Helpdesk migration
Field-level mapping, validation, and rollback between Mava and Salesforce Service Cloud. We move data and schema; workflows are rebuilt natively in Salesforce Service Cloud.
Mava
Source
Salesforce Service Cloud
Destination
Compatibility
7 of 10
objects map 1:1 between Mava and Salesforce Service Cloud.
Complexity
BStandard
Timeline
2-4 weeks
Overview
Moving from Mava to Salesforce Service Cloud is a migration from a community-native, AI-bot-first support platform into an enterprise service management platform with a case-object model. Mava organizes support around Conversations sourced from Discord, Telegram, and web chat, linked to community member identities rather than verified email contacts. We resolve those community IDs against Salesforce's standard Contact and Account records, map conversation threads into Cases with the correct Case Origin tied to the source channel, and preserve message history and timestamps throughout the transfer. Mava's AI bot intents, channel-specific automation rules, and custom webhook payloads do not migrate as functional code; we extract and document them for the customer's admin to rebuild in Salesforce Flow or Service Cloud Voice. SLA configurations map to Service Cloud's entitlement model but require pre-migration setup of entitlement processes and milestones. Teams migrate to Salesforce Queues, and tags map to custom multi-select fields on Case.
Every standard and custom field arrives verified.
AI proposes the map; you confirm before any record moves.
Parent–child, lookups, and ownership stay linked.
Calls, emails, meetings — with original timestamps.
Documents, uploads, and inline notes move with the record.
Source platform
Mava platform overview
Scorecard, SWOT, gotchas, and pricing for Mava.
Destination platform
Salesforce Service Cloud platform overview
Scorecard, SWOT, gotchas, and pricing for Salesforce Service Cloud.
Data migration guide
The complete Salesforce Service Cloud migration guide
Data model, import mechanisms, field mapping strategy, pitfalls, and cutover — by the engineers running it.
Destination checklist
Salesforce Service Cloud migration checklist
Pre- and post-cutover tasks for moving onto Salesforce Service Cloud.
Why teams make this switch
Leaving
What's pushing teams away
Choosing
What's pulling them in
Object mapping
Each row shows how a Mava object lands in Salesforce Service Cloud, including any object-level transformations, lookup resolution, or schema-design dependencies.
Typical mapping — final map is confirmed during the sample migration step.
Mava
Conversation
Salesforce Service Cloud
Case
1:1Mava Conversations migrate to Salesforce Case records. Case Origin maps from Mava's channel source field (Discord, Telegram, or web) to the corresponding Service Cloud channel value. Message history within each Conversation migrates as Case Comments, with the original timestamp preserved. Mava conversation status (open, pending, resolved, closed) maps to Salesforce Case Status values that we configure during schema design. Private notes from Mava agents migrate as internal Case Comments visible only to Service Cloud users, and public replies migrate as standard Case Comments linked to the customer Contact.
Mava
Community Member (Discord/Telegram ID)
Salesforce Service Cloud
Contact
1:1Mava community members linked by Discord user ID or Telegram user ID require identity resolution before Case creation. We extract all distinct platform IDs from Mava's member records and match them against Salesforce Contacts by email where available. For members without email addresses, we create Contact records with the platform ID stored in a custom field community_id__c, and we flag these records for customer enrichment during the scoping phase. Unresolved identities are documented in a reconciliation report with a recommendation to enrich records before production migration.
Mava
Agent
Salesforce Service Cloud
User
1:1Mava Agents map directly to Salesforce Users. We match by email address as the dedupe key. Any Mava Agent without a corresponding Salesforce User record goes to the reconciliation queue for the customer's admin to provision before production migration. Agent role (admin, agent, viewer) maps to Salesforce Profile and Permission Set assignments, which the customer's admin configures in the destination org.
Mava
Team
Salesforce Service Cloud
Queue
1:1Mava Teams migrate to Salesforce Queues. Each Queue is created in Service Cloud with the relevant object access (Case, Lead, Social Post) before Case migration begins. Team member lists from Mava resolve to Salesforce User IDs via the Agent mapping, and Queue membership is granted by adding each User to the Queue. Mava's routing rules that assign conversations to teams do not migrate; we document the routing logic for the customer's admin to rebuild using Salesforce Omni-Channel routing configuration.
