Zoho Desk to Re:amaze is a medium-complexity API migration. Key risks: Zoho's daily credit limits slow extraction, Re:amaze won't accept custom timestamps on messages, and missing suppress_notifications sends live emails to customers.
Migrating from Zoho Desk to Re:amaze requires translating a ticket-centric, department-scoped help desk into a conversation-led, brand-scoped support platform with no native migration path or official import tool. The two platforms have fundamentally different data models: Zoho Desk organizes data around discrete Tickets, Departments, and first-class Account objects, while Re:amaze uses continuous Conversations, Brands, and a flat contact schema with no organizational hierarchy. Critical structural mismatches include the loss of the Account/Organization object (which must be flattened into contact-level custom data attributes), typed custom fields collapsing to untyped key-value strings, and SLA policies, blueprints, and time tracking having no equivalents in Re:amaze. Custom extraction scripts against the Zoho Desk OAuth 2.0 API are required, along with careful data model translation logic, manual staff pre-provisioning, and multi-day extraction scheduling to stay within Zoho Desk's daily API credit limits.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
On-Hold requires a hold_until datetime
When setting status to 5 (On-Hold) via the Re:amaze API, you must also pass a hold_until attribute. If your Zoho Desk "On Hold" tickets don't have a scheduled follow-up date, set a synthetic future date (e.g., 30 days from migration date) or map them to Open instead.
Data center routing matters. Zoho Desk uses region-specific base URLs
desk.zoho.com (US), desk.zoho.eu (EU), desk.zoho.com.au (AU), desk.zoho.in (IN). Using the wrong DC URL returns errors or silently targets the wrong organization. Confirm your DC by checking the URL you use to log into Zoho Desk.
CSV export does not include threads or comments
Zoho Desk's module-level CSV export (Setup → Data Administration → Export) exports ticket metadata — subject, status, priority, dates — but not conversation content. For a complete migration, use the API or request a Data Backup. Do not rely on CSV export as your extraction method.
Contact data attributes override on write via the conversation endpoint
Setting custom data attributes via the conversation POST endpoint completely overrides existing attributes on that contact — it does not merge. If you're importing multiple conversations for the same contact, set contact data once via the Contacts API first, then omit conversation [user][data] on subsequent conversation imports to avoid wiping previously set attributes.
Attachment size limits
Re:amaze enforces a per-file upload limit. If a Zoho Desk ticket contains an oversized file, the Re:amaze API rejects the entire message payload — not just the attachment. Your script must check file size before upload and, for oversized files, skip the attachment and append a text note to the message body: [Attachment not migrated: filename.ext exceeded size limit]. Do not let oversized files silently drop entire messages.
Always set suppress_notifications
true when importing messages. Without this flag, Re:amaze sends a live email or integration notification to the customer for every imported message. For a migration of 50,000 tickets with 5 threads each, that's 250,000 outbound emails to real customers about years-old conversations. This is not recoverable — emails are already delivered before you discover the error. Test with a single conversation batch of 5–10 records and verify zero outbound emails before running at scale.
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 Zoho Desk
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 Reamaze can hold your support model
Walk your current workflow through Reamaze: 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 Zoho Desk → Reamaze 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.
Zoho Desk → Reamaze specifics
- E-commerce workflow integration
- Re:amaze provides native Shopify, WooCommerce, and BigCommerce sidebars that surface live order data, purchase history, and refund actions inside the conversation view without leaving the support interface. Zoho Desk's e-commerce integrations are marketplace extensions that open separate windows or require custom API work. This is an architectural difference: Re:amaze treats order context as a first-class UI element; Zoho Desk treats it as an add-on.
- Continuous conversation model vs. discrete tickets
- Re:amaze groups all interactions from a single customer into continuous, reopenable threads. A Done conversation automatically reopens on customer reply — there is no permanently closed state. Zoho Desk treats each ticket as a discrete unit with a definite lifecycle: Open → On Hold → Escalated → Closed. For B2C support with repeat customers, the continuous model reduces duplicate ticket creation.
