Helpdesk migration
Field-level mapping, validation, and rollback between Yuma AI and Zendesk. We move data and schema; workflows are rebuilt natively in Zendesk.
Yuma AI
Source
Zendesk
Destination
Compatibility
13 of 13
objects map 1:1 between Yuma AI and Zendesk.
Complexity
BStandard
Timeline
4-6 weeks
Overview
Moving from Yuma AI to Zendesk is a dual-layer migration because Yuma AI owns no standalone data store — all tickets, customers, and conversations live in the host helpdesk, while Yuma contributes its AI-modified replies, brand policy rules (Guidelines), and automation workflows (Flows). We run a parallel export: standard helpdesk records through the host platform's API, plus Yuma-specific metadata as structured JSON. In Zendesk, tickets import as native Ticket records with the full conversation thread intact, including Yuma auto-replies that we flag with a custom attribute so the destination AI can use the signal for classification. Guidelines and Flows have no native Zendesk equivalent — we document the full logic and deliver a written mapping to Zendesk Triggers, Macros, and Automations for your admin to rebuild post-migration. We do not migrate workflows, automations, or integrations as code; those are out-of-scope by design.
Every standard and custom field arrives verified.
AI proposes the map; you confirm before any record moves.
Parent–child, lookups, and ownership stay linked.
Calls, emails, meetings — with original timestamps.
Documents, uploads, and inline notes move with the record.
Why teams make this switch
Leaving
What's pushing teams away
Choosing
What's pulling them in
Object mapping
Each row shows how a Yuma AI object lands in Zendesk, including any object-level transformations, lookup resolution, or schema-design dependencies.
Typical mapping — final map is confirmed during the sample migration step.
Yuma AI
Ticket
Zendesk
Ticket
1:1Yuma reads Tickets from the host helpdesk and writes AI-generated replies and resolution status into the ticket thread. We export the full ticket body, metadata, and conversation thread from the host platform API, then import into Zendesk as native Ticket records. Yuma's auto-replies are preserved as comments in the thread and tagged with a custom attribute (yuma_auto_reply: true) so the Zendesk admin or destination AI can identify and optionally review them. Resolution status from Yuma maps to a custom ticket field yuma_resolution_status__c for audit continuity.
Yuma AI
Customer
Zendesk
End User
1:1Customer profiles exist entirely in the host helpdesk. Yuma reads customer name, email, order history, and contact records to ground AI replies. All standard customer fields migrate to Zendesk End User records via the Zendesk Users API. Custom fields populated by Yuma (e.g., subscription tier, order count) migrate as custom user fields in Zendesk.
Yuma AI
Company
Zendesk
Organization
1:1Company-level data from the host helpdesk (e.g., Zendesk Organizations if the source is Gorgias) maps directly to Zendesk Organizations. The domain or company name becomes the Organization's name field and is used as a dedupe key during import. Organization membership on tickets migrates as a standard Zendesk Organization field.
Yuma AI
Agent
Zendesk
Agent
1:1Yuma's Auto-Pilot agents are a logical layer above human agents, not a direct 1:1 map. We export Auto-Pilot agent configurations as structured JSON with their escalation routing rules and channel assignments. During Zendesk migration, we map the escalation routing to Zendesk's Agent role and group structure. Any Auto-Pilot agents without a human agent equivalent are exported separately as documentation for the admin to implement in Zendesk's routing rules.
Yuma AI
Team
Zendesk
Group
1:1Yuma routes edge-case tickets to specific human teams based on rules configured in Flows. We map these team assignments to Zendesk Groups and, where applicable, to Zendesk Business Rules for routing. Teams that exist only within Yuma (e.g., a tier-3 escalation queue defined in Yuma Flows) are flagged as requiring manual Group creation in Zendesk.
Yuma AI
Custom Ticket Fields
Zendesk
Custom Ticket Fields
1:1Yuma respects and populates custom fields defined in the host helpdesk, such as order ID, return reason, subscription tier, or product category. We preserve all custom field values at migration time using the host platform's custom field export and map them to Zendesk custom ticket fields by field ID. Zendesk's custom_fields array uses numeric IDs rather than display names — we resolve by ID during import to prevent misalignment if field names were duplicated in the source.
Yuma AI
Conversations / Messages
Zendesk
Ticket Comments
1:1Every message in a ticket thread is preserved, including Yuma auto-replies. We flag which messages were authored by a Yuma Auto-Pilot versus a human agent using a custom comment attribute (author_type: yuma_autopilot | human_agent). This attribution is critical for the destination AI or admin to understand which resolutions were autonomous and to train future routing models on Yuma's automation corpus.
