Migrating Trengo to Helpshift requires restructuring channel-based tickets into app-scoped Issues, mapping custom fields from contact-level to issue-level, and handling Trengo's 120 RPM rate limit during export.
Migrating from Trengo to Helpshift requires a fundamental restructuring of your data model, not a simple database copy. No native migration path or official tooling exists between the two platforms; the entire migration must be executed via Trengo REST API v2 for export and Helpshift REST API v1 for import. The core architectural incompatibility is that Trengo is channel-centric — organizing tickets around email, WhatsApp, social, and voice inboxes — while Helpshift is app-centric, requiring every Issue to be scoped to a specific App ID. Custom transformation logic must be written to map Trengo channels and contacts to Helpshift Apps and Users, and significant data entities including VoIP call logs, Contact Groups, Canned Responses, and historical analytics have no migration path and must be archived or rebuilt manually.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
If a significant portion of your Trengo volume comes from email or social channels, think
If a significant portion of your Trengo volume comes from email or social channels, think carefully about whether Helpshift is the right target. It excels at in-app support but is not designed as a general-purpose omnichannel helpdesk like Zendesk or Help Scout.
The integrations feature must be enabled on your Helpshift account to access the REST API
This is not enabled by default. If you receive 403 Forbidden on your first API call, contact Helpshift support to enable it before starting any work. Factor this lead time into your project plan.
Do not attempt to create Issues before your Custom Issue Fields and Tags exist in the Dashboard
Helpshift silently ignores Custom Issue Fields that don't match a defined field key — no error is returned, the field is simply absent from the record. This is the most common source of undetected data loss in this migration.
User Hub Bulk API availability may be gated by Helpshift plan tier
Confirm with your account manager before designing your user import pipeline — some tiers require individual user creation via a separate endpoint at significantly lower throughput.
Silent CIF data loss
If a CIF key in your Create Issue payload does not exactly match a field key defined in the Dashboard, Helpshift returns a 200 OK response but does not create the field. There is no error, no warning, and no indication in the response body that the field was dropped. Audit CIF key names character by character — trailing spaces and capitalization differences are the most common cause. Run a test import of 10 Issues and retrieve them via GET to verify CIF values are present before running the full migration.
Create a Custom Issue Field called source_channel with a dropdown type listing your
Create a Custom Issue Field called source_channel with a dropdown type listing your Trengo channels (email, whatsapp, instagram, facebook, voice, sms, live_chat). Apply it to every migrated Issue. Create a second CIF called original_contact_identifier as a singleline field to store the email address, phone number, or social handle from the original channel. These two fields give agents immediate context about the original interaction without accessing Trengo.
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 Trengo
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 Helpshift can hold your support model
Walk your current workflow through Helpshift: 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 Trengo → Helpshift 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.
Trengo → Helpshift specifics
- SDK-level integration
- Helpshift embeds directly into iOS, Android, Unity, React Native, and Unreal Engine. Trengo has no equivalent in-app SDK. API-created Issues (the migration path) do not receive automatic SDK device context — they arrive as bare Issues without attached device metadata unless you pass that data explicitly in the payload.
- AI automation depth
- Helpshift's bot framework and Guided Issue Filing offer structured in-app automation flows. These are not migrated; they must be rebuilt from scratch in Helpshift after migration.
- Gaming and high-volume mobile support
- Helpshift is the dominant platform in mobile gaming support, where ticket volumes can be massive and in-app device context is non-negotiable for effective triage.
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 Trengo 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 Trengo 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 Trengo 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
Trengo → Helpshift specifics
- User profiles
- Confirm end-user data imported correctly via User Hub with correct identifiers.
- Tag name mismatch
- Tags must match exactly (case-sensitive). "Billing" and "billing" are different tags. If the tag doesn't exist in the Dashboard, it's silently dropped — no error.
- Tag character limit exceeded
- Helpshift enforces a character limit on tag names. Trengo labels exceeding this limit return 400 Bad Request. Add truncation logic in your transformation layer and log all truncations for manual review.
- CIF type mismatch
- If your Trengo data contains values that don't conform to the target CIF type (e.g., free-text string mapped to a date CIF), Helpshift drops the field silently. Audit Trengo data for type consistency before extraction and add type validation in your transform layer.
- Message ordering
- Messages not sorted by created_at before import will produce unreadable conversation threads in Helpshift. Sort ascending at the transform stage, not at import time.