- Unified inbox vs. departmental routing
- Re:amaze routes by Brand and Channel, letting agents handle email, live chat, social, and SMS in one view. Zoho Desk routes through Departments, adding routing configuration overhead for small teams handling fewer than 3–4 distinct queues.
- Live chat architecture
- Re:amaze's chat widget includes co-browsing, automated message sequences, and an embedded FAQ — all within one product. Zoho Desk's live chat runs through Zoho SalesIQ, a separate product with its own authentication, configuration, and pricing.
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 Zoho Desk 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 Zoho Desk 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 Zoho Desk 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 -
Run final delta extraction
Pull all tickets created or updated since your last full extraction, using createdTimeRange or modifiedTimeRange filters.
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 Zoho Desk → Reamaze 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 Zoho Desk → Reamaze 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 Reamaze 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.
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 Reamaze sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Reamaze 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 Reamaze'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 Reamaze, 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 Zoho Desk 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 Reamaze'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 Zoho Desk 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 Zoho Desk read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
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Freeze new ticket creation in Zoho Desk
Disable incoming email channels and web forms.
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Import delta into Re:amaze
Load the final batch; use the same suppression flags.
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Enable Re:amaze channels
Point email forwarding, chat widgets, and social integrations to Re:amaze.
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 Zoho Desk and Reamaze 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 Reamaze, 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 Zoho Desk 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.
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Verify live ticket flow
Send test messages through every channel (email, chat, social) and confirm they appear in Re:amaze with correct routing.
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.
Zoho Desk Re
| Zoho Desk field | Reamaze field | Notes |
|---|---|---|
| Ticket | Conversation | Status mapping required (see below) |
| Thread (reply/comment) | Message | visibility field controls public vs. internal |
| Contact | Contact | Direct map; supports multiple identities |
| Account (Org) | Contact custom data | Flattened to key-value pairs — lossy |
| Department | Brand or Channel | Decide on 1:1 or N:1 mapping before import |
| Agent | Staff user | Must exist before import; cannot be created via API |
| Knowledge Base Article | FAQ Article | Requires HTML sanitization |
| Tags | Tags | Direct map via tag_list array |
| Custom Fields | Custom Data Attributes | Typed → untyped; one level deep only |
| Attachments | Attachments (URL-based) | Must be hosted at accessible URL before import |
| SLA Policies | ❌ Not available | No equivalent; rebuild with workflow automations |
| Blueprints/Workflows | ❌ Not available | Rebuild using Re:amaze workflow automations |
| Time Tracking | ❌ Not available | Export separately for archival |
| Community/Forums | ❌ Not available | No equivalent in Re:amaze |
| Satisfaction Ratings | Satisfaction Ratings | Re:amaze has its own CSAT system |
| Forward-type Threads | Message or Internal Note | Apply decision rule: external→message, internal→note |
| Merged ticket history | ❌ Not available | No relationship structure in Re:amaze |
| Response Templates/Macros | Response Templates | Cannot be imported via API; manual recreation required |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets / Conversations | medium | Tickets map directly to Conversations but require status translation logic, and Zoho's custom statuses must be dynamically resolved via the ticketStatusDefs endpoint to avoid hardcoding assumptions that break on non-standard configurations. |
| Threads / Messages | medium | Public replies and internal comments map to Re:amaze Messages with different visibility values, but forward-type threads require case-by-case classification with no fully automatable rule, creating a risk of data being silently miscategorized. |
| Contacts | low | Contacts have a direct structural equivalent in Re:amaze and support multiple identities, making this one of the cleanest mappings in the migration with low risk of data loss. |
| Accounts / Organizations | high | Zoho Desk's Account object has no equivalent in Re:amaze and must be flattened into contact-level custom data attributes and tags, permanently destroying account-level hierarchy, reporting, and the ability to view all tickets belonging to a single organization. |
