clear use case first. Password resets and billing inquiry status checks often work well because the decision logic is unambiguous, escalation scenarios are predictable, and the volume is high enough to generate meaningful metrics within the first few weeks. Use the Control Tower's Supervisor View to monitor early interactions closely, catching any Context Graph node that produces unexpected outputs before it becomes a pattern.
#Integrate voice and chat AI
Once your first use case achieves stable deflection and zero compliance incidents, expand to voice and chat. Validate that your CCaaS integration (such as Genesys) handles the escalation handoff correctly under concurrent call volume. For chat, confirm that escalation alerts reach human agents promptly and that session context transfers completely when the AI triggers a handoff.
#Scaling multi-channel CX operations
Each new use case added to the platform can benefit from the human-AI flywheel: every human intervention generates production data that updates the relevant Context Graph node, reducing escalations over time. We support 100+ languages across all channels, enabling multi-market rollout without requiring separate AI implementations per region.
#Upskilling agents for AI-powered support
When AI handles routine interactions, your human agents absorb the interactions that are too complex, too emotional, or too policy-sensitive for automation. Without training for that shift, attrition accelerates. Agents who spent significant time on routine queries now handle more complex complaints and eligibility disputes. Empathy and structured problem-solving become increasingly valuable, which can be a positive shift for agent satisfaction when framed correctly.
#Structured AI-to-agent handoffs
When an AI agent escalates to a human, the receiving agent sees the complete conversation history, the customer's CRM record, and the specific reason the AI triggered escalation. The customer ideally does not repeat their issue. The agent does not start from scratch. This structured handoff is what separates governed escalation architecture from a basic fallback that drops context at the moment it is most needed. CMSWire's coverage of the Control Tower launch describes this two-way model as AI requesting human guidance on edge cases and validation of sensitive actions, with a real-time governance layer giving supervisors live visibility and control.
#Quantifying migration ROI and cost savings
#Deflection benchmarks from enterprise deployments
Track true resolution, a query resolved without a subsequent contact on the same issue within 7 days, rather than containment. The benchmarks below come from GetVocal enterprise deployments and are reported by GetVocal.
| Customer or segment | Deflection / resolution result | Timeframe |
|---|---|---|
| Platform average across enterprise customers | 70% deflection (company-reported) | Within 3 months of launch |
| Glovo (food delivery / logistics) | 35% increase in deflection rate, 5x uptime improvement | Within weeks |
| Movistar Prosegur Alarmas (telecom / security) | 42% of callers guided to app self-service, 30% reduction in median handle time | Post-IVR replacement |
| Public sector deployment (1.8M calls / year) | 28% deflection | Sustained |
| Manufacturing deployment (400K calls / year, 20 sites, 10 countries) | 31% deflection, +30% production line efficiency | Sustained |
| Platform aggregate | 31% fewer live escalations, 45% more self-service resolutions vs traditional solutions | Across enterprise customer base |
#Optimizing cost per contact
Across our enterprise customer base, deployments achieve 31% fewer live escalations and 45% more self-service resolutions. The actual savings depend on your interaction volume and current cost base, making the pre-migration baseline measurement critical to building a credible business case.
#Measuring early migration ROI
Define your 90-day success criteria before deployment begins. For the controlled first use case pilot, success means: 50%+ deflection on the target use case, zero compliance incidents, first contact resolution maintained at or above your pre-migration baseline, and agent CSAT scores stable or improved on escalated interactions. These four metrics give your CFO measurable proof of progress without requiring full-scale deployment to validate the business case. We report ROI becoming visible within 1-2 months for well-scoped pilot deployments.
Schedule a 30-minute technical architecture review with our solutions team to assess integration feasibility with your specific CCaaS and CRM platforms. You can also request the Glovo case study to see the implementation timeline, integration approach with Genesys and Salesforce, and KPI progression.
#FAQs
How long does a migration from Gradient Labs realistically take?
Core use case deployment runs 4 to 8 weeks with pre-built integrations. If your existing documentation is incomplete, budget additional time upfront for the pre-migration audit phase.
Can GetVocal govern existing AI agents without rebuilding everything?
We can govern third-party AI agents under a single Control Tower in some configurations, so you may not have to rebuild use cases that already work. You can maintain existing workflows while the new Context Graph architecture handles expansion use cases.
How do I migrate existing conversation automation flows into GetVocal?
Document your existing decision trees and map each rule explicitly into Context Graphs where the logic is enforced deterministically. Test each imported flow with synthetic edge cases before routing live traffic through it.
How does GetVocal handle GDPR data sovereignty and AI procurement approvals?
We offer on-premise deployment and EU-hosted cloud options to meet data residency requirements, and provide SOC 2 Type II reports and GDPR data processing agreement templates upfront for procurement and Legal review.
What does a migration cost in total?
Platform and per-resolution fees are scoped to your deployment requirements. Implementation and professional services costs vary depending on integration complexity and scope. Contact sales for a custom total cost of ownership estimate based on your specific deployment requirements.
What is the right first use case for the controlled pilot?
Start with high-volume, policy-clear interactions where decision logic is unambiguous and escalation scenarios are predictable. Billing inquiry status checks, password resets, account verification, and appointment scheduling are common starting points. Choose use cases with sufficient volume to generate meaningful deflection metrics quickly.
#Key terms glossary
Context Graph: A transparent, graph-based conversation protocol in GetVocal's ContextGraphOS that encodes business rules as explicit, testable decision nodes. Every path is visible before deployment and auditable after.
Control Tower: GetVocal's operational command layer where operators define AI conversation rules (Operator View) and supervisors monitor and intervene in live interactions (Supervisor View).
Deflection rate: The percentage of customer interactions resolved by AI without requiring a human agent, measured as true resolution (no repeat contact within 7 days) rather than containment.
Knowledge distribution problem: The challenge of preserving implicit knowledge accumulated in one AI platform (corrections, edge case resolutions, escalation patterns) when migrating to or expanding across new channels.
Human-in-the-loop: The operational model where AI handles routine interactions autonomously while human agents provide validation, guidance, and intervention at decision boundaries, with each intervention improving the relevant Context Graph node.