Helpshift Issues become Plain Threads via REST extraction and GraphQL import mutations. No native path — plan for status flattening, cursor pagination, HTML sanitization, and attachment re-hosting.
Migrating from Helpshift to Plain requires a full custom ETL pipeline — there is no native import path and Helpshift is not among Plain's documented built-in importers (Zendesk, Freshdesk, Intercom, Front, Help Scout). The two platforms differ fundamentally in architecture: Helpshift is a mobile-first, REST-based platform built around Issues, End User Profiles, and Apps, while Plain is a B2B GraphQL-native platform organized around Threads, Customers, Companies, and Tenants. Data must be extracted via Helpshift's REST API with cursor pagination, structurally transformed (including a 7-state to 3-state status collapse, CIF-to-Thread Field schema mapping, and app-to-tenant/label resolution), and loaded through Plain's importThread and importThreadMessages GraphQL mutations. Additional custom work is required for attachment re-hosting, HTML sanitization, bot transcript aggregation, anonymous profile handling, and rate limit management on both sides.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
If your business still depends on Helpshift's gaming-native support delivery — in-game
If your business still depends on Helpshift's gaming-native support delivery — in-game SDK UI, cross-platform player identity, console-to-mobile handoff, or proactive engagement tied to player lifecycle events — this is not a like-for-like swap. Evaluate that architectural trade-off before any ETL work starts. (helpshift.com)
If your team relies on distinguishing between new, new-for-agent, and
If your team relies on distinguishing between new, new-for-agent, and pending-reassignment, you lose that granularity in Plain. Store the original Helpshift state as a Thread Field or label for audit purposes.
If email is unavailable, you will have orphaned profiles
Decide early whether to import these as anonymous customers or skip them — do not wait until the load stage to make that policy.
Plain Thread Fields must be configured in Settings → Thread fields before import
If a Thread Field is marked required, Plain enforces it before a thread can be marked done. For migrations, either disable required-field enforcement during import or ensure every closed thread has that field populated in your source data. Historical DONE imports will fail for threads where required CIF data is absent — this is one of the most common causes of partial import failure. (plain.mintlify.app)
Sort from oldest to newest (sort-order=asc) when paginating
New issues added during extraction only affect the last pages, minimizing duplicate risk.
Imported threads do not trigger SLAs or autoresponders and are marked with import
Imported threads do not trigger SLAs or autoresponders and are marked with import provenance tracking. No false alerts, no auto-replies to customers during migration. (plain.mintlify.app)
Attachments uploaded but not referenced by any message are deleted after 24 hours
Run attachment uploads and message imports in tight sequence. Helpshift attachment URLs are signed and time-limited — copying the URL string into a Plain message without re-hosting will break within days. For older issues (12–18+ months), treat expired Helpshift attachment URLs as an expected condition, not an error.
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 Helpshift
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 Plain can hold your support model
Walk your current workflow through Plain: 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 Helpshift → Plain 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.
Helpshift → Plain specifics
- importThread
- Creates the thread with metadata (title, status, priority, labels, thread fields) and the original creation timestamp.
- importThreadMessages
- Adds conversation history (inbound, outbound, and note messages) in batches of up to 25 messages per call.
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 Helpshift 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 Helpshift 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 Helpshift 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
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 Helpshift → Plain 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 Helpshift → Plain 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 Plain 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 Plain sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Plain 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 Plain'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 Plain, 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 Helpshift 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 Plain'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 Helpshift 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 Helpshift 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 Helpshift and Plain 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 Plain, 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 Helpshift 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.
Cus m Issue Fields
Customers can be upserted using the Helpshift profile ID as externalId, but app-scoped duplicate profiles and the high rate of missing email addresses in consumer verticals require deduplication and anonymous-profile policy decisions before extraction.
