Migrating from Pylon to Missive means translating a Slack-first ticketing model into a conversation-centric inbox. There is no native import — plan for a custom API pipeline with strict rate limits on both sides.
Migrating from Pylon to Missive requires translating a Slack-first, account-centric ticketing model into a conversation-centric collaborative inbox, with no native import path between the two platforms. Contacts can be moved via CSV import, but historical issue threads demand a custom API pipeline using Pylon's REST API and Missive's Posts or custom channel endpoints. Significant custom engineering is required to flatten Pylon's relational B2B data model—including Accounts, Issues, and Custom Objects—into Missive's simpler label-and-conversation architecture, with strict rate limits, a 400-message-per-conversation hard cap, and no equivalents for knowledge bases, SLA management, ticket forms, or custom objects.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
TL;DR — Pylon to Missive Migration
Migrating from Pylon to Missive means translating a Slack-first, account-centric ticketing model into a conversation-centric collaborative inbox. There is no native import path. Contacts can move via CSV, but historical issue threads require a custom API pipeline using Pylon's REST API and Missive's Posts or custom channel endpoints. Plan for strict rate limits on both sides, a 400-message-per-conversation hard limit in Missive, and the loss of custom objects, knowledge base, and SLA management. End-to-end timelines range from 1–2 days for contact-only migrations to 3–8 weeks for full historical migrations with attachments, depending on volume and complexity.
Key constraint
Missive does not have a dedicated ticket import API or bulk data import endpoint for historical conversations. You can import contacts via CSV or API, but conversation history must be injected via the Posts endpoint (which creates a notification-style card in a conversation, containing HTML body text, a title, and optional shared label assignments) or loaded via custom channels. This is the single biggest architectural constraint of this migration.
Data structure compromise
Missive is not a helpdesk — it's a collaborative inbox. Migrating from Pylon means accepting a simpler operational model. If you need structured ticketing, SLAs, custom objects, or account portal support, evaluate whether Missive is the right target before investing in migration.
Pylon Issues API constraint
The GET /issues endpoint requires a time range filter with a maximum window of 30 days. To extract all historical issues, you must iterate through 30-day windows. Factor this into your extraction script.
Missive conversation size limit
Missive enforces a 400-message hard limit per conversation. Very long Pylon issues must be split, truncated, or archived outside Missive. (missiveapp.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 Pylon
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 Missive can hold your support model
Walk your current workflow through Missive: 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 Pylon → Missive 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.
Pylon → Missive specifics
- Channel consolidation
- Pylon is optimized for Slack-first B2B support. When a team needs a unified inbox across email, SMS, WhatsApp, Instagram, and live chat as equal channels, Missive's multi-channel architecture is a better fit.
- Collaborative email workflows
- Missive's standout feature is real-time collaborative drafting — multiple team members can co-write a single email before sending. Pylon doesn't offer this.
- Simpler operational model
- Teams moving from complex B2B account-based support to a leaner shared inbox model — common for agencies, professional services firms, and small-to-mid-size operations — find Missive's label-and-assignment workflow faster to adopt.
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 Pylon 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 Pylon 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 Pylon 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 Pylon → Missive 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 Pylon → Missive 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 Missive 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 Missive sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Missive 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 Missive'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 Missive, 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 Pylon 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 Missive'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 Pylon 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 Pylon 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 Pylon and Missive 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 Missive, 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 Pylon 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.
Object Equivalent
Missive has no custom object support, so all Pylon custom object data must be migrated to an external system or lost entirely.