| Custom Fields | high | Zoho Desk's typed, multi-layout custom fields collapse to flat, untyped string attributes in Re:amaze, losing all picklist validation, date formatting, checkbox logic, and multi-select structure with no mechanism to reconstruct them post-migration. |
| Attachments | medium | Re:amaze requires attachments to be referenced via publicly accessible URLs at import time, meaning all Zoho Desk attachments must be extracted, hosted on an accessible endpoint, and linked correctly before or during the load phase or they will be lost. |
| Tags | low | Tags map directly via the tag_list array in the Re:amaze API with no structural transformation required, representing one of the lowest-risk data entities in the migration. |
| Knowledge Base Articles | medium | Knowledge Base Articles map to Re:amaze FAQ Articles but require HTML sanitization to remove Zoho-specific markup, and there is no bulk import API, meaning large article libraries require scripted creation calls or manual recreation. |
| SLA Policies and Workflows | high | Zoho Desk's SLA policies, escalation rules, and Blueprints have no equivalents in Re:amaze and cannot be imported; all automation logic must be manually rebuilt using Re:amaze's workflow automation system, with no guarantee of functional parity. |
| Agents / Staff Users | medium | Re:amaze does not support staff user creation via the API, so all agents must be manually provisioned and verified in Re:amaze before migration begins, and any conversation assigned to a non-existent staff user will fail to import correctly. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
API Credit Exhaustion Risk
Extracting full ticket and thread data from Zoho Desk is credit-intensive; a dataset of 50,000 tickets with an average of 5 threads each requires over 250,000 API credits, exceeding most plans' daily limits by a factor of 2–10x and forcing multi-day chunked extraction strategies.
Account Object Has No Equivalent
Zoho Desk's first-class Account object, which supports B2B organizational hierarchies linking multiple contacts and tickets to a single company record, has no direct counterpart in Re:amaze and must be flattened into contact-level custom data attributes, resulting in irreversible loss of account-level reporting and hierarchical views.
Department-to-Brand Mapping Decision
Each Zoho Desk Department must be mapped either to a separate Re:amaze Brand or collapsed into Channels within a single Brand before import begins, and this structural decision is irreversible, with multi-Brand setups carrying separate agent pools and billing implications.
Thread Type Ambiguity on Import
Zoho Desk's forward-type threads have no direct Re:amaze equivalent and must be individually classified as either a customer-visible message or an internal note based on whether the original forward targeted an external party or an internal team, with no automated rule covering all cases.
Custom Field Type Fidelity Loss
Zoho Desk supports strongly typed, multi-layout custom fields (picklist, date, checkbox, multi-select) per department, while Re:amaze stores custom data as flat, untyped string key-value pairs, causing all type enforcement and valid-value constraints to be lost during migration.
On-Hold Status Requires Synthetic Datetime
Re:amaze's On-Hold conversation status requires a mandatory hold_until datetime value via the API, meaning any Zoho Desk On Hold tickets without a scheduled follow-up date must either receive a synthetic future date or be remapped to Open status to prevent import failures.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate Zoho Desk tickets to Re:amaze with a CSV export?
No. Zoho Desk's CSV export includes ticket metadata (subject, status, priority, dates) but not thread content, comments, or attachments. You need the Zoho Desk API or a Data Backup request to extract full conversation history for import into Re:amaze.
Does Re:amaze preserve original ticket timestamps during migration?
No. The Re:amaze API does not accept a custom created_at when creating messages. Imported messages carry the timestamp of the API call, not the original Zoho Desk thread date. Store original timestamps in custom data attributes or message body headers for reference.
How do I prevent Re:amaze from emailing customers during migration?
Set suppress_notifications, suppress_autoresolve, and suppress_surveys to true on every POST request when creating conversations and messages. Without these flags, Re:amaze sends live notifications for every imported message.
Can I migrate Zoho Desk Accounts to Re:amaze?
Re:amaze does not have a native Account object. Zoho Desk Accounts must be flattened and mapped to custom data attributes on the Re:amaze Contact object. Use consistent tags for account-level grouping in reporting.
How long does a Zoho Desk to Re:amaze migration take?
For under 5,000 tickets: 3–5 days. For 5,000–25,000 tickets: 5–10 days. For 25,000–100,000 tickets: 10–21 days. Primary bottlenecks are Zoho Desk API credit limits on extraction and Re:amaze rate limits on loading. Attachment re-hosting adds significant time for large volumes.