Mava
AI Bot Intent
Salesforce Service Cloud
Flow (documentation only)
lossyMava's AI bot intents are configured per channel with automated response rules and intent-based routing. These configurations are stored as JSON payloads that vary by channel type and do not map directly to Salesforce's automation model. We extract the full intent catalog across all channels, document the trigger conditions, automated actions, and response templates, and deliver this as a written inventory for the customer's admin to rebuild in Salesforce Flow, Einstein Bots, or Service Cloud Voice if they implement telephony. Mava's bot configurations do not migrate as functional code.
Mava
Tag
Salesforce Service Cloud
Custom Multi-Select Picklist
1:1Mava conversation tags migrate to a custom multi-select picklist field on Case. We preserve tag names as-is and add them as picklist values during schema design. If a customer uses more than 500 distinct tags, we discuss a tag consolidation strategy during scoping to avoid picklist overflow limits.
Mava
SLA Policy
Salesforce Service Cloud
Entitlement Process
lossyMava's first-response and resolution time targets map to Salesforce Entitlement Processes with milestones. The destination org requires pre-migration setup of Entitlement Processes and Service Contracts before SLA records can be created. We extract Mava's SLA configurations per channel and team, document the target response and resolution times, and provide a setup guide for the customer's Salesforce admin to create matching Entitlement Processes before production migration. SLA records then attach to Cases during migration.
Mava
Custom Webhook
Salesforce Service Cloud
Outbound Message / Flow (documentation only)
1:1Mava webhook configurations are customer-defined with no standardized payload schema. We extract the webhook endpoint URLs, HTTP methods, authentication types, and trigger conditions from Mava's configuration and document them in the migration manifest. Webhook payloads do not migrate because they are external system configurations, not records. The customer's admin uses our documentation to recreate the webhook integrations in Salesforce using Outbound Messages, Platform Events, or Flow-based HTTP callouts.
Mava
Attachment
Salesforce Service Cloud
ContentDocument
1:1File attachments on Mava conversations migrate as Salesforce ContentDocument records linked to the corresponding Case via ContentDocumentLink. We preserve file name, MIME type, and original upload date. Files are uploaded to Salesforce via the REST API using chunked encoding for files exceeding 25 MB. Files without a resolvable parent Case are held in a staging location for manual association.
Mava
Channel Source Metadata
Salesforce Service Cloud
Case Field
lossyMava's channel source (Discord, Telegram, or web) per conversation is preserved in a custom Case field Original_Channel__c as a picklist value. This field enables the customer's admin to filter and report on case volume by original channel post-migration. Channel-specific metadata such as Discord thread ID or Telegram message ID is preserved in a custom text field for reference.
| Mava | Salesforce Service Cloud | Compatibility | |
|---|---|---|---|
| Conversation | Case1:1 | Fully supported | |
| Community Member (Discord/Telegram ID) | Contact1:1 | Fully supported | |
| Agent | User1:1 | Fully supported | |
| Team | Queue1:1 | Fully supported | |
| AI Bot Intent | Flow (documentation only)lossy | Fully supported | |
| Tag | Custom Multi-Select Picklist1:1 | Fully supported | |
| SLA Policy | Entitlement Processlossy | Fully supported | |
| Custom Webhook | Outbound Message / Flow (documentation only)1:1 | Fully supported | |
| Attachment | ContentDocument1:1 | Fully supported | |
| Channel Source Metadata | Case Fieldlossy | Fully supported |
Gotchas + challenges
Platform-specific issues from each side, plus the pair-specific challenges that don't show up on either platform's page on its own.
