Pylon's 10 req/min Issues API limit makes bulk Zendesk migration a multi-day project. Map Orgs→Accounts, Tickets→Issues, flatten Custom Objects—or use a managed service.
There is no native migration path from Zendesk to Pylon; while Pylon partners with a third-party tool (Help Desk Migration), most migrations require custom API work or managed services. The fundamental data model shift is significant: Zendesk's email-first, highly configurable ticketing system with custom objects and multi-level relationships must be mapped to Pylon's simpler, Slack-first, account-centric Issue model that lacks custom object support entirely. Custom work is required to flatten relational custom objects into Pylon Custom Fields or CRM syncs, rebuild triggers and automations from scratch, handle severe API rate-limit asymmetry (Pylon allows only 10 issue creates per minute), and manage careful dependency ordering of Accounts, Contacts, and Issues during loading.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Zendesk Custom Objects with multi-level relationships (e.g., Asset → Contract → Ticket)
Zendesk Custom Objects with multi-level relationships (e.g., Asset → Contract → Ticket) cannot be natively represented in Pylon. You must either flatten these into Custom Fields, store them in your CRM and sync to Pylon, or accept data loss. Plan this mapping before any code is written.
The GET /issues endpoint enforces a maximum time range of 30 days per request
If you have 3 years of ticket history, you need at minimum 36 separate windowed requests just to enumerate your issues. This complicates validation significantly — you cannot query "all issues from 2023" in a single pass.
Pylon uses Bearer token authentication
Only Admin users can create API tokens. Generate a dedicated migration token and rotate it post-migration. (docs.usepylon.com)
Pylon's CRM docs warn that blank CRM values can clear existing Pylon field values
Define field ownership — what lives natively in Pylon vs. what syncs from the CRM — before enabling any sync. (support.usepylon.com)
Always set destination_metadata.destination to "internal" when creating historical issues via API
If you omit this or set it to "email" or "slack", Pylon will send a live notification to your customer for every migrated ticket.
Common Pitfalls & Constraints
- API Rate Limits: Freshdesk allows up to 400 req/min on higher plans, but Pylon restricts Issue creation to just 10 req/min. You must build robust exponential backoff and queueing into your pipeline. - Attachments: Freshdesk attachment URLs are temporary and require authentication. Your script must download the file from Freshdesk, store it temporarily, and upload it to Pylon's /attachments endpoint (also limited to 10 req/min) before creating the Issue. - Pagination Limits: Freshdesk's API caps offset pagination at page 300 (9,000 records). For large datasets, you must use updated_since time-windowing to extract all historical tickets without silently dropping data.
The runbook
Work top to bottom. Tick steps as you go — your progress is saved in this browser.
01 Discovery Establish why you are moving, what "done" means, and who signs off.
Objective A written scope with agreed success criteria, a named owner per workstream, and a budget approved by finance.
Keep these open
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Pull the real numbers out of Zendesk
Export counts for tickets (open and closed separately), contacts, organisations, attachments, macros, triggers, automations, views and SLA policies. Note the oldest ticket date — history depth drives the whole timeline. Estimating from memory is the single most common cause of a blown migration window.
Data Profiler Get real record counts instead of estimating from memory -
Decide what history actually moves
Agree a cut-off with the support lead: all history, last 24 months, or open tickets plus a read-only archive. Every extra year of closed tickets adds API time and cost without adding much agent value. Get this in writing — it is the decision people relitigate mid-cutover.
A "move everything" default is what turns a two-week migration into a two-month one.
COI & ROI Calculator Build the 36-month business case you will need for sign-off -
Confirm Pylon can hold your support model
Walk your current workflow through Pylon: multi-brand, business hours, SLA targets, CSAT, side conversations, public vs internal notes, and any channel you depend on (voice, chat, WhatsApp, social). List anything with no native equivalent — those are project risks, not configuration details.
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Build the business case
Model licence delta, migration effort, agent retraining, and the cost of staying put (Cost of Inaction). Executives approve a number, not a plan, and you will be asked for it again at the go/no-go.
