eDesk to Pylon migration requires a custom API pipeline. Pylon's 10 req/min Issues API is your primary bottleneck, and eDesk's order data has no native Pylon equivalent.
There is no native migration path from eDesk to Pylon. eDesk is an eCommerce-first helpdesk organized around marketplace orders and seller workflows, while Pylon is a B2B, Slack-first support platform built around account-level relationships and conversational issues — the core data models are fundamentally incompatible. Migration requires a custom API-based ETL pipeline or a managed migration service, as eDesk's CSV exports are capped at 1,000 rows and exclude conversation history, and Pylon's Issues API is rate-limited to 10 requests per minute, making brute-force approaches impractical at scale.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
eDesk Sales Orders, tracking links, and marketplace-specific metadata have no native
eDesk Sales Orders, tracking links, and marketplace-specific metadata have no native equivalent in Pylon. If this data is business-critical, archive it separately or flatten it into Pylon custom fields before migration. There is no way to reconstruct marketplace order context inside Pylon. (developers.edesk.com)
Preserve the original eDesk status in a custom field if you need audit accuracy
The Pylon state set is smaller and semantically different.
Pylon custom fields support these types
text, number, decimal, boolean, date, datetime, user, url, select, and multiselect. If eDesk has a custom field type that does not map cleanly (e.g., rich text), convert to text and accept the formatting loss. Match option sets for select fields before loading data — Pylon will reject values not in the defined option list. (docs.usepylon.com)
Every Issue must be associated with an Account or Contact
If an eDesk ticket lacks a valid customer email, the Pylon API will reject the payload. Build fallback logic to assign orphaned tickets to a default "Unknown Customer" Account and Contact record.
Pylon's 10 requests/minute ceiling on Issues is the primary throughput bottleneck
This rate limit is per-workspace, not per-token — creating multiple API tokens does not increase throughput. Every issue creation and every issue list query consumes from this budget. At this rate, loading 10,000 issues takes approximately 16.7 hours. Loading 50,000 issues takes roughly 3.5 days of continuous writes. Plan your migration timeline around this constraint.
Checkpoint everything
Write each created Pylon ID to a local mapping file (SQLite is recommended over JSON for concurrent access safety) as you go. If the migration fails at record 5,000 of 10,000, you need to resume from 5,001 — not restart from zero. The code samples above demonstrate this pattern with the migration_checkpoint.db SQLite database.
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 eDesk
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 Pylon can hold your support model
Walk your current workflow through Pylon: 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 eDesk → Pylon 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.
eDesk → Pylon specifics
- Business model shift
- Companies that started in eCommerce and evolved into B2B SaaS or service businesses need account-centric support, not order-centric support. eDesk's data model does not map to recurring customer relationships.
- Channel alignment
- B2B customers live in Slack Connect and Teams. Pylon integrates natively with these channels. eDesk is built for email, marketplace messaging, and social — not real-time collaboration.
- Cost and stack fit
- eDesk's API access requires the Enterprise plan. Teams that no longer need marketplace integrations are paying for features they will not use. (edesk.com)
- Small business (<5K tickets, no dev team)
- Third-party migration tool with a test run first. Validate Pylon adapter coverage before committing.
- Mid-market (5K–50K tickets, some engineering bandwidth)
- API-based migration with a staged approach. Extract to a local database first, transform, then load.
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 eDesk 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 eDesk 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 eDesk 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 eDesk → Pylon 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 eDesk → Pylon 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 Pylon 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 Pylon sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Pylon 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 Pylon'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 Pylon, 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 eDesk 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 Pylon'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 eDesk 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 eDesk read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
eDesk → Pylon specifics
- For small datasets (<500 Issues)
- Delete individually via the Pylon UI or DELETE /issues/{id} API calls (if available — verify with Pylon support)
- For large datasets
- Contact Pylon support for bulk cleanup assistance, or provision a fresh Pylon workspace and re-run the migration
- Best practice
- Always run test migrations into a separate workspace before touching production. The cost of a second workspace is negligible compared to recovering from a bad production load.
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 eDesk and Pylon 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 Pylon, 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 eDesk 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.
eDesk → Pylon specifics
- Ongoing sync (post-migration)
- Middleware platform for new ticket forwarding only.
- Validation overhead
- Building record-count comparison, field-level validation, and sampling logic is its own project — typically 20–30% of total migration engineering time
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.
