Desk365 tickets map to Dixa conversations via API, but Dixa import only supports email and genericapimessaging channels. Plan 2–4 weeks for 50K+ tickets.
Migrating from Desk365 to Dixa has no native or third-party automated path as of January 2025, requiring a fully custom API-to-API or ETL-based approach. The two platforms use fundamentally incompatible data models: Desk365 stores discrete tickets with multi-state statuses, categories, and Microsoft 365-native constructs, while Dixa models everything as conversations routed through visual Flows and Queues with a binary Open/Closed state. Custom work is required to flatten Desk365's multi-status ticket lifecycle into Dixa's binary model, remap custom fields from cf_-prefixed ticket fields to UUID-based Custom Attributes, normalize all historical conversation threads into Dixa's supported import channel types (email or genericapimessaging), and manually rebuild all automations and SLA policies as Dixa Flows. Dixa's import endpoint has no idempotency guarantee, meaning deduplication logic must be implemented externally to prevent duplicate conversations on any retry.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
TL;DR — Desk365 to Dixa Migration
Migrating from Desk365 to Dixa moves your support operation from a ticket-centric, Microsoft 365-native helpdesk to a conversation-centric, push-based omnichannel routing platform. The two systems use fundamentally different data models: Desk365 stores discrete tickets with statuses and categories; Dixa models everything as conversations routed through Flows and Queues. A typical migration for 50,000–150,000 tickets takes 2–4 weeks including data mapping, test migrations, and cutover. The biggest technical challenges: Desk365's ticket replies must map into Dixa conversation messages, Dixa's import endpoint only supports email and genericapimessaging channel types, and Desk365 automations and SLAs cannot be migrated — they must be rebuilt as Dixa Flows manually. The Desk365 API v3 caps extraction at 10,000 tickets per hour; Dixa's API allows 10 requests per second per token with a daily ceiling of 864,000 requests. Dixa does not deduplicate on re-import — there is no native idempotency — so every retry without deduplication logic creates duplicate conversations. Teams with fewer than 10,000 tickets and no complex custom fields can attempt a scripted DIY migration (60–100 engineer-hours). For anything larger or with multi-level custom fields, a managed migration service is the safer path.
Rate limit math
At 10 requests/second, importing 50,000 conversations (plus tags, notes, and custom attributes) generates roughly 150,000–250,000 API calls. At maximum throughput, that's 4–7 hours of continuous loading — but real-world throughput with retries, validation, and 429 backoffs is typically 30–50% of theoretical maximum. Plan for 1–3 days of load time for 50K tickets. For a 200K-ticket migration with an average of 5 messages per ticket, plan for 3–10 days of load time if making separate API calls per conversation, message, note, and tag.
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 Desk365
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 Dixa can hold your support model
Walk your current workflow through Dixa: 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 Desk365 → Dixa 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.
Desk365 → Dixa specifics
- Omnichannel consolidation
- Desk365 channels are largely limited to email, Teams, and web forms. Dixa natively routes voice, email, chat, WhatsApp, and social messaging through a single engine — no add-ons.
- Push-based routing architecture
- Desk365 uses traditional pull-based views and round-robin assignment. Dixa's offer-based routing pushes conversations to agents based on skills, capacity, and priority — reducing cherry-picking and idle time.
- Scaling beyond Microsoft 365
- Teams whose customer base extends beyond the M365 ecosystem often outgrow Desk365's Microsoft-centric design.
- Tickets older than 2–3 years
- Weigh the cost of migrating rarely-accessed history vs. archiving to CSV.
- Resolved tickets with no replies
- If a ticket was auto-closed with no customer interaction, it may not need to exist in Dixa.
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 Desk365 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 Desk365 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 Desk365 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
Desk365 → Dixa specifics
- Base URL
- https://{yoursubdomain}.desk365.io/apis/v3/
- Authentication
- API key found in the Desk365 portal: Settings > Integrations > API (per Desk365 API documentation).
- Rate limit
- Maximum 10,000 tickets per hour (per Desk365 API v3 documentation).
- Pagination
- Use ticket_count parameter (30, 50, or 100 per call) with page offsets
- Filtering
- The default result returns all tickets sorted by creation date. You can filter by status, priority, type, group, agent, date range, and other attributes (per Desk365 API documentation).