Helpdesk Migration Planner Turn ticket volume into a dated Zendesk → Pylon timeline -
Name owners and set the go/no-go date
One named owner each for data, configuration, integrations, and agent enablement, plus a decision-maker who can call a rollback. Put the go/no-go meeting in calendars now, 48 hours before the freeze.
Zendesk → Pylon specifics
- Channel alignment
- B2B customers live in Slack Connect and Teams. Pylon integrates natively with these channels so customers never leave their workspace. Zendesk treats chat as a secondary channel bolted onto an email-first architecture.
- Account-centric support model
- Pylon ties every Issue to an Account, giving agents full customer context — health scores, CRM data, renewal risk — directly in the support view. Zendesk can achieve this, but it requires significant customization and app marketplace add-ons. Pylon also supports partner accounts and subaccounts for multi-entity customers. (docs.usepylon.com)
- Cost and complexity reduction
- Zendesk pricing at scale (Enterprise at $169+/agent/month, Enterprise Plus at $209+/agent/month) plus add-ons for advanced API access, AI features, and custom objects can exceed what growing B2B teams want to pay for features they only partially use.
- Rate-limit orchestration built in
- We handle exponential backoff, parallel token management, and windowed batching so you don't build and maintain that infrastructure.
- Custom object flattening
- We map Zendesk Custom Objects into Pylon Custom Fields or CRM-synced fields, preserving data integrity where native parity doesn't exist.
Don't move on until
- Record counts confirmed for tickets, contacts, organisations and macros
- Success criteria signed off by the support lead
- Freeze window provisionally booked with the business
02 Data Audit Find out what is actually in the data before you try to move it.
Objective A profiled, cleaned export with every quality defect either fixed at source or explicitly accepted.
Keep these open
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Take a full Zendesk export and profile it
Export to CSV or JSON and profile every file: row counts, null rates per column, distinct values, and type consistency. Compare row counts against the API totals from Discovery — a gap here means your export is silently truncated, usually by pagination.
Data Profiler Profile the Zendesk export for nulls, outliers and type drift -
Validate file structure before anyone writes a transform
Check delimiters, quoting, encoding (expect UTF-8, watch for BOMs and Latin-1), duplicate headers, and embedded newlines in ticket bodies. Ticket descriptions with raw newlines and commas break naive CSV parsers and silently shift columns.
A single unescaped quote in one ticket body can shift every subsequent column without any error.
CSV Validator Catch broken headers and ragged rows in the raw export -
Inventory PII and set retention
Scan for emails, phone numbers, payment card fragments, national IDs and anything else regulated in ticket bodies and custom fields — support tickets are where customers paste things they should not. Decide what gets migrated, masked, or dropped, and record the legal basis.
Ticket bodies and attachments routinely contain card and ID data that never appears in a structured field.
PII & Compliance Scanner Find regulated fields before they land in a new system -
Quantify duplicates, orphans and dead references
Count duplicate contacts (same email, different casing), tickets whose requester no longer exists, organisations with no members, and attachments whose parent ticket is gone. Fix these in Zendesk where you can — migrating them just moves the mess.
Data Cleaner Strip empty rows, stray whitespace and dead columns -
Clean and normalise the export
Trim whitespace, drop empty rows and columns, normalise casing on emails and tags, and standardise every timestamp to UTC ISO 8601. Timezone drift is invisible at load time and shows up weeks later as SLA reports nobody can reconcile.
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Produce a masked copy for sandbox work
Generate a realistic but fake version of the export for testing and for any vendor who needs sample data. Loading real customer PII into a sandbox is a breach in most jurisdictions, and sandboxes are rarely covered by your DPA.
PII Masker Generate a safe copy for sandbox and vendor testing -
Audit and Prep
Review our Freshdesk Migration Checklist (with specific Pylon notes) to clean up unused automations, tags, and custom fields.