| Helpshift field | Plain field | Notes |
|---|---|---|
| Single-line text | STRING | Direct mapping |
| Multiline text | STRING | No multiline-specific type in Plain |
| Number | NUMBER | Direct mapping |
| Date | DATETIME | Serialize as ISO 8601 |
| Checkbox | BOOL | Direct mapping |
| Dropdown | ENUM | Pre-create enum values in the Thread Field schema |
Dataset Size Extraction
| Helpshift field | Plain field | Notes |
|---|---|---|
| <5K issues | 1–2 hours | 4–8 hours |
| 5K–25K issues | 2–6 hours | 1–2 days |
| 25K–100K issues | 6–24 hours | 2–4 days |
| 100K+ issues | 1–3 days | 3–5 days |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Issues (Threads) | medium | Issues map 1:1 to Plain Threads via the idempotent importThread mutation using externalId, but status mapping requires deliberate policy decisions and original state granularity is permanently lost. |
| End User Profiles (Customers) | medium | Customers can be upserted using the Helpshift profile ID as externalId, but app-scoped duplicate profiles and the high rate of missing email addresses in consumer verticals require deduplication and anonymous-profile policy decisions before extraction. |
| Messages | medium | Inbound and outbound messages map to Plain's INBOUND, OUTBOUND, and NOTE timeline types, but bot transcripts must be aggregated and HTML content must be sanitized before load. |
| Attachments | high | Helpshift attachment URLs are not persistent external assets; files must be downloaded, re-hosted on accessible storage, and new URLs substituted before importing threads, making this one of the most operationally complex steps. |
| Custom Issue Fields (Thread Fields) | high | CIF types map to Plain Thread Field types but all enum dropdown values must be pre-created in Plain's schema, and required Thread Fields will cause import failures for any DONE thread missing that data in the source. |
| Tags (Labels) | low | Tags have a clean 1:1 mapping to Plain Labels and can be pre-created before thread import with labels referenced by key during the importThread call. |
| Apps (Tenants or Labels) | medium | Helpshift Apps have no direct Plain equivalent and must be mapped to either Tenants or Labels depending on whether they represent organizational boundaries or channel distinctions, with tenant membership required before any thread referencing that tenantId can be imported. |
| Device Metadata and Custom Data | high | SDK-collected device metadata and developer-defined custom key-value pairs have no native Plain target, requiring a deliberate strategy of serializing into notes, mapping critical fields to Thread Fields, or externalizing via Customer Cards. |
| Issue State Granularity | medium | The collapse from 7 Helpshift states to 3 Plain states is irreversible in the target system, so original Helpshift state values should be preserved as a Thread Field or label during migration for audit and reporting purposes. |
| Bot Transcripts | medium | Helpshift's native QuickSearch and Custom Bot interactions are stored as structured bot messages that Plain has no equivalent model for, requiring aggregation and reformatting into NOTE or INBOUND message entries before import. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
No Native Import Path
Helpshift is not supported by Plain's built-in migration importers, requiring a fully custom extraction, transformation, and load pipeline built against both platforms' APIs.
Cursor Pagination Volume Ceiling
Helpshift's REST API enforces a 50,000-row cursor pagination ceiling, requiring careful pagination state management and checkpoint recovery logic to extract large datasets without data loss.
Status Model Collapse
Helpshift's 7 distinct issue states must be collapsed into Plain's 3-state model (TODO, SNOOZED, DONE) with statusDetail sub-types, which is a policy decision that permanently loses original state granularity.
Thread Field Schema Pre-configuration
All Plain Thread Fields, including enum options derived from Helpshift dropdown CIFs, must be created in Plain's schema before import begins, and any required Thread Fields will cause DONE thread imports to fail if source data is missing.
Anonymous End User Profiles
A significant share of Helpshift End User records in consumer and gaming apps lack email addresses, requiring an early policy decision on whether to import these as anonymous customers or exclude them entirely.
Device Metadata Has No Target Model
Helpshift's SDK-collected device metadata (OS, device model, battery, carrier, app version) and custom key-value pairs have no native equivalent in Plain, requiring serialization into notes, selective Thread Field mapping, or externalization via Customer Cards.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Does Plain have a native Helpshift importer?
Not as of this writing. Plain documents built-in importers for Zendesk, Freshdesk, Intercom, Front, and Help Scout. Helpshift requires custom API extraction via its REST API and loading through Plain's importThread and importThreadMessages GraphQL mutations.
How long does a Helpshift to Plain migration take?
For a mid-size dataset (5K–25K issues), expect 2–3 weeks elapsed time including script development, extraction, transformation, loading, and validation. Datasets over 100K issues can take 5–8 weeks.
What Helpshift data cannot be migrated to Plain?
Plain lacks native equivalents for Helpshift's device metadata (battery level, carrier, OS details), in-app FAQ system, CSAT rating fields, and 7-state issue lifecycle. Metadata can be serialized into notes, FAQs must move to an external system, and issue states collapse from 7 to 3 with sub-types.
Does Helpshift have an API export limit?
Yes. Helpshift's REST API enforces a page × page-size ceiling of 50,000. To export more than 50K issues, you must use cursor-based pagination with created_since timestamps as a sliding window rather than standard page-number pagination.
Does Plain's import API trigger automations on imported threads?
No. Plain's importThread and importThreadMessages mutations do not trigger SLAs or autoresponders. Imported threads are marked with import provenance tracking, so there is no risk of auto-replies reaching customers during migration.