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 Trengo → Helpshift 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 Trengo → Helpshift 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 Helpshift 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.
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 Helpshift sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Helpshift 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 Helpshift'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 Helpshift, 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 Trengo 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 Helpshift'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 Trengo 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 Trengo read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Trengo → Helpshift specifics
- Historical Sync
- Migrate all closed tickets and historical data up to a cutoff date (e.g., one week before planned cutover). This moves the bulk of your data volume without impacting live operations in Trengo.
- Delta Sync
- Run a daily incremental script that migrates only tickets updated since the last sync, using Trengo's updated_since filter parameter.
- The Freeze
- Choose a low-traffic window (e.g., Saturday 2 AM in your primary timezone). Stop routing new inquiries to Trengo. Redirect support email forwarding and SDK endpoints.
- Final Catch-up
- Run the migration script one final time with a narrow updated_since window covering the last 24–48 hours, catching any tickets created or updated since the last delta sync.
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 Trengo and Helpshift 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 Helpshift, 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 Trengo 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.
Trengo → Helpshift specifics
- Issue count parity
- Total Issues in Helpshift matches total Tickets exported from Trengo, minus any intentionally excluded records (spam, test tickets). Document exclusions.
- Message count per Issue
- Spot-check a random 5% sample. Verify thread completeness and chronological order.
- Custom Issue Field integrity
- Retrieve 5% of Issues via GET and compare CIF values against source data. This check is essential given silent CIF drop behavior.
- FAQ accuracy
- Confirm all articles rendered correctly, including HTML formatting, images, and links. Test rendering in the Helpshift mobile SDK, not just the browser admin view.
- Multi-locale FAQs
- If applicable, verify each language variant is present and correctly attributed to the right locale.
Don't move on until
- Full reconciliation report attached to the project record
- Reporting baselines match pre-migration figures within agreed tolerance
- Formal acceptance signed and archive retention scheduled
Field mapping reference
The field-by-field mapping for each object. Use this as the starting point for your mapping spec.
Object Equivalent
| Trengo field | Helpshift field | Notes |
|---|---|---|
| Contacts / Profiles | Users (End Users) | Helpshift users are app-scoped; one user can have multiple app profiles |
| Contact Custom Fields | Custom Issue Fields | Helpshift CIFs are per-Issue, not per-User — fundamentally different model |
| Contact Groups | N/A | No equivalent in Helpshift |
| Tickets | Issues | Each Issue is scoped to a specific App — decide your App mapping first |
| Ticket Messages | Messages on Issues | Import in strict chronological order; supports attachments |
| Internal Notes | Private Notes | Maps directly |
| Labels | Tags | Tags must exist in Dashboard before applying via API; names are case-sensitive |
| Help Center Articles | FAQs | Helpshift FAQs are grouped into Sections, not categories |
| Help Center Categories | FAQ Sections | Sections are scoped to an App; multi-locale FAQ content requires separate handling |
| Quick Replies | N/A | Recreate as Canned Responses in the Helpshift Dashboard — no import API |
| Users (Agents) | Agents | Provision in Helpshift Dashboard; agent IDs needed for Issue assignment |
| Teams | Agent Groups | Create in Dashboard before migration |
| Custom Field Definitions | Custom Issue Field Definitions | Create in Settings → Custom Issue Fields before importing data |
| Webhooks | Webhooks | Event models differ; requires rebuild and testing, not just re-pointing |
| Reporting / Analytics | N/A | Helpshift has its own analytics; historical Trengo data won't import |
| VoIP Call Logs | N/A | Helpshift has no voice support |
| WhatsApp / Social Messages | Issues (lossy) | Channel-specific context (WhatsApp number, social handle) stored as metadata only |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets / Issues | medium | Trengo tickets can be created as Helpshift Issues via API, but each Issue must be explicitly mapped to an App ID and will lose all channel-specific context such as email headers, social handles, or voice metadata unless that data is preserved manually in Custom Issue Fields. |
| Ticket Messages | medium | Messages can be migrated via the Add Message API endpoint, but they must be imported in strict chronological order and any failure in sequencing will produce an inaccurate conversation history that cannot be easily corrected post-import. |
| Internal Notes | low | Trengo internal notes map directly to Helpshift Private Notes via API with no structural differences, making this one of the lowest-risk entities in the migration. |