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 Desk365 → Dixa 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 Desk365 → Dixa 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 Dixa 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.
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Create separate custom attributes
for each level (e.g., product_category, product_subcategory)
Desk365 → Dixa specifics
- Unused custom fields
- Desk365 lets you create any number of additional ticket fields (per Desk365 custom fields documentation) — audit which ones actually contain data.
- Concatenate levels
- "Level1 > Level2 > Level3" as a single text attribute
- Discard lower levels
- if they carry low signal — verify usage rates in the audit
- Custom fields
- Returned with cf_ prefix in v3 responses
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 Dixa sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Dixa 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 Dixa'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.
Desk365 → Dixa specifics
- Spam and test tickets
- Filter by status or tag before extraction.
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 Dixa, 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 Desk365 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 Dixa'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 Desk365 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.
-
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 Desk365 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 Desk365 and Dixa 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 Dixa, 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 Desk365 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.
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
| Desk365 field | Dixa field | Notes |
|---|---|---|
| Ticket | Conversation | 1:1, but structure changes fundamentally |
| Ticket Reply | Message (Inbound/Outbound) | Must specify direction and author |
| Private Note | Internal Note | Via POST /v1/conversations/{id}/notes after conversation import |
| Contact | End User | Email is the primary identifier |
| Company | End User custom attribute | Dixa has no native company object |
| Group | Team + Queue | Groups map to Teams; routing logic maps to Queues |
| Agent | Agent | Create agents in Dixa first; match by email |
| Tag | Tag | Direct mapping via Tag API |
| Custom Field | Custom Attribute | Must create attributes in Dixa first; each gets a UUID |
| Automation Rule | Flow | Manual rebuild required — no migration path |
| SLA Policy | SLA (in Dixa) | Manual configuration required |
| Canned Response | Quick Reply | Manual recreation required |
| Knowledge Base Article | Knowledge Base Article | Separate migration path (see below) |
Channel Import Type
| Desk365 field | Dixa field | Notes |
|---|---|---|
| Direct mapping; requires emailIntegrationId | ||
| Microsoft Teams | Loses original channel context | |
| Web Form | genericapimessaging | No integration ID required |
| Web Widget | genericapimessaging | No integration ID required |
Desk365 Dixa
| Desk365 field | Dixa field | Notes |
|---|---|---|
| Ticket Number | Custom Attribute | Store as desk365_ticket_id for audit and traceability |
| Subject | Subject | Direct map |
| Description | First Message | HTML to Dixa content; rehost inline images |
| Status | State | Map to open/closed; preserve original as tag |
| Priority | Tag | Create priority tags (e.g., priority:high) |
| Type | Tag | Map types to tags (e.g., desk365_type:Incident) |
| Contact Email | Requester | Resolve via End User API first |
| Contact Name | End User Display Name | Direct map |
| Company | Custom Attribute | Create company attribute on end user |
| Group | Queue | Map operational ownership to queue IDs |
| Assigned Agent | Agent | Map by email to Dixa agent UUID |
| Created Date | Created At | ISO 8601 UTC format |
| Custom Field | Custom Attribute | Type conversion required; flatten multi-level |
| Tags | Tags | Create tags in Dixa first, then apply via API |
| Private Notes | Internal Notes | Import separately after conversation |
| Attachments | Attachment URLs | Host on S3 if Desk365 URLs expire |
| Channel | Tag | Store as desk365_channel tag for provenance |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets / Conversations | medium | Tickets map reasonably to Dixa conversations via the import endpoint, but the Desk365 API's 10,000-ticket-per-hour extraction cap requires careful rate management for large datasets to avoid incomplete pulls. |
| Conversation Threads / Replies | high | Desk365 CSV exports do not include full reply threads, so conversation history requires full API extraction and transformation into Dixa's message model, with risk of thread ordering or participant attribution errors during mapping. |
| Contacts / End Users | low | Desk365 contacts map directly to Dixa End Users via the End Users API with straightforward field correspondence, making this one of the lower-risk entity transfers in the migration. |