Zendesk → Pylon specifics
- Big bang
- Migrate everything in a single weekend cutover. Works for teams under 20 agents with <50K tickets. Requires a hard freeze on Zendesk.
- Phased by channel
- Migrate Slack/Teams sources first (net-new in Pylon), then email, then chat. This is Pylon's recommended approach — it minimizes dual-system time for each channel. (support.usepylon.com)
- Delta migration
- Run the bulk migration, continue using Zendesk for new tickets, then run a delta sync to catch records created during the transition window.
- API Rate Limits
- Freshdesk allows up to 400 req/min on higher plans, but Pylon restricts Issue creation to just 10 req/min. You must build robust exponential backoff and queueing into your pipeline.
- Attachments
- Freshdesk attachment URLs are temporary and require authentication. Your script must download the file from Freshdesk, store it temporarily, and upload it to Pylon's /attachments endpoint (also limited to 10 req/min) before creating the Issue.
Don't move on until
- Export parses cleanly with no ragged rows or encoding errors
- PII inventory complete and retention decisions recorded
- Duplicate and orphan records quantified and triaged
03 Field Mapping Turn two schemas into one signed-off mapping spec.
Objective A reviewed field-level mapping covering every object, with an explicit decision for every field that has no target.
Keep these open
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Generate the first-pass Zendesk → Pylon field map
Start from an automated match on both schemas, then review every row by hand. Automated matching gets the obvious 70% right and is confidently wrong on the rest — especially anything named "type", "status" or "custom_field_1".
Schema Mapper Opens pre-loaded with the Zendesk → Pylon field pair -
Map status, priority and channel values, not just field names
Enumerate every value in each picklist on both sides and map them explicitly. Value-level mismatches are the defect class that survives all the way to production because the field itself mapped fine — a ticket that should be "Pending" arriving as "Open" reopens SLA clocks.
Statuses with no target equivalent (on-hold, pending-customer) need a policy decision, not a best guess.
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Decide how custom fields land
Create the target custom fields first, matching type exactly (a dropdown mapped to free text can never be mapped back). Where Pylon has no equivalent, decide between a new custom field, a tag, or a note appended to the ticket body — and record which.
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Resolve identity and threading
Decide how source IDs are preserved — most platforms will not let you set the primary key, so keep the original ID in a custom field. Without it, reconciliation becomes fuzzy matching and every future support question about an old ticket is unanswerable.
Losing the original ticket ID makes reconciliation and rollback effectively impossible.
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Plan attachments, inline images and threading order
Confirm size limits, allowed MIME types, and whether inline images survive as attachments or need rehosting. Decide the comment ordering and author attribution rules: comments loaded out of order, or all attributed to the API user, destroy the conversation history agents rely on.
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Freeze and sign off the mapping spec
Version the spec, walk the support lead through it row by row, and get explicit sign-off. Any change after this point goes through change control — mid-flight mapping edits are how partial loads happen.
Zendesk → Pylon specifics
- Transform
- Map Freshdesk statuses (e.g., Pending, Resolved) to Pylon's B2B-centric states (waiting_on_customer, closed).
Don't move on until
- Every source field is mapped, deliberately dropped, or parked in a custom field
- Status, priority and channel value maps agreed with the support lead
- Mapping spec version-controlled and signed off
04 Test Migration Prove the pipeline on a small, representative slice.
Objective A pilot load into a Pylon sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Pylon sandbox that matches production config
Create the custom fields, groups, brands, business hours and SLA policies first. A pilot into a default sandbox tests nothing, because the failures you care about are all configuration mismatches.
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Pick a deliberately nasty pilot sample
Take 500-1000 records chosen for difficulty, not convenience: the longest ticket threads, tickets with the most attachments, non-Latin character sets, merged and split tickets, deleted requesters, and every status value. A clean random sample proves only that easy records are easy.
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Run the load with masked data and instrument everything
Log every API request and response with its source record ID. When 40 records fail out of 10,000 you need to know exactly which ones and why, without re-running the whole batch.
