Helpdesk migration

Migrate from Mava to Salesforce Service Cloud

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 logo

Mava

Source

Salesforce Service Cloud

Destination

Salesforce Service Cloud logo

Compatibility

70%

7 of 10

objects map 1:1 between Mava and Salesforce Service Cloud.

Complexity

BStandard

Timeline

2-4 weeks

Rollback included Accuracy guarantee Field-level validation

Overview

What this migration involves

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.

Field-level fidelity

Every standard and custom field arrives verified.

Schema-aware mapping

AI proposes the map; you confirm before any record moves.

Relationships preserved

Parent–child, lookups, and ownership stay linked.

Full activity history

Calls, emails, meetings — with original timestamps.

Attachments & notes

Documents, uploads, and inline notes move with the record.

Why teams make this switch

Two sides of the same decision

Leaving

Mava logo

Mava

What's pushing teams away

  • Limited third-party integrations beyond Discord, Telegram, and website chat mean teams with established tech stacks often outgrow Mava and migrate to more flexible platforms.
  • Small review sample size makes it difficult to assess long-term reliability and enterprise-readiness before committing to the platform at scale.
  • Enterprise tier pricing is not publicly documented, which creates uncertainty for mid-market companies evaluating budget and scoping requirements.

Choosing

Salesforce Service Cloud logo

Salesforce Service Cloud

What's pulling them in

  • Deep Salesforce ecosystem integration with Sales Cloud, Marketing Cloud, and custom Apex apps creates a single pane of glass for enterprise customer data and cross-functional workflows.
  • Omnichannel case routing — email, chat, phone, social, and messaging — unified under one case object means agents do not lose context when customers switch channels mid-interaction.
  • AI for customer service (Einstein AI / Agentforce) offers automated case classification, suggested replies, and chatbot routing that reduces Tier-1 ticket volume without manual rule authoring.
  • Entitlement and milestone tracking enforces SLA compliance natively, automatically calculating breach windows and surfacing violations to supervisors in dashboards.
  • Salesforce's massive AppExchange ecosystem provides pre-built connectors, industry-specific managed packages, and third-party tools that extend Service Cloud beyond its out-of-box capabilities.

Object mapping

How Mava objects map to Salesforce Service Cloud

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

maps to

Salesforce Service Cloud

Case

1:1
Fully supported

Mava 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)

maps to

Salesforce Service Cloud

Contact

1:1
Fully supported

Mava 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

maps to

Salesforce Service Cloud

User

1:1
Fully supported

Mava 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

maps to

Salesforce Service Cloud

Queue

1:1
Fully supported

Mava 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

maps to

Salesforce Service Cloud

Flow (documentation only)

lossy
Fully supported

Mava'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

maps to

Salesforce Service Cloud

Custom Multi-Select Picklist

1:1
Fully supported

Mava 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

maps to

Salesforce Service Cloud

Entitlement Process

lossy
Fully supported

Mava'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

maps to

Salesforce Service Cloud

Outbound Message / Flow (documentation only)

1:1
Fully supported

Mava 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

maps to

Salesforce Service Cloud

ContentDocument

1:1
Fully supported

File 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

maps to

Salesforce Service Cloud

Case Field

lossy
Fully supported

Mava'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.

Gotchas + challenges

What specifically takes care here

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 logo

Mava gotchas

Medium

Community identity linkage may be incomplete

Low

Bot configurations are channel-specific

Low

Webhook payloads lack standardized schema

Salesforce Service Cloud logo

Salesforce Service Cloud gotchas

High

Data Export 512MB file size cap breaks large org exports

High

API Daily Request Limits vary by license edition

High

No automatic data backup in base Salesforce

Medium

Picklist dependencies silently break records when unmapped

Medium

Workflow rules fire unexpectedly during data load

Pair-specific challenges

  • Discord and Telegram identity linkage may block Contact resolution

    Mava links users to their source platform identity (Discord ID or Telegram ID) rather than a verified email in many cases. Salesforce requires Contacts to be associated with an email address or Salesforce Personal Account identifier. Before migration, we extract all distinct community IDs, run an email match against the destination org's Contact table, and flag records where no email match exists. These records require enrichment with a valid email address or must be converted to anonymous contacts with a community_id__c custom field. Skipping this step results in Cases without a valid Contact lookup, which breaks assignment rules, entitlement processes, and SLA tracking in Salesforce.

  • Mava has no documented public API

    Unlike most CRM and helpdesk platforms, Mava does not publish API documentation in the research data available. This means our extraction approach relies on any available data export endpoints or direct database access if Mava provides it. If Mava exposes a CSV export or a private API for migration purposes, we use it with rate-limit and chunking controls. If no export mechanism exists, we coordinate directly with Mava's support team to obtain a data export and adapt our extraction pipeline accordingly. This adds scoping time and potential data format risk compared to migrations from platforms with documented REST APIs.