Yuma AI
Attachment
Zendesk
Ticket Attachment
1:1Inline attachments on tickets (images, PDFs, order screenshots) carry over via the host helpdesk API. Yuma does not store its own attachment blob — we pull attachments from the host platform as normal and insert them into Zendesk Ticket Comments via the Zendesk Attachments API. Attachments associated with Yuma auto-replies are linked to the same comment record in Zendesk to preserve the original message context.
Yuma AI
Tag
Zendesk
Tag
1:1Yuma applies its own internal tags during processing — e.g., resolution_type, automation_status, channel_origin, or language. We export all tags applied by Yuma alongside the ticket record and import them into Zendesk as native Tags. Tags that Yuma used for AI routing logic (e.g., needs_human_review, wismo_resolved) are preserved so the destination platform can use them as training signals or for future automation buildout.
Yuma AI
Guidelines
Zendesk
Triggers / Macros (documentation only)
1:1Guidelines are Yuma's brand policy rules that constrain AI behavior and prevent hallucinations — they define conditions, actions, allowed channels, and guardrails for Auto-Pilot responses. Exported as structured JSON with full condition logic and action sets. Zendesk has no native equivalent construct. We export Guidelines as a documented JSON artifact and provide a written mapping to Zendesk Triggers, Macros, and Automations for the admin to manually reimplement. This work is out-of-scope for standard migration and is scoped as a separate automation rebuild engagement.
Yuma AI
Flows
Zendesk
Business Rules / Routing (documentation only)
1:1Flows are Yuma's visual workflow builder for automating ticket routing, triggers, and escalation sequences. We export Flow definitions as JSON including trigger conditions, branch logic, wait steps, and escalation paths. Zendesk's routing engine (Triggers, Macros, and Automations) uses a different model. We deliver the Flows as documented JSON with a written mapping to Zendesk equivalents. The admin rebuilds routing logic in Zendesk based on this inventory.
Yuma AI
Auto-Pilot Configuration
Zendesk
Routing Rules (documentation only)
1:1Yuma Auto-Pilot configurations (escalation thresholds, response tone settings, brand voice parameters, and channel-specific behavior) export as structured JSON. Zendesk has no equivalent to these per-channel AI behavior settings. We export the configuration and deliver it as a reference document the admin uses to configure Zendesk Agent Copilot settings and channel-specific routing rules post-migration.
Yuma AI
Integrations
Zendesk
Zendesk Integrations (re-connection required)
1:1Yuma's direct integrations with Shopify, Recharge, and Klaviyo are outbound API connections that exist only within Yuma. They do not carry over to Zendesk. We document each active integration, its API endpoints and trigger conditions, and provide a connection guide for re-establishing the same data flows in Zendesk. Shopify metafields, order lookups, and refund actions in Zendesk use the Zendesk Shopify integration or the Zendesk Sunshine API — these are new connections, not migrated ones.
| Yuma AI | Zendesk | Compatibility | |
|---|---|---|---|
| Ticket | Ticket1:1 | Fully supported | |
| Customer | End User1:1 | Fully supported | |
| Company | Organization1:1 | Fully supported | |
| Agent | Agent1:1 | Fully supported | |
| Team | Group1:1 | Fully supported | |
| Custom Ticket Fields | Custom Ticket Fields1:1 | Mapping required | |
| Conversations / Messages | Ticket Comments1:1 | Fully supported | |
| Attachment | Ticket Attachment1:1 | Fully supported | |
| Tag | Tag1:1 | Fully supported | |
| Guidelines | Triggers / Macros (documentation only)1:1 | Mapping required | |
| Flows | Business Rules / Routing (documentation only)1:1 | Mapping required | |
| Auto-Pilot Configuration | Routing Rules (documentation only)1:1 | Fully supported | |
| Integrations | Zendesk Integrations (re-connection required)1:1 | Not supported |
Gotchas + challenges
Platform-specific issues from each side, plus the pair-specific challenges that don't show up on either platform's page on its own.