PII Masker Never load real customer PII into a sandbox -
Measure real throughput against the rate limit
Record achieved records-per-hour under Pylon's actual rate limits, including retries and backoff. Extrapolate to the full volume: if the maths says the full load exceeds your freeze window, you fix that now, not on cutover night.
Published rate limits are ceilings, not throughput. Assume real-world rates are meaningfully lower once retries and backoff are counted.
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Reconcile the pilot and triage every failure
Diff source against target on record counts and field-level values. Every discrepancy gets a root cause and a fix — "probably fine" at pilot scale becomes thousands of broken records at full scale.
Migration Validation Tool Diff the pilot batch against source before scaling up -
Put real agents in front of the pilot data
Have two or three agents work sample tickets end to end in the sandbox. They find the things reconciliation cannot see: unreadable threading, missing context, macros that no longer make sense. Fix the mapping, then re-run.
Don't move on until
- Pilot batch reconciles to 100% on record counts
- Agents have reviewed sample tickets and confirmed they are workable
- Measured throughput extrapolates to a viable full-load window
05 Cutover Execute the switch inside a controlled, reversible window.
Objective All in-scope data live in Pylon, agents working in the new system, and a rollback path that stayed available throughout.
Keep these open
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Pre-load history before the freeze
Load closed tickets and contacts days or weeks ahead while Zendesk stays live. Only open tickets and the final delta need to move inside the freeze — this is the single biggest lever on window length.
Helpdesk Migration Planner Size the freeze window from Pylon's real API limits -
Publish the runbook with times, owners and abort criteria
A timed sequence: freeze start, final export, delta load, channel switch, smoke test, go/no-go, agent switch. Name who does each step and the explicit condition that triggers a rollback. Decide the abort criteria before the night, when nobody wants to be the one to call it.
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Freeze Zendesk and take the final delta
Stop new ticket creation, let agents finish in-flight replies, then export everything changed since the pre-load. Announce the freeze to the whole business, not just support — someone always tries to raise a ticket during it.
Tickets created during an unenforced freeze land in the old system and are the most common source of permanently lost data.
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Load the delta and open tickets
Run the delta load, then reconcile counts before touching any channel. Do not repoint email until the delta has verified — an inbound ticket arriving mid-load is far harder to untangle than a few extra minutes of freeze.
Migration Validation Tool Confirm the final delta landed before you reopen -
Repoint channels and verify with live traffic
Switch email forwarding and MX or connector settings, update chat widgets and web forms, and re-authorise integrations. Then send real test tickets through every channel and confirm each lands, routes and triggers the right automation.
Email forwarding changes can take up to a full DNS TTL to propagate — check the TTL days in advance and lower it if needed.
Cron Expression Builder Schedule the delta syncs that run through the freeze -
Run the go/no-go and switch the agents
Walk the exit criteria with the decision-maker, call it explicitly, then move agents over with a named person on hand for the first few hours. Keep Zendesk read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Don't move on until
- Full historical load complete and counts matched
- Inbound channels repointed and verified with live test tickets
- Rollback decision point passed explicitly, not by default
06 Validation Prove the migration is complete, then close it out.
Objective Documented evidence that data, workflow and reporting all survived, and a signed acceptance.
Keep these open
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Run the full reconciliation
Compare source and target on every object: total counts, counts by status, counts by group, attachment counts, and field-level spot checks on a random sample. Produce one report you can hand to an auditor.
Migration Validation Tool Reconcile Zendesk and Pylon record-for-record -
Verify field completeness, not just record counts
Re-profile the loaded data and compare null rates per field against the source profile. Matching record counts with a field that silently arrived empty is the failure mode counts alone will never catch.
Data Profiler Prove field completeness held up through the load -
Rebuild reporting and compare against baselines
Recreate your core dashboards — volume, first response time, resolution time, CSAT — and compare to pre-migration figures for the same period. Explain every variance; a changed SLA calculation is a real finding, not a rounding error.
SLA and first-response metrics are usually recalculated from the loaded timestamps, so they will differ if any timestamp mapping was approximate.