| Pylon field | Missive field | Notes |
|---|---|---|
| Issue | Conversation (via Posts / custom channels) | Pylon Issues have lifecycle states (open, snoozed, closed). Missive Conversations have inbox states (inbox, archived, closed, trashed). No 1:1 parity. |
| Account | Organization (Contact Book) | Pylon Accounts are first-class objects with domains, owners, custom fields, and channels. Missive Organizations are lightweight groupings within contact books. |
| Contact | Contact | Closest 1:1 mapping. Missive contacts live in Contact Books and support name, email, phone, company, groups, notes, and custom fields. |
| Messages (replies + notes) | Posts / Comments | Customer replies → Posts in a conversation. Internal notes → Comments (Missive's internal annotation layer). |
| Tags | Shared Labels | Pylon Tags are flat key-value pairs on Issues. Missive Shared Labels can be applied to conversations and trigger rules. |
| Custom Fields | Contact Notes / Custom Fields | Pylon supports custom fields on Accounts, Issues, and Contacts. Missive custom fields exist on contacts but are not available on conversations natively. |
| Custom Objects | No equivalent | Missive has no custom object support. Data must be archived externally or flattened into contact notes. |
| Knowledge Base | No native KB | Missive is an inbox, not a helpdesk. KB articles must be migrated to Notion, GitBook, Confluence, or similar. |
| Tasks & Projects | Tasks (comment-based) | Missive tasks are lightweight — comments with a checkbox. Pylon's tasks have assignees, due dates, and account associations. |
| Macros | Canned Responses | Must be recreated manually. No import mechanism. |
| Ticket Forms | No equivalent | Missive does not have structured intake forms. |
| Opportunities | No equivalent | Missive has no native CRM pipeline. Keep these in your CRM and surface via integrations. (missiveapp.com) |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Contacts | low | Contacts have the closest 1:1 mapping between platforms and can be migrated via CSV import or API with minimal transformation. |
| Accounts / Organizations | medium | Pylon Accounts are rich first-class objects with domains, owners, and custom fields, but Missive Organizations are lightweight groupings, requiring significant field flattening. |
| Issues / Conversations | high | Historical issue threads must be injected via the Posts endpoint or custom channels as read-only content, with no native ticket import and a 400-message hard limit per conversation. |
| Attachments | high | Attachments embedded in Pylon issue messages require individual download, re-upload, and association with the correct Missive conversation, adding significant complexity and time. |
| Custom Fields | medium | Pylon supports custom fields on Accounts, Issues, and Contacts, but Missive only supports custom fields on contacts—issue and account-level fields must be archived or flattened. |
| Custom Objects | high | Missive has no custom object support, so all Pylon custom object data must be migrated to an external system or lost entirely. |
| Tags / Shared Labels | low | Pylon Tags map reasonably well to Missive Shared Labels, though labels must be pre-created in Missive and applied programmatically during migration. |
| Knowledge Base Articles | high | Missive has no native knowledge base, so all KB content must be migrated to an external platform such as Notion, GitBook, or Confluence. |
| Macros / Canned Responses | medium | Missive supports canned responses but offers no import mechanism, requiring each macro to be manually recreated in the target system. |
| SLA Policies | high | Missive has no native SLA management, meaning all Pylon SLA configurations and historical compliance data will be lost unless tracked externally. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
No Native Import Path
Missive has no dedicated ticket import API or bulk historical conversation import endpoint, forcing all conversation data through the Posts endpoint or custom channels.
Data Model Flattening Required
Pylon's rich relational model of Accounts, Issues, Contacts, and Custom Objects must be flattened into Missive's simpler Conversations, Organizations, and Contact Books structure.
Conversation Message Hard Limit
Missive enforces a 400-message-per-conversation hard limit, requiring long Pylon issue threads to be split or truncated during migration.
Strict API Rate Limits
Both Pylon and Missive impose rate limits that constrain throughput and require careful batching, retry logic, and throttling in any custom ETL pipeline.
Custom Objects Have No Equivalent
Missive does not support custom objects, so Pylon's custom object data must be archived externally or flattened into contact notes, resulting in structural data loss.
Knowledge Base and SLA Loss
Missive lacks native knowledge base and SLA management features, requiring KB articles to be migrated to external tools and SLA workflows to be rebuilt or abandoned.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Is there a native Pylon to Missive importer?
No. Missive documents CSV import for contacts and a support-assisted migration path from Front, but there is no documented native Pylon import flow. Contacts can move via CSV, but historical conversations require extraction from Pylon's REST API and injection into Missive via the Posts endpoint or custom channels.
What are the API rate limits for Pylon and Missive during migration?
Pylon's Issues endpoint is limited to 10 requests per minute (the main bottleneck), while Accounts and Contacts allow 60 req/min. Missive allows 300 requests per minute with a max of 5 concurrent requests and a 900-request cap per 15 minutes. Plan for multi-day extraction windows for large datasets.
Does Missive support custom objects like Pylon?
No. Missive has no custom object support. Data stored in Pylon Custom Objects must be either archived externally (to Airtable, Google Sheets, or your CRM), flattened into contact notes, or accepted as data that will not transfer.
Can I preserve original timestamps when migrating to Missive?
Yes, partially. Missive's custom channel message endpoint accepts a delivered_at field, and the Posts endpoint accepts notification.date. However, these may not control conversation sort order in the inbox the same way native email timestamps do. Test this behavior in a proof of concept.
What is the biggest hard limit to plan around in Missive?
Conversation size. Missive enforces a 400-message hard limit per conversation. Long Pylon issues must be split across multiple Missive conversations, truncated, or archived outside Missive.