| Contacts / Users | medium | End-user records can be imported via the User Hub Bulk APIs in batches of up to 10,000, but Helpshift users are app-scoped rather than globally scoped, meaning a single Trengo contact may need to be represented as multiple app-specific user profiles in Helpshift. |
| Custom Fields | high | Trengo contact-level custom fields must be remapped to Helpshift's Issue-level Custom Issue Fields, a fundamentally different data model, and any CIF value imported before its field definition is created in the Dashboard will be silently discarded with no error returned. |
| Labels / Tags | medium | Tags must be pre-created in the Helpshift Dashboard before being applied via API, are case-sensitive, and have a character limit that will cause a 400 Bad Request error if exceeded, requiring thorough pre-migration validation of all tag values. |
| Help Center Articles / FAQs | medium | FAQ content can be migrated via API but must be restructured from Trengo's category model into Helpshift's Section-based model scoped per App, and multi-locale content requires separate handling that adds significant transformation complexity. |
| Contact Groups | high | Contact Groups have no equivalent entity in Helpshift's data model and cannot be migrated via any API method; they must be archived to CSV before cutover with Tags used as a partial functional substitute. |
| VoIP Call Logs and Recordings | high | Helpshift has no voice support whatsoever, making VoIP call logs and recordings completely non-migratable; they must be archived to external storage before cutover or the data will be permanently inaccessible after decommissioning Trengo. |
| Reporting and Analytics | high | Historical Trengo reporting data is incompatible with Helpshift's analytics model and cannot be imported; all historical metrics must be exported to CSV before cutover as they will not be available in any form within Helpshift. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Channel-to-App Model Translation
Every Helpshift Issue must be assigned to a specific App via app_publish_id, but Trengo tickets originate from channels (email, WhatsApp, social, VoIP) that have no direct App equivalent, requiring custom mapping logic to be defined before any import can run.
Silent Custom Field Data Loss
Helpshift silently drops Custom Issue Field values on import if the corresponding field definition does not already exist in the Dashboard, returning no error, which makes undetected data loss the most likely failure mode for this entity.
Strict Import Dependency Ordering
Helpshift's data model requires Apps, Agents, Custom Issue Field Definitions, and Tags to be fully provisioned before Issues can be created, and Messages must be added to Issues in strict chronological order, making dependency sequencing a hard technical requirement.
API Access Requires Manual Enablement
The Helpshift REST API is gated behind an integrations feature that is disabled by default, requiring a support request to Helpshift before any programmatic import can begin, introducing potential project timeline risk.
Trengo Rate Limit Export Throughput
Trengo enforces a 120-requests-per-minute rate limit on its REST API v2, requiring exponential backoff logic in the export pipeline and making large-volume exports a time-constrained operation that must be factored into the migration schedule.
Automation and Workflow Rebuild Required
Helpshift's automation engine uses a fundamentally different event-driven model compared to Trengo's workflow logic, meaning all routing rules, Smart Views, and bot flows cannot be converted or exported and must be rebuilt from scratch in the Helpshift Dashboard.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate Trengo tickets to Helpshift via API?
Yes. Use Helpshift's Create Issue REST API to import Trengo tickets as Issues. Each Issue must be scoped to a Helpshift App via its app_publish_id. Messages and private notes are added via separate API calls after the Issue is created. Tags and Custom Issue Fields must be pre-created in the Helpshift Dashboard.
What data is lost when migrating from Trengo to Helpshift?
You'll lose channel-specific context (email headers, WhatsApp metadata, social handles), VoIP call logs and recordings, Contact Groups, reporting history, and email threading. Helpshift is mobile-first and has no voice or email channel support. Archive this data separately before migration.
How do Trengo custom fields map to Helpshift?
Trengo has contact-level custom fields (per customer). Helpshift has Custom Issue Fields (per Issue). This is a fundamental model difference. You'll need to decide whether to duplicate customer-level fields onto every Issue, store them only on user profiles via User Hub, or use a hybrid approach.
How do I handle attachments during a Trengo to Helpshift migration?
You cannot pass Trengo attachment URLs directly to Helpshift — they often expire or require authentication. Your migration script must download the file from Trengo, upload it to Helpshift via a multipart/form-data POST request, and attach the new reference to the Issue message.
What is Trengo's API rate limit for data export?
Trengo enforces a rate limit of 120 requests per minute. Exceeding it returns HTTP 429 with a Retry-After header. For large accounts with 50,000+ tickets, a full export can take 24-48 hours. Build backoff logic using the Retry-After header value.