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Test the workflow layer end to end
Fire every trigger, automation, SLA escalation, macro and notification with a live ticket. Workflow does not migrate — it gets rebuilt — so it is untested until someone has actually watched it run.
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Confirm compliance and produce the audit trail
Re-scan the loaded data for regulated fields, confirm retention and deletion policies are configured in Pylon, and file the evidence with your PII decisions from the audit phase.
PII & Compliance Scanner Produce the compliance evidence your auditor will ask for -
Sign off, then decommission on a schedule
Get written acceptance against the Discovery success criteria. Keep Zendesk read-only for an agreed period (30-90 days is typical), take a final archive export, and only then cancel. Diarise the decommission date so it does not quietly renew.
Zendesk → Pylon specifics
- Triggers and automations
- Pylon's trigger system uses different conditions and actions. Rebuild from your documented Zendesk trigger list. (docs.usepylon.com)
- Integrations
- Re-connect CRM (Salesforce, HubSpot), engineering tools (Linear, Jira), and other third-party apps through Pylon's Apps Directory.
- Chat widget
- Swap the Zendesk Web Widget code for Pylon's Chat Widget. This requires a front-end deployment.
- Email routing
- Configure email cutover carefully. Pylon's email migration guide warns that overlap can create duplicate tickets. (support.usepylon.com)
Don't move on until
- Full reconciliation report attached to the project record
- Reporting baselines match pre-migration figures within agreed tolerance
- Formal acceptance signed and archive retention scheduled
Field mapping reference
The field-by-field mapping for each object. Use this as the starting point for your mapping spec.
Object Equivalent
Pylon has no equivalent to Zendesk-style custom objects with relationships, requiring complete flattening into Custom Fields or external CRM sync with potential structural data loss.
| Zendesk field | Pylon field | Notes |
|---|---|---|
| Organizations | Accounts | Map organization.name → account.name, organization.domain_names → account.domains. Store organization.id in account.external_ids for reruns. Create first to establish relationships. |
| Users (end-users) | Contacts | Map user.email → contact.email, user.name → contact.name. Link to Accounts via account_id. Store user.id in contact.external_ids. |
| Users (agents) | Users | Ensure agent email addresses match exactly to preserve historical attribution. Roles must be recreated manually. |
| Tickets | Issues | Map ticket.subject → issue.title, ticket.description → issue.body_html. Link via account_id and requester_id. |
| Ticket Comments | Messages | Each comment becomes a Pylon Message on the parent Issue. Distinguish internal notes from public replies. |
| Groups | Teams | Recreate agent groups as Pylon Teams. Map ticket.group_id → issue.team_id. |
| Tags | Tags | Direct 1:1 mapping. Both platforms treat tags as string arrays. |
| Custom Ticket Fields | Custom Fields (on Issues) | Pylon supports String, Boolean, Date, and Select types. Multi-select and regex fields from Zendesk need transformation. |
| Custom Org Fields | Custom Fields (on Accounts) | Same type constraints apply. |
| Knowledge Base Articles | KB Articles / Collections | Map article.title, article.body → Pylon KB article. Zendesk Sections/Categories map to Pylon Collections. |
| Macros | Macros | Pylon supports macros, but the action set differs. Rebuild rather than migrate. |
| Triggers / Automations | Triggers | Not directly portable. Pylon's trigger system is structured differently. Rebuild from scratch. |
| Custom Objects | Custom Fields or CRM sync | Pylon does not have Zendesk-style custom objects with relationships. Flatten relational data into Custom Fields on Accounts/Issues or sync through a connected CRM. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Organizations → Accounts | low | Direct mapping of name and domain fields is straightforward, and Pylon supports external_ids for cross-referencing Zendesk organization IDs. |
| Contacts (End-Users) | low | Email-based contact mapping is a clean 1:1 transfer with external_id support, though contacts must be linked to previously created Accounts. |
| Tickets → Issues | high | The 10 requests/minute rate limit on issue creation makes bulk migration extremely slow, and all historical tickets import as closed, requiring a separate cutover plan for open work. |
