Dixa conversations extract via Exports API (31-day windows, 10 req/sec). Load into Salesforce as Cases + EmailMessages via Bulk API 2.0. The workflow rebuild — Dixa Flows to Salesforce routing and rules — takes 40–60% of total effort.
There is no native migration path from Dixa to Salesforce Service Cloud; the migration requires custom extraction via Dixa's Exports API and a fully scripted load process using Salesforce Bulk API 2.0. The fundamental data model difference is Dixa's flat user-conversation structure versus Salesforce's relational Account → Contact → Case hierarchy, meaning every record must be restructured and linked before import. Critical technical constraints include Salesforce's silent 4,000-character truncation on CaseComments (requiring EmailMessage as the correct target object), read-only CreatedDate fields requiring two separate org-level configuration steps to override, and Salesforce governor limits that affect large-scale inserts. Workflow logic presents the greatest effort: Dixa's single-canvas Flow Builder must be decomposed into Salesforce Assignment Rules, Escalation Rules, Omnichannel Routing Configurations, Entitlement Processes, and Salesforce Flow Builder — typically consuming 40–60% of total migration effort.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
TL;DR — Dixa to Salesforce Service Cloud Migration
Dixa conversations export via the Exports API (31-day query windows, 10 requests/second per token). The critical mapping decision: store conversation history as EmailMessages (up to 131,072 characters by default, configurable to 384,000) rather than CaseComments (hard 4,000-character truncation — silent, no error returned). Dixa's flat conversation model must be translated into Salesforce's Account → Contact → Case hierarchy. Preserving historical timestamps requires enabling "Set Audit Fields upon Record Creation" (org-level Setup) and assigning the "Set Audit Fields" user permission to the API user's profile or permission set — both steps are required. Dixa's Flow Builder logic must be rebuilt across Salesforce's distributed configuration model (Assignment Rules, Omnichannel Routing, Entitlement Processes, Flow Builder). Voice recordings should be offloaded to external storage (AWS S3) with URLs mapped to Cases. Use Salesforce Bulk API 2.0 for the data load — it handles up to 100 million records per 24-hour period and doesn't consume your org's REST API quota. Realistic timeline: 3–6 weeks depending on volume and Flow complexity.
Check your org's EmailMessage limit before migration
The default HtmlBody limit is 131,072 characters. Orgs with very large historical email threads should verify this limit via a test insert before running bulk loads.
Use a sandbox first
Always run the full migration in a Salesforce sandbox before touching production. Sandbox orgs get the same API limits as production, including Bulk API 2.0 capacity and governor limits.
CreatedDate requires two configuration steps
Enable "Set Audit Fields upon Record Creation" in Setup and assign the "Set Audit Fields" permission to the API user. Enabling only the org setting produces no error — the API call succeeds, but CreatedDate is silently set to the insert timestamp. You will not discover this until you query historical records and find every 2021 ticket shows today's date.
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 Dixa
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 Salesforce Service Cloud can hold your support model
Walk your current workflow through Salesforce Service Cloud: 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 Dixa → Salesforce Service Cloud 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.
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Set up Queues
in Salesforce matching your Dixa queue structure.
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Disable automation temporarily
Turn off assignment rules, escalation rules, auto-response rules, and Apex triggers that fire on Case insert. 100K Case inserts triggering email notifications will exhaust your org's daily email sending limits and create noise in your monitoring.
Dixa → Salesforce Service Cloud specifics
- CRM consolidation
- Running Dixa alongside Salesforce creates duplicate contact records, broken pipeline-to-support reporting, and manual data syncing. Service Cloud resolves this by putting support, sales, and success data on a single customer record.
- Enterprise reporting
- Salesforce Reports and Dashboards support cross-object joins and custom report types. Dixa's analytics are conversation-scoped; they cannot join to opportunity or account data without an external integration.
- Compliance requirements
- Salesforce offers HIPAA BAA eligibility, FedRAMP authorization (Government Cloud), and Shield Platform Encryption. Dixa holds SOC 2 Type II and GDPR compliance but does not publicly offer HIPAA or FedRAMP paths.
- Platform extensibility
- Salesforce AppExchange offers thousands of managed packages with native data access. Dixa's integration ecosystem is smaller and requires webhook-based data extraction.
- AI depth
- Salesforce Einstein for Service — case classification, next-best-action, article recommendations — operates across your full CRM dataset. Dixa's automation is strong within conversations but doesn't extend to CRM-wide context.
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 Dixa 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 Dixa 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 Dixa 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 -
Enable "Set Audit Fields upon Record Creation"
in Setup → User Interface → and assign the "Set Audit Fields" permission to the API user's profile or a dedicated permission set. Both steps are required — enabling the org setting without the user permission produces no error but CreatedDate remains today's date.