| Companies | high | Dixa has no native company entity, so all company-level data from Desk365 must either be denormalized onto individual End User records or discarded, with no lossless mapping path available. |
| Custom Fields / Custom Attributes | high | Multi-level or complex custom fields require the most transformation effort, as <code>cf_</code>-prefixed Desk365 fields must be pre-created as UUID-based Custom Attributes in Dixa before migration and individually mapped with type validation. |
| Ticket Statuses | high | Desk365's multi-state status model including custom statuses collapses into Dixa's binary Open/Closed model, and any custom statuses without an explicit mapping strategy will be silently lost during migration. |
| Tags | low | Tags are supported in both platforms and can be carried over during conversation import with relatively low transformation complexity, though Desk365 category and type fields intended to become tags require explicit mapping decisions. |
| Attachments | medium | Attachments are not included in Desk365 CSV exports and must be retrieved via API, then re-uploaded to Dixa during the import process, introducing additional API call volume and potential for broken attachment references if URLs expire. |
| Automations / SLA Policies | high | There is no programmatic migration path for Desk365 automation rules or SLA policies into Dixa Flows; all routing logic must be manually rebuilt in Dixa's visual Flow builder, creating operational risk during the post-cutover period. |
| Knowledge Base Articles | medium | Both platforms have built-in knowledge bases, but there is no direct import bridge between them, requiring either manual recreation of articles or a custom export-and-load script with HTML content transformation. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Ticket-to-Conversation Model Mismatch
Desk365's discrete ticket model with multi-state statuses (Open, Pending, Resolved, Closed, plus custom statuses) must be translated into Dixa's binary Open/Closed conversation model, requiring explicit mapping of intermediate statuses to Dixa tags or Custom Attributes to avoid data loss.
Import Channel Type Restrictions
Dixa's <code>/v1/conversations/import</code> endpoint only accepts <code>email</code> and <code>genericapimessaging</code> as valid channel values, requiring all Desk365 conversation history — regardless of original channel — to be normalized into one of these two types before loading.
No Import Idempotency
Dixa's conversation import endpoint does not deduplicate on re-import, meaning any script retry or failure recovery without a custom deduplication layer in an intermediate staging store will create duplicate conversations in the target system.
Automations and SLAs Cannot Be Migrated
Desk365 automation rules and SLA policies have no direct export or import path into Dixa and must be fully rebuilt from scratch as Dixa Flows using the visual builder, representing a significant manual configuration effort outside the data migration scope.
Custom Field Schema Remapping
Desk365 custom ticket fields use a <code>cf_</code>-prefixed naming convention while Dixa Custom Attributes are UUID-based, requiring a field-by-field mapping exercise and pre-creation of all target Custom Attributes in Dixa before any data load can begin.
No Native Company Entity in Dixa
Desk365 supports a native Company object linked to Contacts, but Dixa has no equivalent company entity, meaning company-level relationships and metadata must be flattened into End User attributes or dropped entirely during migration.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate Desk365 tickets to Dixa using CSV?
Only partially. Desk365 exports tickets as CSV, but Dixa has no native CSV import for conversations — you still need the API for loading. CSV exports also exclude full reply threads and attachments, so they are only useful for small contact/company seeds or audit extracts.
What Dixa API channels are supported for importing historical conversations?
Dixa's POST /v1/conversations/import endpoint only supports 'email' and 'genericapimessaging' channel types. Phone calls, live chats, Teams-originated tickets, and social messages from Desk365 must be normalized into one of these two types during migration.
How long does a Desk365 to Dixa migration take?
A typical migration for 50,000–150,000 tickets takes 2–4 weeks including data mapping, test migrations, and cutover. Dixa's API rate limit (10 requests/second per token, 864,000 daily ceiling) is the primary bottleneck for the data load phase.
Does Dixa support custom fields from Desk365?
Yes, but with structural differences. Desk365's custom ticket fields (including multi-level dropdowns) must be mapped to Dixa's flat custom attributes. Multi-level fields need to be flattened — either concatenated into a single text value or split into separate attributes.
Can Desk365 automations and SLAs be migrated to Dixa?
No. Desk365 automation rules, SLA policies, and canned responses cannot be programmatically migrated. They must be manually rebuilt in Dixa using the visual Flow Builder, SLA configuration, and Quick Replies.