| Ticket Comments → Messages | medium | Each comment must be created as a separate Message on the parent Issue while correctly distinguishing internal notes from public replies, multiplying API call volume significantly. |
| Custom Fields | medium | Pylon only supports String, Boolean, Date, and Select types, so Zendesk multi-select and regex-validated fields require transformation or data loss. |
| Custom Objects | high | Pylon has no equivalent to Zendesk-style custom objects with relationships, requiring complete flattening into Custom Fields or external CRM sync with potential structural data loss. |
| Attachments | high | The attachment upload endpoint is limited to 10 requests per minute, creating a severe bottleneck for ticket histories with significant file attachments. |
| Tags | low | Both platforms treat tags as string arrays, enabling a direct 1:1 mapping with no transformation required. |
| Triggers and Automations | high | Zendesk triggers and automations are not portable and must be completely rebuilt in Pylon's differently structured trigger system, risking workflow gaps if not thoroughly audited. |
| Knowledge Base Articles | medium | Article title and body content maps cleanly, but Zendesk's Section/Category hierarchy must be restructured into Pylon's Collections model and inline media re-uploaded. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Severe API Rate-Limit Asymmetry
Pylon's issue creation endpoint is limited to 10 requests per minute, meaning 10,000 tickets take approximately 17 hours and 100,000 tickets take over 7 days of continuous runtime, excluding comments and attachments.
Custom Object Flattening Required
Zendesk custom objects with multi-level relationships (e.g., Asset → Contract → Ticket) cannot be natively represented in Pylon and must be flattened into Custom Fields, synced via CRM, or accepted as data loss.
Status and Priority Remapping
Zendesk and Pylon use different status and priority vocabularies requiring explicit mapping rules, and all imported historical tickets land in a closed state regardless of their original status.
Trigger and Automation Rebuilds
Zendesk triggers, automations, and macros are not directly portable to Pylon's differently structured trigger system and must be rebuilt from scratch.
Dependency Ordering for Record Creation
Accounts must be created before Contacts and Contacts before Issues to preserve relationship links, and a single missed dependency order silently orphans records.
30-Day Query Window Limitation
Pylon's GET /issues endpoint enforces a maximum 30-day time range per request, requiring dozens of windowed queries to enumerate multi-year ticket histories and significantly complicating post-migration validation.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
What are Pylon's API rate limits for migration?
Pylon's GET /issues and POST /issues endpoints are limited to 10 requests per minute, with a maximum 30-day time range per request on queries. Other endpoints like Accounts, Contacts, and Users allow 60 requests per minute. These limits make bulk historical migrations slow without careful batching and backoff logic.
How do Zendesk objects map to Pylon?
The standard mapping is Organizations → Accounts, Users (end-users) → Contacts, Tickets → Issues, Ticket Comments → Messages, Groups → Teams, and KB Categories → Collections. Tags transfer directly. Custom Objects have no native Pylon equivalent and must be flattened into Custom Fields or synced via CRM.
How long does a Zendesk to Pylon migration take?
At Pylon's rate of 10 issue creates per minute, migrating 10,000 tickets takes roughly 17 hours of continuous API calls. A 50,000-ticket migration can take 3–4 days. That's assuming zero errors and zero retries, and excludes accounts, contacts, comments, and attachments.
Can I migrate Zendesk Custom Objects to Pylon?
Not directly. Pylon does not support Zendesk-style custom objects with inter-object relationships. You must flatten relational custom object data into Pylon Custom Fields (String, Boolean, Date, Select types) on Accounts or Issues, or store the data in a connected CRM and sync it to Pylon.
Can I use CSV or Zapier to migrate Zendesk tickets to Pylon?
CSV only works for very small, shallow moves — Zendesk CSV exports omit ticket comments and descriptions. Zapier and similar middleware are suitable for forward sync after cutover but fail for bulk historical migrations due to rate limits, brittle retries, and inability to handle complex data mapping.