Mava gotchas
Community identity linkage may be incomplete
Bot configurations are channel-specific
Webhook payloads lack standardized schema
Salesforce Service Cloud gotchas
Data Export 512MB file size cap breaks large org exports
API Daily Request Limits vary by license edition
No automatic data backup in base Salesforce
Picklist dependencies silently break records when unmapped
Workflow rules fire unexpectedly during data load
Pair-specific challenges
Migration approach
Discovery and scoping
We audit the source Mava workspace for conversation volume, active channels (Discord, Telegram, web), agent count, team structure, tag taxonomy, SLA configurations, AI bot intent catalogs, and webhook endpoint inventory. We assess identity resolution feasibility by extracting all distinct community member IDs and checking for email addresses in the member records. We pair this with a review of the target Salesforce org's existing schema — existing Contact and Account structures, active Queues, Entitlement Processes, and Record Types — to determine whether the destination org requires new schema elements before migration. The discovery output is a written migration scope, an identity reconciliation plan for community IDs, and a Salesforce Service Cloud edition recommendation.
Identity resolution and enrichment
We run the community ID to email resolution across all Mava member records. Records with a resolvable email are matched to existing Salesforce Contacts. Records without email are flagged and handed to the customer for enrichment. We provide a CSV template listing each unresolved community ID with a recommendation to add a valid email address or confirm that the record should be migrated as an anonymous Contact. Migration cannot proceed to Case import until Contact resolution is complete because Salesforce's entitlement and SLA tracking requires a valid Contact lookup on Case.
Schema design and entitlement setup
We configure the Salesforce Service Cloud destination schema before any data import. This includes creating custom fields (community_id__c, Original_Channel__c, SLA_Target_Response__c, SLA_Target_Resolution__c), adding channel values to Case Origin picklist, creating Queues mapped from Mava Teams, and setting up Entitlement Processes and Entitlements linked to the migrated SLA policies. Schema is deployed to a Salesforce Sandbox for validation before production migration begins. The customer's Salesforce admin reviews and approves the schema configuration before we proceed.
Sandbox migration and reconciliation
We run a full migration into a Salesforce Sandbox using production-like data volume. The customer's Service Cloud admin reviews record counts (Cases in, Contacts in, Queues populated, SLA records attached), spot-checks 25-50 random Cases against the source Mava conversations for message accuracy and timestamp fidelity, and verifies that internal notes appear correctly as restricted Case Comments. Any mapping corrections and schema adjustments happen in the Sandbox before production migration begins.
Production migration in dependency order
We run production migration in record-dependency order: Salesforce Users (validated from agent reconciliation), Contacts (with community ID resolution applied), Entitlements and Entitlement Processes (established before Cases), Cases (with ContactId, QueueId, EntitlementId, and Original_Channel__c resolved), Case Comments (message history threaded to parent Case via Bulk API with chunking for large message volumes), ContentDocuments (file attachments via REST API with chunked upload for large files), and finally webhook endpoint documentation delivered as a manifest for the customer's admin to rebuild. Each phase emits a row-count reconciliation report before the next phase begins.
Cutover, validation, and rebuild handoff
We freeze Mava writes during cutover, run a final delta migration of any conversations created or modified during the migration window, then enable Salesforce Service Cloud as the system of record. We deliver the AI Bot Intent inventory, webhook configuration manifest, and Mava automation rules document to the customer's admin team. We support a one-week hypercare window where we resolve any data reconciliation issues raised by the support team. We do not rebuild Mava's bot intents as Einstein Bots or rebuild Mava's automations as Salesforce Flow inside the migration scope; these are separate engagements handled by a Salesforce partner or the customer's admin team.
Platform deep dives
Mava
Source
Strengths
Weaknesses
Salesforce Service Cloud
Destination
Strengths
Weaknesses
Complexity grading
Standard Helpdesk migration. 1 of 7 objects need a manual workaround.
Overall complexity
Standard migration
Derived from compatibility, mapping clarity, API constraints, and data volume across Mava and Salesforce Service Cloud.
Object compatibility
1 of 7 objects need a manual workaround.
Field mapping clarity
Field mapping is derived from defaults — final spec confirmed during the sample migration.
Timeline complexity
7-object category — typical timelines run 2–7 days end-to-end.
API constraints
Mava: Not publicly documented..
Data volume sensitivity
Mava doesn't expose a bulk API — REST + parallelization used for high-volume runs.
Estimator
Rule-based pricing — no per-record fees, no manual quotes. Migrations over 2M records are scoped individually.
Step 1
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