Migrating Gladly to Freshdesk requires sessionizing continuous conversations into discrete tickets. Biggest risks: Freshdesk API won't set historical timestamps, and off-the-shelf tools can't handle the split logic.
There is no native migration path from Gladly to Freshdesk, and no off-the-shelf tool handles this transition well due to fundamentally incompatible data models. Gladly uses a person-centric, ticketless architecture where all interactions exist as one continuous conversation per customer, while Freshdesk organizes support around discrete, numbered tickets with defined lifecycles. Migrating requires custom sessionization logic to break Gladly's lifelong conversation threads into individual Freshdesk tickets, along with extensive field-level mapping, custom field pre-creation, and manual rebuilding of automations, SLA policies, and AI workflows that have no direct equivalents in Freshdesk.
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Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
TL;DR — Gladly to Freshdesk Migration
Migrating from Gladly to Freshdesk is a high-complexity project because the platforms use fundamentally incompatible data models. Gladly uses a person-centric, ticketless model — one continuous conversation per customer — while Freshdesk uses discrete tickets. You must "sessionize" Gladly's lifelong conversation threads into individual Freshdesk tickets, which no off-the-shelf tool handles well. A typical migration takes 2–3 weeks including mapping, sessionization logic, testing, and cutover. The single biggest risk: Freshdesk's standard public API does not let you set historical created_at timestamps on tickets, so without workarounds through Freshdesk support, all migrated tickets show today's date. Gladly Rules, SLA policies, People Match configurations, and Sidekick Guides cannot be migrated and must be rebuilt manually. Teams with fewer than 5,000 conversation items and simple schemas can attempt a scripted DIY migration. For anything larger or with complex sessionization requirements, a managed migration service is the safer path. Targets Gladly REST API v1 and Freshdesk API v2.
What has no clean equivalent on Freshdesk
Gladly's People Match (AI-powered customer identification), Sidekick Guides (conversational AI workflows), Lookup Adaptor integrations, and the continuous Conversation Timeline structure have no direct Freshdesk equivalents. These must be rebuilt using Freshdesk's native automation tools, Freddy AI, or custom integrations.
Gladly data retention post-cancellation
Gladly does not publicly document how long your data remains accessible after you cancel your subscription. Before initiating the migration, confirm with your Gladly account manager: (1) how long after cancellation you can still access the Export API, (2) whether export files generated before cancellation remain available for the full 14-day window, and (3) whether REST API access is revoked immediately on cancellation or after a grace period. Extract and archive all data locally before canceling your Gladly contract.
Gladly API Rate Limits
Gladly enforces a default rate limit of 10 requests per second across all HTTP methods (GET, POST, PUT, PATCH, DELETE). Exceeding this returns HTTP 429. Monitor usage via response headers: Ratelimit-Limit-Second and Ratelimit-Remaining-Second. The Reports API is slower at 10 requests per minute with a concurrency limit of two. At 10 req/sec you can make 36,000 API calls per hour — but the Export API's bulk JSONL files are far more efficient for large datasets. (developer.gladly.com)
Trial account trap
Freshdesk trial accounts are throttled to 50 API calls per minute. If you run test migrations against a trial instance, your scripts will hit 429 errors within seconds. Budget for a paid plan before serious testing, or request a temporary limit increase from Freshdesk support for your sandbox environment. Provisioning a test environment: Create a separate Freshdesk account on the plan tier you intend to use in production (e.g., Pro at 400 calls/min). This ensures your migration scripts are tested against realistic rate limits. Freshdesk offers 14-day free trials on all paid plans — use this for your test migration, then convert to paid for the production run. (support.freshdesk.com)
Choosing the right session gap
Start with 48 hours and validate against 50–100 sample customers. If your support team handles fast-turnaround e-commerce issues (average resolution under 4 hours), 24 hours may produce more natural ticket boundaries. If you handle longer B2B cases (average resolution 3–7 days), 72 hours or topic-based splitting may be better. If old history is mostly reference material, consider migrating only the active window (e.g., last 12–24 months) and archiving the rest as a static JSONL backup — that is usually better than forcing years of passive context into live tickets.
Freshdesk validation gotcha
Freshdesk's list tickets endpoint defaults to the last 30 days and maxes out at 30,000 tickets. Validation scripts need the updated_since parameter or Freshdesk data exports (Admin → Account → Export Data) for older or larger datasets. Do not rely on include=conversations on the tickets endpoint, which only returns up to ten entries per ticket and consumes extra API credits against your rate limit. (developers.freshdesk.com)
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 Gladly
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 Freshdesk can hold your support model
Walk your current workflow through Freshdesk: 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 Gladly → Freshdesk 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.
Gladly → Freshdesk specifics
- Cost structure
- Gladly does not publish pricing and requires a sales engagement for any budget estimate. Freshdesk's Growth plan starts at $15/agent/month, with transparent, published pricing. Freshdesk also offers a free tier (up to 2 agents) that Gladly has no equivalent to.