Dixa → Salesforce Service Cloud specifics
- 3 years of history
- = 36+ sequential API calls per endpoint (conversations + messages = 72+ calls minimum)
- At 10 req/sec with typical response times of 2–4 seconds per call
- (network + server processing), a full historical export takes approximately 5–15 minutes of wall-clock time for the API calls themselves
- The bottleneck is data volume per call
- , not request rate. A call returning 50,000 conversations may take 8–12 seconds. At 100K conversations across 3 years, plan for 30–90 minutes of extraction time including pagination and retry overhead
- Attachments are separate
- Binary file downloads from Dixa's CDN URLs are not subject to the same rate limit but add significant time proportional to file count and size
- /v1/conversation_export
- Returns conversation metadata: IDs, status, channel, queue, agent, timestamps, tags, custom attributes, wrap-up notes.
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 Dixa → Salesforce Service Cloud 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 Dixa → Salesforce Service Cloud 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 Salesforce Service Cloud 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 custom fields
on Case and Contact: Dixa_Conversation_Id__c (Text, External ID, Unique), Dixa_EndUser_Id__c (Text, External ID, Unique), CSAT_Score__c (Number), CSAT_Date__c (Date).
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 Salesforce Service Cloud sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Salesforce Service Cloud 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 Salesforce Service Cloud'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 Salesforce Service Cloud, 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 Dixa 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 Salesforce Service Cloud'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 Dixa 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 Dixa read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Dixa → Salesforce Service Cloud specifics
- Initial Sync
- Two weeks before go-live, extract all historical Dixa data up to a specific timestamp. Load into Salesforce sandbox for UAT, then production. Captures ~95% of total volume.
- Delta Sync
- On cutover weekend, query Dixa for conversations created or modified after the initial sync timestamp. Load the gap data.
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 Dixa and Salesforce Service Cloud 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 Salesforce Service Cloud, 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 Dixa 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.
Dixa Salesforce Data Mapping
| Dixa field | Salesforce Service Cloud field | Notes |
|---|---|---|
| Conversation | Case | One conversation = one Case. Status mapping required (open/pending/closed → picklist values). |
| Message (inbound/outbound) | EmailMessage | Preferred over CaseComment — supports HTML, up to 131K characters default, preserves sender/recipient. |
| Internal Note | CaseComment (IsPublished=false) | Or FeedItem on Case (Chatter). 4,000-character limit on CaseComment applies here too. |
| End User (contact) | Contact | Map email, phone, name. Link to Account if org uses Account hierarchy. |
| Agent | User | Match by email. Salesforce Users must exist before Case import. |
| Queue | Queue | Configured in Setup. Map Dixa queue names to Salesforce Queue DeveloperNames. |
| Tag | Case Topic or Custom Field | Topics allow multiple tags per record. Tags indicating specific processes (e.g., "Refund Requested") should map to custom fields for reporting. |
| Custom Attribute (conversation) | Custom Field on Case | Text → Text, Select → Picklist. |
| Custom Attribute (end user) | Custom Field on Contact | Same approach. |
| CSAT Rating | Custom Number Field on Case | Salesforce has no native CSAT field. Create CSAT_Score__c (Number) and CSAT_Date__c (Date). |
| Conversation Wrap-Up Note | CaseComment or Custom Field | Wrap-up notes are separate from message-level internal notes in Dixa's data model. |
| Attachment | ContentVersion → ContentDocumentLink | Files use the ContentVersion/ContentDocument model. Link via ContentDocumentLink to parent Case. |
| Knowledge Article | Knowledge__kav | Requires Record Types and Data Categories setup before import. PublishStatus must be set explicitly or articles import as Drafts. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Conversations (Cases) | medium | Conversation-to-Case mapping is structurally straightforward, but status picklist values, queue assignments, and priority fields require explicit transformation logic before load. |
| Messages (EmailMessages) | high | Using the wrong target object (CaseComment instead of EmailMessage) causes silent, permanent truncation of message content beyond 4,000 characters with no API error or truncation indicator. |
| Internal Notes (CaseComments) | medium | CaseComment bodies are hard-capped at 4,000 characters with silent truncation, so any internal notes exceeding this limit will lose content without warning during import. |
| Contacts (End Users) | medium | Every Salesforce Contact must be linked to an Account, so Dixa end users lacking company-level data require either a placeholder Account or a Person Account org configuration, which is irreversible. |
| Historical Timestamps | high | Failing to complete both required configuration steps — enabling org-level audit field creation and assigning the user permission — results in all migrated records receiving the import date as CreatedDate, permanently destroying historical chronology. |
| Custom Fields and Attributes | medium | Dixa custom attributes on conversations and end users must be manually mapped to pre-created Salesforce custom fields with compatible data types before bulk load, and any missing field mappings result in silently dropped data. |
| Attachments (ContentVersion) | medium | Attachments require a two-step Salesforce load — ContentVersion insertion followed by ContentDocumentLink creation — and binary file downloads from Dixa CDN URLs add significant extraction time proportional to file count and size. |
| CSAT Ratings | low | Salesforce has no native CSAT field, but the risk is low because custom fields (CSAT_Score__c and CSAT_Date__c) can be created before migration and the data itself is simple numeric and date values. |
| Knowledge Articles | high | Knowledge article import requires Record Types and Data Categories to be fully configured in Salesforce before load, and articles without explicit PublishStatus values will import as Drafts rather than published content. |
| Workflow and Routing Logic | high | Dixa Flow Builder logic has no direct import mechanism and must be entirely rebuilt manually across multiple Salesforce configuration objects, with no automated validation that the rebuilt logic replicates original routing behavior. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Flat-to-Relational Data Restructuring
Dixa's flat user-conversation model must be transformed into Salesforce's three-level Account → Contact → Case hierarchy, requiring entity resolution and relational linking for every imported record.
