Migrating Gladly to Unthread requires splitting lifelong conversation timelines into discrete Slack tickets while handling Gladly's 10 req/sec API limit and .jsonl export format.
There is no native migration path from Gladly to Unthread. Gladly uses a person-centric model where all interactions across channels are threaded into a single lifelong conversation per customer, while Unthread tracks discrete Slack-based tickets with defined lifecycle states, assignees, and SLA clocks. Migration requires custom ETL work to extract Gladly data via its Export API, split continuous customer timelines into individual tickets using deterministic heuristics, remap customer and agent identities to Slack users or Unthread Customer objects, and load the transformed data through Unthread's REST API—all while respecting strict rate limits, attachment constraints, and data truncation thresholds on both platforms.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Do not map one Gladly customer profile to one Unthread ticket
A customer with three years of history will generate a single, massive ticket containing hundreds of unrelated messages. This breaks SLA tracking, overwhelms the Slack interface, and makes the data operationally useless. (help.gladly.com)
Gladly's Export API returns .jsonl format (one JSON object per line)
Export files are available for 14 days after generation. One-time exports covering periods greater than six months may require coordination with Gladly Support and could incur a service fee. (developer.gladly.com)
Do not load a multi-gigabyte Gladly .jsonl export into memory at once
Use a streaming parser or stage the data in an intermediary database (PostgreSQL, for example) before running transformation logic. This avoids out-of-memory exceptions and gives you a queryable layer for debugging.
Do not write a single transformation function that accesses content.content uniformly
Do not write a single transformation function that accesses content.content uniformly across all item types. Branch on type (or initiator.channel) before extracting message text. Voice and SMS records in particular will break a naive unified parser.
The content ["content"] access pattern in these examples is only valid for chat-type items
Email items use content ["body"] (HTML); voice items carry call metadata rather than a message body. Branch on item type before accessing message content — a uniform accessor will raise key errors or silently produce empty messages on non-chat records.
The safest rollback path is not needing it
Two full test migrations against a staging Unthread workspace, with automated validation after each run, catch the structural problems that cause post-cutover failures. Do not skip the test migration phase to save time.
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 Unthread can hold your support model
Walk your current workflow through Unthread: 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 → Unthread 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 → Unthread specifics
- No 1:1 mapping exists
- You must define splitting logic — by topic change, time gap, channel switch, or a combination.
- Customer identity must be resolved
- Gladly merges profiles across channels into a single record. Unthread ties conversations to Slack users or customer records. If your customers are not Slack users (common when moving from B2C on Gladly to B2B on Unthread), you need to represent historical identities as external user profiles or Unthread Customer objects.
- Attachments are channel-bound
- Gladly stores attachments across email, chat, and social. Unthread is constrained by Slack's limits: 10 files per message, 20MB maximum per file.
- The conversation timeline API caps at 1,000 items per conversation
- If a customer has a longer history, the Gladly-Limited-Data response header will be true, but the API will not paginate further. For high-volume customers, the Export API is the only path to complete data.
- Splitting logic iteration
- The first version of your conversation-splitting heuristic will produce bad tickets — too granular, too broad, or inconsistent across channels. Expect multiple rounds of review with support ops before the logic is production-ready.
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 -
Export jobs are date-range scoped
You specify a start and end time, and Gladly generates files covering all communications delivered in that window.
Gladly → Unthread specifics
- Output format is .jsonl
- one JSON object per line. File types include customers, conversation_items, agents, and topics.
- Files expire after 14 days
- Download them immediately or automate the pull on job completion.
- Conversation items with invalid attributes may be silently excluded
- undelivered emails or auto-replies generated by Gladly rules can be absent from exports.
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 → Unthread 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 → Unthread 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 Unthread 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 Unthread sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Unthread 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 Unthread'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 Unthread, 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 Unthread'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 Unthread 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 Unthread, 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.
Gladly → Unthread specifics
- Routing rules
- Gladly uses inbox-based routing with skills matching. Unthread routes via Slack channels, assignments, and automation rules. These must be reconfigured from scratch.
- SLA policies
- Gladly SLAs are tied to conversations. Unthread SLAs are tied to tickets with distinct response and resolution targets. Rebuild these in Unthread's dashboard.
- Knowledge base
- If you were using Gladly's Answers, migrate that content separately. Unthread can sync documentation from web-based sources or native integrations.
- Automations and triggers
- Unthread supports custom TypeScript automations, webhook triggers, and Zapier/Make integrations. Rebuild your Gladly rules as Unthread automations.
- Agent training
- Your team is moving from a dedicated agent desktop to a Slack-native interface. The workflow muscle memory is completely different. Plan at least one week of parallel operation before cutting over.
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.