- Ecosystem breadth
- Freshdesk's marketplace lists 1,000+ pre-built integrations across categories including CRM, e-commerce, analytics, and DevOps. Gladly's integration model centers on its Lookup Adaptor pattern and a smaller set of native connectors (Shopify, Magento, Salesforce, Klaviyo), which can limit teams that need to connect a wider range of tools.
- B2B and multi-vertical fit
- Gladly's architecture is optimized for retail, DTC, and hospitality — the "people, not tickets" model is purpose-built for consumer brand loyalty. Teams expanding into B2B, SaaS, or ITSM use cases often find Freshdesk's ticket-centric model a better structural fit, especially with Freshdesk's multi-product portal support for managing multiple brands or product lines from a single account.
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 Gladly 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 Gladly 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 Gladly 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
Gladly → Freshdesk specifics
- File types
- conversation_items.jsonl, customers.jsonl, agents.jsonl, topics.jsonl
- Scheduling
- Daily by default. Hourly schedules available with approximately 2 hours of lag. Contact Gladly Support to change frequency.
- Availability
- Export files are available for 14 days, then auto-deleted. Archive them locally before they expire.
- Date range
- Export all communications delivered within a custom date range
- Ticket creation
- POST /api/v2/tickets — creates tickets with the current timestamp by default.
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 Gladly → Freshdesk 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 Gladly → Freshdesk 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 Freshdesk 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.
Gladly → Freshdesk specifics
- Custom fields
- Pass inside a custom_fields object with keys prefixed cf_. Use GET /api/v2/ticket_fields to discover API names.
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 Freshdesk sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Freshdesk 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 Freshdesk'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 Freshdesk, 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 Gladly 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 Freshdesk'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 Gladly 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 Gladly 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 Gladly and Freshdesk 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 Freshdesk, 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 Gladly 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.
Gladly Freshdesk Object Mapping Matrix
| Gladly field | Freshdesk field | Notes |
|---|---|---|
| Customer | Contact | Map name, emails, phones, address. Gladly stores multiple emails/phones per Customer natively; Freshdesk Contact supports multiple emails but only one primary. |
| Customer customAttributes | Contact custom fields (cf_*) | Gladly custom attributes come from Lookup Adaptors (Shopify, custom API). Must be manually mapped to Freshdesk custom fields with cf_ prefix. |
| Customer business/account data | Company | Freshdesk companies support domains; use them for domain-based association. |
| Conversation | Ticket(s) | One Gladly Conversation → multiple Freshdesk Tickets. Requires sessionization logic. |
| Conversation Item (EMAIL) | Ticket Reply or Note | Map initiator.type = CUSTOMER to public note with incoming: true, AGENT to agent note or reply. |
| Conversation Item (CHAT_MESSAGE) | Ticket Note (private) | No native chat-to-ticket replay; store as private notes with channel metadata. |
| Conversation Item (VOICE) | Ticket Note (private) | Voice transcripts (if available) become notes. Call recordings require separate attachment handling. |
| Conversation Item (SMS) | Ticket Note (private) | SMS messages stored as notes with original phone number metadata. |
| Topic | Tag | Gladly Topics are labels applied to Conversations for categorization. Map directly to Freshdesk Tags. |
| Freeform Topic attributes | Ticket custom fields | Conversation-level custom attributes in Gladly (e.g., Order Number) map to Freshdesk ticket custom fields. |
| Agent | Agent | Map by email address. Verify Freshdesk agent seat limits before import. |
| Team / Inbox | Group | Gladly Inboxes route by channel + destination; Freshdesk Groups route by team function. Not always 1:1. |
| Task | Ticket (type = task) or Note | Gladly Tasks are standalone work items. Map open tasks to Freshdesk tickets, closed tasks to private notes. |
| Answer | Solution Article | Gladly Answers (knowledge base) map to Freshdesk Solutions. Freshdesk Solutions are organized in a three-tier hierarchy: Category → Folder → Article. Create categories and folders via POST /api/v2/solutions/categories and POST /api/v2/solutions/folders/{category_id}/folders before importing articles. Multi-language content requires per-language article creation using the {language} variant endpoints. Inline formatting often needs HTML cleanup. |
| Rules / People Match | Automations / Dispatch'r | Cannot be migrated. Must be rebuilt manually in Freshdesk. |
| Sidekick Guides | — | No Freshdesk equivalent. Must be recreated using Freshdesk's Freddy AI or bot builder. |
Gladly Freshdesk
| Gladly field | Freshdesk field | Notes |
|---|---|---|
| name | name | Direct map |
| emails [0].original | Use primary; additional emails via other_emails | |
| phones [0].original | phone | Use primary. Gladly stores in E.164 format; Freshdesk is more permissive but will reject invalid lengths. Extras to custom field. |
| address | address | Direct map |
| customAttributes.* | custom_fields.cf_* | Manual mapping per attribute |