CaseComment Silent Truncation Risk
Salesforce silently truncates CaseComment bodies exceeding 4,000 characters with no API error or warning, requiring all conversation messages to be mapped to EmailMessage objects instead to prevent permanent data loss.
Historical Timestamp Preservation
Salesforce's CreatedDate field is read-only by default, and restoring historical timestamps requires both enabling 'Set Audit Fields upon Record Creation' at the org level and assigning the 'Set Audit Fields' permission to the API user — both steps are mandatory.
Flow Builder Logic Decomposition
A single Dixa Flow with 15–20 decision nodes must be rebuilt across 4–6 separate Salesforce configuration objects including Assignment Rules, Escalation Rules, Omnichannel Routing, and Salesforce Flow Builder, consuming an estimated 40–60% of total migration effort.
Native Telephony and Recording Gap
Dixa's built-in telephony, IVR, and call recordings have no direct equivalent in Salesforce without purchasing the Service Cloud Voice add-on or a third-party CTI adapter, and historical recordings require external storage offloading with URL mapping to Cases.
Dixa API Extraction Window Constraints
The Dixa Exports API enforces 31-day query windows and a 10 requests/second rate limit, requiring sequential pagination across multiple date ranges for multi-year historical exports and significantly extending total extraction time for large data volumes.
What breaks
Known failure modes. Have a recovery plan for each before you cut over.
Agent @mentions
Dixa allows agents to @mention each other using internal Dixa user IDs. When migrated to Salesforce CaseComment, these appear as plain text (@User1234). Re-linking to Salesforce Chatter mentions requires regex parsing and Dixa ID → Salesforce User ID mapping during transformation.
Call recordings
Salesforce cannot efficiently store audio files natively. Upload .wav or .mp3 files to AWS S3 and write the secure S3 URL into a custom URL field (Dixa_Recording_URL__c) on the Salesforce Case.
Agent matching
If Dixa agents don't exist as Salesforce Users, Cases cannot be assigned to them. Create all agent User records first, or map unresolved agents to a "Migration Import" user and store the original agent name in a custom text field.
Side conversations
Map to related Cases (use a lookup relationship) or to EmailMessages with a custom field flagging them as side conversations.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Should I use CaseComment or EmailMessage for Dixa conversation history in Salesforce?
Use EmailMessage in almost all cases. CaseComment has a hard 4,000-character limit on CommentBody that silently truncates content. EmailMessage supports up to 131,072 characters by default (configurable to 384,000), preserves HTML formatting, and retains sender/recipient metadata.
How do I preserve original Dixa timestamps when migrating to Salesforce?
Enable 'Set Audit Fields upon Record Creation' in Salesforce Setup and grant the API user the 'Set Audit Fields' permission. Without this, all migrated Cases will show the import date as their CreatedDate, destroying your historical timeline and SLA reporting.
Can Dixa Flows be migrated to Salesforce automatically?
No. Dixa Flows use proprietary visual routing logic with no export format. You must reverse-engineer each Flow from the visual canvas and rebuild the logic manually using Salesforce Assignment Rules, Escalation Rules, Omnichannel Routing, and Flow Builder.
How long does a Dixa to Salesforce Service Cloud migration take?
A typical migration takes 3–6 weeks, including data mapping, Salesforce org prep, test migration in a sandbox, workflow rebuild, UAT, and production cutover. The workflow redesign (Dixa Flows → Salesforce configuration) is usually the longest phase at 5–10 days.
Can I migrate Dixa call recordings to Salesforce?
Salesforce is not designed to store large audio files natively. The standard practice is to extract Dixa recordings, host them in AWS S3 or similar external storage, and write the secure URL into a custom URL field on the Salesforce Case.