  • AI bot intents and automation rules do not migrate to Salesforce Flow

    Mava's AI bot intents and per-channel automation rules are built on Mava's own bot framework and do not have a direct equivalent in Salesforce Service Cloud. Einstein Bots and Service Cloud Flow use different intent classification models, trigger types, and action builders. We extract the intent catalog, automated response templates, and routing conditions across all channels and deliver a written inventory document that the customer's admin uses to rebuild bots in Einstein Bot Builder or automations in Salesforce Flow post-migration. This is not a limitation of our migration tooling — it is a structural difference between the two platforms' automation architectures.

  • Case Comments have a 4,000-character limit in Salesforce

    Salesforce Case Comments are capped at 4,000 characters per comment. Mava conversation messages can exceed this length, particularly for long diagnostic threads or copied error logs. We split messages exceeding 4,000 characters into multiple Case Comments during migration, with a note appended indicating continuation. For internal agent notes that are longer, we consider migrating them as Salesforce Files attached to the Case instead of Case Comments to preserve full content without splitting.

  • Webhook payloads require manual rebuild post-migration

    Mava webhook configurations are customer-defined with no standard payload schema. We extract the endpoint URLs, trigger events, and authentication configuration and document them in the migration manifest. Payload transformation logic does not migrate because it is specific to each external system's expected format. The customer's admin reviews our webhook documentation and rebuilds the integrations in Salesforce using Outbound Messages, Platform Events, or Flow HTTP callouts with the correct payload structure for each destination system.

Migration approach

Six steps for a successful Mava to Salesforce Service Cloud data migration

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

Context on both ends of the pair

Mava logo

Mava

Source

Strengths

  • Deep Discord and Telegram native integration for community-first support workflows
  • AI bot automation with intent-based routing reduces manual triage workload
  • Free tier available for small teams and early-stage community projects
  • Pricing tiers scale from free through enterprise with clear feature differentiation

Weaknesses

  • Limited third-party integrations beyond core community channels
  • No publicly documented API or developer documentation in the research data
  • Enterprise pricing opaque without direct sales engagement
  • Small review sample size limits visibility into long-term reliability
Salesforce Service Cloud logo

Salesforce Service Cloud

Destination

Strengths

  • Enterprise-grade security, compliance certifications, and audit logging available across all paid editions with Shield offering enhanced event monitoring.
  • Scalable multi-tenant cloud architecture supporting orgs from 5 users to 150,000+ seat enterprises without infrastructure management overhead.
  • Omnichannel contact center unifying email, live chat, phone, messaging, and social into a single Case timeline per customer interaction.
  • Rich workflow automation via Salesforce Flow, Process Builder, and Apex triggers enabling complex case escalation, routing, and field updates.
  • Native AI capabilities (Agentforce / Einstein) for case auto-routing, classification, suggested responses, and chatbot escalation without third-party add-ons.

Weaknesses

  • Per-seat pricing model with no contact limits creates unpredictable cost scaling for large organizations adding many agents over time.
  • No automatic data backup — organizations must purchase a third-party backup solution or build manual Data Loader exports to protect against data loss from human error, failed deployments, or integrations overwriting records.
  • Steep learning curve for non-technical users requiring dedicated admin resources and formal training investment before teams reach productive velocity.
  • Annual contract requirements and limited pro-ration on exit create significant switching cost friction, especially for organizations evaluating alternatives mid-cycle.
  • Add-on licensing (CPQ, Einstein Activity Capture, Shield, Data Cloud) can double effective per-seat cost without clear documentation of which features are included in base tiers.

Complexity grading

How hard is this migration?

Standard Helpdesk migration. 1 of 7 objects need a manual workaround.

B

Overall complexity

Standard migration

Derived from compatibility, mapping clarity, API constraints, and data volume across Mava and Salesforce Service Cloud.

  • Object compatibility

    B

    1 of 7 objects need a manual workaround.

  • Field mapping clarity

    C

    Field mapping is derived from defaults — final spec confirmed during the sample migration.

  • Timeline complexity

    B

    7-object category — typical timelines run 2–7 days end-to-end.

  • API constraints

    B

    Mava: Not publicly documented..

  • Data volume sensitivity

    B

    Mava doesn't expose a bulk API — REST + parallelization used for high-volume runs.

Estimator

Estimate your Mava to Salesforce Service Cloud migration cost

Rule-based pricing — no per-record fees, no manual quotes. Migrations over 2M records are scoped individually.

Step 1

What are you migrating?

Pick a category, then your source and destination platforms.

Category

FAQ

Frequently asked questions about Mava to Salesforce Service Cloud data migrations

Answers to the questions buyers ask most during Mava to Salesforce Service Cloud migration scoping. Not seeing yours? Book a call.

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Most migrations land between two and four weeks for accounts with under 10,000 Conversations and cleanly resolvable community identities (Discord or Telegram member IDs with email addresses). Migrations with large conversation histories (over 100,000 messages), unresolved identity gaps requiring customer enrichment, multiple channels with distinct bot configurations, or complex SLA policies move to six to ten weeks because of identity reconciliation time, Bulk API chunking, and entitlement model setup.

Adjacent paths

Related migrations to explore

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