| externalCustomerId | custom_fields.cf_gladly_id | Preserve for cross-reference |
Endpoint Returns
| Gladly field | Freshdesk field | Notes |
|---|---|---|
| GET /api/v1/customer-profiles | Customer search results | Sorted by updatedAt descending |
| GET /api/v1/customers/{id} | Single Customer | Returns merged-customer errors if applicable |
| GET /api/v1/customers/{id}/conversations | Conversation list | Gladly-Limited-Data header indicates if more exist |
| GET /api/v1/conversation-items/{conversationId} | Conversation Items | Use for item-level detail; export files are safer for full-fidelity backfills |
| GET /api/v1/customers/{id}/tasks | Task list | Gladly-Limited-Data header if more exist |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Contacts | low | Gladly Customer records map relatively cleanly to Freshdesk Contacts, though multiple emails and phones require careful primary/secondary designation. |
| Tickets | high | Gladly's continuous conversations must be sessionized into discrete Freshdesk tickets using custom logic, and historical created_at timestamps cannot be set via the standard API. |
| Conversation Items (Replies/Notes) | high | Email, chat, SMS, and voice items must be individually mapped to Freshdesk reply or note types with correct directionality, and multi-channel metadata may be lost or flattened. |
| Custom Fields | medium | All Gladly custom attributes must be pre-created in Freshdesk with exact type matching, and date format differences between ISO 8601 and Freshdesk's YYYY-MM-DD require transformation. |
| Agents | low | Agents can be mapped by email address, but Freshdesk agent seat limits must be verified before import to avoid license overages. |
| Topics / Tags | low | Gladly Topics map directly to Freshdesk Tags, requiring only a Topic ID to name resolution before applying them. |
| Companies | medium | Gladly's customer business/account data must be extracted and restructured into Freshdesk Company objects with domain-based association, which may not always align cleanly. |
| Knowledge Base Articles | medium | Gladly Answers must be restructured into Freshdesk's three-tier solution hierarchy with HTML cleanup and per-language variant creation for multi-language content. |
| Automations and Rules | high | Gladly Rules, People Match, and Sidekick Guides have no migration path and must be entirely rebuilt using Freshdesk's native automation and AI tools. |
| Attachments | medium | Voice call recordings and file attachments require separate handling and re-upload to Freshdesk, with potential size and format constraints on the receiving end. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Conversation Sessionization Into Tickets
Gladly's single continuous conversation per customer must be algorithmically segmented into discrete Freshdesk tickets using custom sessionization logic, as no 1:1 mapping exists between the two data models.
Historical Timestamp Preservation
Freshdesk's standard public API does not allow setting historical created_at timestamps on tickets, causing all migrated tickets to show the import date unless a workaround is arranged through Freshdesk support.
Multi-Channel Data Flattening
Gladly natively tracks email, chat, SMS, and voice in a unified timeline, but Freshdesk's source enum does not cover every Gladly channel, requiring channel metadata to be preserved in custom fields or tags.
Custom Attribute Schema Alignment
Gladly's key-value custom attributes must be pre-defined as custom fields with the cf_ prefix in Freshdesk before import, or the API will reject entire payloads with 400 errors.
Automation and Rules Rebuilding
Gladly Rules, People Match configurations, SLA policies, and Sidekick Guides cannot be migrated and must be manually recreated using Freshdesk's native Dispatch'r, automations, and Freddy AI tools.
Knowledge Base Hierarchy Restructuring
Gladly Answers must be reorganized into Freshdesk's three-tier Category → Folder → Article hierarchy, with multi-language content requiring per-language article creation and inline HTML cleanup.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
How long does a Gladly to Freshdesk migration take?
A typical Gladly to Freshdesk migration takes 2–3 weeks end-to-end, including discovery, sessionization design, scripting, testing, and cutover. The actual data transfer for 50,000 tickets on a Freshdesk Pro plan runs approximately 12–15 hours of continuous API calls.
What data cannot be migrated from Gladly to Freshdesk?
Gladly Rules, SLA policies, People Match configurations, Sidekick Guides, and Lookup Adaptor integrations cannot be migrated — they must be rebuilt manually. Voice call recordings stored in external telephony providers do not transfer; only text transcripts can be carried over. The continuous conversation structure requires sessionization into discrete tickets.
Can I use CSV imports for a Gladly to Freshdesk migration?
No. Freshdesk's native CSV import tool only supports Contacts and Companies. To migrate Gladly conversations, messages, notes, and attachments, you must use the Freshdesk REST API (v2).
Will Freshdesk automations fire on imported tickets?
Yes. Freshdesk automation rules run on ticket creation, including API-created tickets. Disable Dispatch'r, Supervisor, and Email Notifications before any migration load, and tag imported tickets with a migration identifier so exclusion rules can detect them.
How much does a Gladly to Freshdesk migration cost?
DIY costs are primarily engineering time: 100–300 hours depending on complexity, plus a Freshdesk paid plan ($15–$79/agent/month). A managed migration service typically ranges from $3,000–$15,000+ for 10,000–200,000 conversation items, depending on sessionization requirements and attachment handling.