Yuma AI gotchas
Per-resolution billing inflates costs during peak volume
Yuma owns no standalone data — migration is always helpdesk-centric
Guidelines and Flows require manual recreation at destination
No phone/voice channel creates a migration gap
Zendesk gotchas
Data export requires API scripting on non-Enterprise plans
Automations cap at 500 active rules and 1,000 tickets per hour
Help Center has no native export feature
Custom Objects and full data export are Enterprise-only
Pair-specific challenges
Migration approach
Discovery and dual-scope audit
We audit two parallel scopes in parallel. First, the host helpdesk: ticket volume, ticket fields (standard and custom), organization and user records, attachment count, tag taxonomy, and conversation thread history. Second, the Yuma AI layer: active Guidelines with their brand policy rules, active Flows with routing and escalation logic, Auto-Pilot agent configurations, and a full resolution log for the past 12 months. We use this audit to produce a written migration scope that lists every object in scope, every Yuma automation requiring manual rebuild, and every integration requiring re-connection in Zendesk.
Dual-track export from Yuma AI and the host helpdesk
We run two simultaneous exports. The helpdesk export uses the host platform's REST API to pull Tickets, Users, Organizations, Custom Fields, Tags, Attachments, and conversation threads in paginated batches with exponential backoff on rate limits. The Yuma export pulls Guidelines and Flows as structured JSON, Auto-Pilot agent configs as JSON, resolution logs with per-ticket attribution (which messages were Yuma-authored), and any brand voice or escalation routing settings. The exports run independently so that a slow helpdesk API response does not block the Yuma layer export.
Schema mapping and automation rebuild inventory
We map every helpdesk field to its Zendesk equivalent: ticket fields to Zendesk ticket fields, custom fields by Zendesk field ID (not display name), organizations to Zendesk Organizations, and tags to Zendesk Tags. We flag any Zendesk field types that do not support the source data format (e.g., multi-select arrays, nested JSON) and define transformation rules. For Yuma's automation layer, we produce a written inventory: each Guideline becomes a documented JSON artifact with a written recommendation for Zendesk Triggers and Macros; each Flow becomes a flowchart-equivalent JSON document with a Zendesk routing and automation equivalent. This inventory is the handoff document for the post-migration rebuild.
Sandbox migration and thread attribution validation
We run a full migration into a Zendesk Sandbox using production-like data volume. The customer reviews the imported ticket threads with a focus on two things: whether AI-modified replies from Yuma are present and correctly attributed in the Zendesk thread, and whether custom field values have survived the import without truncation or format corruption. We run reconciliation counts (tickets in, users in, organizations in, tags in) and the customer signs off the sandbox before production migration begins. Any mapping corrections happen here, not in production.
Production migration in dependency order
We run production migration in Zendesk in dependency order: Organizations first (since ticket imports reference them), then Users (Agents and End Users), then Custom Fields, then Tags, then Tickets with full conversation threads and Yuma attribution flags, then Attachments linked to the correct ticket comment records. Each phase emits a row-count reconciliation report before the next phase begins. Yuma-specific metadata (Guidelines JSON, Flows JSON, Auto-Pilot configs) is delivered as a separate structured data package alongside the ticket migration.
Cutover, delta sync, and automation rebuild handoff
We freeze writes to the host helpdesk during cutover, run a final delta migration for any tickets modified during the window, then enable Zendesk as the system of record. We deliver two packages: the data migration completion report (record counts, reconciliation results, any remaining exceptions) and the automation rebuild inventory (Guidelines JSON, Flows JSON, Auto-Pilot config JSON, and written Zendesk construct mappings). We support a one-week hypercare window for data reconciliation issues raised by the Zendesk admin. We do not rebuild Yuma's Guidelines or Flows as Zendesk Triggers, Macros, or Automations inside the migration scope — that is a separate engagement scoped by our automation team or the customer's Zendesk admin.
Platform deep dives
Yuma AI
Source
Strengths
Weaknesses
Zendesk
Destination
Strengths
Weaknesses
Complexity grading
Standard Helpdesk migration. 1 of 7 objects need a mapping; the rest are 1:1.
Overall complexity
Standard migration
Derived from compatibility, mapping clarity, API constraints, and data volume across Yuma AI and Zendesk.
Object compatibility
1 of 7 objects need a mapping; the rest are 1:1.
Field mapping clarity
Field mapping is derived from defaults — final spec confirmed during the sample migration.
Timeline complexity
7-object category — typical timelines run 2–7 days end-to-end.
API constraints
Yuma AI: Not publicly documented — rate limits are governed by the host helpdesk API (Gorgias, Zendesk, etc.).
Data volume sensitivity
Yuma AI doesn't expose a bulk API — REST + parallelization used for high-volume runs.
Estimator
Rule-based pricing — no per-record fees, no manual quotes. Migrations over 2M records are scoped individually.
Step 1
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