Source Target
| Gladly field | Unthread field | Notes |
|---|---|---|
| Customer Profile (id, name, emails, phones) | Account + Customer | Map externalCustomerId to account.externalCrmMetadata.id. Gladly allows multiple emails/phones; choose a primary identifier. In B2B Slack Connect setups, map to an Account with associated email domains and Slack channels. |
| Conversation (lifelong timeline) | Multiple Conversations (Tickets) | Split using time-gap, topic, or channel heuristics. One Gladly conversation → N Unthread tickets. |
| Conversation Item (content, timestamp, initiator) | Message / Reply | Convert to Slack-compatible markdown. Preserve chronological order. Use onBehalfOf for authorship attribution. |
| Agent (id, name, email) | User (Workspace Member) | Match by email to a Slack workspace member. Provision agents in Unthread before migration. Fall back to a placeholder assignee if no match exists. |
| Topic / Tag | Tag | Direct mapping. Flatten Gladly's hierarchical topics. Preserve original topic IDs in metadata for analytics continuity. |
| Conversation Status | Status (open, in_progress, on_hold, closed) | Map through a deterministic status table. Preserve original Gladly status in metadata. |
| Attachment (binary + metadata) | Attachment (multipart upload) | Download from Gladly, re-upload to Unthread. Slack enforces 10 files per message, 20MB per file. |
| Voicemail / Phone Transcript | Note or archived text | Unthread has no native voice channel. Convert to text-based notes or omit from scope. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Conversation History | high | Gladly's lifelong conversation timelines must be split into discrete tickets using custom heuristics, and the timeline API truncates at 1,000 items per conversation without further pagination. |
| Customer Profiles | high | Gladly's merged multi-channel customer profiles have no direct equivalent in Unthread's Slack-centric model, especially for B2C customers who are not Slack users. |
| Attachments | medium | Attachments from email, chat, and social channels must be filtered and restructured to comply with Slack's 10-file-per-message and 20MB-per-file limits. |
| Agent Assignments | medium | Gladly agent identities must be remapped to Slack user accounts in Unthread, and historical assignment changes within a single Gladly conversation must be correctly distributed across split tickets. |
| Topics and Tags | medium | Gladly topics serve as both categorization and splitting signals, and must be mapped to Unthread ticket types or tags with potential many-to-one or one-to-many relationships. |
| SLA and Resolution Metadata | high | Gladly does not use discrete ticket statuses or SLA clocks in the same way as Unthread, so resolution states and timing data must be inferred and reconstructed from conversation timestamps and splitting logic. |
| Timestamps and Chronology | medium | Preserving correct message ordering across split tickets is critical, and webhook retry behavior on both platforms can introduce ordering drift during migration. |
| Channel Metadata | low | Gladly tracks the originating channel (email, chat, voice, SMS, social) per interaction, but Unthread is Slack-native so channel provenance becomes informational metadata rather than a functional attribute. |
| Custom Fields | medium | Any custom attributes on Gladly customer profiles or conversations require manual mapping to Unthread's data model, with no automated schema translation available. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Lifelong Conversation Timeline Splitting
Gladly stores all customer interactions as a single continuous timeline per person, which must be segmented into many discrete Unthread tickets using deterministic splitting rules based on time gaps, topic changes, or channel switches.
Customer Identity Resolution
Gladly merges customer profiles across all channels into one record, but Unthread ties conversations to Slack users, requiring historical identities to be remapped to Slack accounts or represented as external user profiles and Unthread Customer objects.
API Rate Limits and Truncation
Gladly enforces a strict 10-requests-per-second rate limit across all HTTP methods and caps conversation timeline responses at 1,000 items, meaning high-volume customers may have truncated data unless the bulk Export API is used.
Attachment Format and Size Constraints
Gladly stores attachments across email, chat, voice, and social channels, but Unthread is constrained by Slack's limits of 10 files per message and 20MB per file, requiring filtering and restructuring during migration.
Export File Expiration and Format
Gladly's Export API generates .jsonl files that expire after 14 days and may silently exclude conversation items with invalid attributes, requiring immediate automated downloads and careful data completeness validation.
No Native Integration or Migration Path
iPaaS tools like Zapier and Make can handle basic field mapping for forward sync but cannot perform the structural data model transformation required to split Gladly timelines into discrete Unthread tickets.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can you directly export data from Gladly to Unthread?
No. There is no native export-to-Unthread path. You must extract data from Gladly using its Export API (.jsonl files) or REST API, transform the data to split lifelong conversations into discrete tickets, and then load into Unthread via its REST API.
What is Gladly's API rate limit?
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 the Ratelimit-Remaining-Second response header to throttle proactively. For bulk extraction, use the Export API instead of per-record REST calls.
How do you map Gladly conversations to Unthread tickets?
Gladly uses a single lifelong conversation per customer. You must apply a splitting heuristic — typically based on time gaps between messages, topic changes, or channel switches — to convert one Gladly conversation into multiple discrete Unthread tickets. Expect several iterations before the splitting logic is production-ready.
What format does the Gladly Export API use?
Gladly's Export API returns .jsonl (JSON Lines) files where each line is a self-contained JSON object. File types include customers, conversation_items, agents, and topics. Files are available for download for 14 days after generation.
How long does a Gladly to Unthread migration take?
A DIY API-based migration typically takes 4–6 weeks of engineering time including script development, splitting logic iteration, and validation. A managed migration service like ClonePartner can complete the same migration in days using pre-built extraction and transformation pipelines.