Concept eDesk
| eDesk field | Pylon field | Notes |
|---|---|---|
| Core record | Ticket (tied to channel/order) | 1:1 mapping possible, but context differs |
| Customer | Customer record | eDesk customers flatten to Pylon contacts |
| Company | No formal company object | Must be derived from customer email domains or tags |
| Conversations | Messages within a ticket | Thread structure differs |
| Order data | Sales Orders, tracking links, order notes | Must be archived or stored in custom fields |
| Channels | Amazon, eBay, Shopify, email, chat, social | Marketplace channels have no Pylon equivalent |
| Tags | Tags and tag groups | Direct mapping |
| Custom fields | Custom fields on tickets | Type matching required |
| Automations | eDesk rules and templates | Must be manually rebuilt |
| Knowledge base | eDesk knowledge base | Separate migration required |
Object Object
| eDesk field | Pylon field | Notes |
|---|---|---|
| Customer | Contact | Map by email address. Deduplicate first. |
| Customer (company domain) | Account | eDesk has no company object — derive Accounts from customer email domains or tags. |
| Ticket | Issue | 1:1 mapping. Map status, priority, tags, custom fields. |
| Message (in ticket) | Message (in Issue) | Map as reply or internal note based on message type. |
| Note / Order note | Private message or Account Activity | Use is_private=true for internal notes. |
| Tag | Tag | Direct mapping. Create tags in Pylon first. |
| Custom Field | Custom Field | Type-match required. |
| Sales Order | No equivalent | Archive or flatten to Issue custom fields. |
| Template | Macro | Manual recreation required. |
| Knowledge Base Article | KB Article | Separate migration via KB API. |
Status Recommended Pylon State
| eDesk field | Pylon field | Notes |
|---|---|---|
| Open | waiting_on_you | Active, needs agent action |
| Pending | waiting_on_customer | Awaiting customer response |
| Closed | closed | Direct map |
| Archived | closed | Preserve original status in a custom field |
| Spam | Exclude from migration | Or store in custom field for audit |
Ticket Issue
A 1:1 ticket-to-issue mapping is possible via Pylon's /import/issues endpoint, but contextual differences between order-centric tickets and account-centric issues require careful transformation.
| eDesk field | Pylon field | Notes |
|---|---|---|
| ticket_id | Custom field: edesk_ticket_id | Cast to string. Critical for deduplication and delta syncs. |
| subject | title | Direct map. Default to "(no subject)" if empty. |
| body | body_html | Convert to HTML if plain text. Wrap in <p> tags. |
| status | state | Map per status table above. |
| priority | priority | Map values. |
| channel | Custom field: source_channel | Store as metadata. Marketplace channels have no Pylon equivalent. |
| assigned_agent | assignee_id | Lookup Pylon user ID by agent email. |
| tags | tags | Array of tag slugs. |
| created_at | created_at | ISO 8601. Ensure UTC normalization. |
| customer_email | requester_email | Direct map. |
| customer_name | requester_name | Direct map. |
| Custom fields | custom_fields | Slug-based mapping with type conversion. |
| sales_order.order_id | Custom field: order_id | Flatten. No native order object in Pylon. |
| messages [].body | Message body_html | Convert to HTML. |
| messages [].attachments | attachment_urls | Re-upload to Pylon. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets / Issues | medium | A 1:1 ticket-to-issue mapping is possible via Pylon's /import/issues endpoint, but contextual differences between order-centric tickets and account-centric issues require careful transformation. |
| Conversation History | high | Message threads, internal notes, and attachments must be extracted via the eDesk API since CSV exports exclude them entirely, and Pylon's 10 req/min rate limit significantly constrains import throughput. |
| Contacts / Customers | medium | eDesk customer records map to Pylon contacts, but each contact must be linked to a Pylon account that does not exist in eDesk and must be derived from email domains or tags. |
| Accounts / Companies | high | eDesk has no formal company object, so Pylon accounts must be synthetically constructed from customer email domains, tags, or external CRM data, introducing significant deduplication risk. |
| Sales Orders & Marketplace Data | high | Pylon has no equivalent for eDesk's sales orders, tracking links, or marketplace-specific metadata, meaning this data must be archived externally or lossy-compressed into custom fields. |
| Tags | low | Both platforms support tags with a direct mapping, making this one of the most straightforward entities to migrate. |
| Custom Fields | medium | eDesk custom fields can map to Pylon custom fields on issues, contacts, or accounts, but field type matching and validation are required to avoid data truncation or type mismatches. |
| Attachments | medium | Attachments must be extracted via the eDesk API and re-uploaded to Pylon, adding complexity and increasing migration time due to file transfer and rate-limit constraints. |
| Automations & Workflows | high | eDesk rules and templates cannot be programmatically exported or imported into Pylon's trigger and macro system and must be manually rebuilt from scratch. |
| Knowledge Base | medium | Knowledge base articles require a separate migration effort with content reformatting, as there is no direct transfer mechanism between eDesk's and Pylon's knowledge base systems. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
No Native Migration Path
Neither eDesk nor Pylon provides a built-in import/export bridge between the two platforms, requiring custom API integration or third-party tooling for any production migration.
Incompatible Core Data Models
eDesk's flat, order-centric model (Customer → Ticket → Messages → Sales Orders) must be restructured into Pylon's relational, account-centric hierarchy (Account → Contact → Issue → Messages), with company/account objects derived from email domains or tags since eDesk has no formal company entity.
Pylon API Rate Limits
Pylon's Issues API is capped at 10 create requests per minute, meaning a 50,000-ticket migration takes approximately 3.5 days of continuous, uninterrupted API writes.
Marketplace Data Has No Equivalent
eDesk's sales orders, tracking links, and marketplace-specific metadata have no native counterpart in Pylon and must be archived separately or flattened into custom fields before migration.
Limited CSV Export Capabilities
eDesk's CSV export is Enterprise-only, capped at 1,000 rows, and excludes conversation threads, attachments, and internal notes, making it unsuitable for anything beyond a proof-of-concept.
Automation and Workflow Rebuild
eDesk rules, templates, and knowledge base content cannot be programmatically transferred and must be manually recreated as Pylon triggers, macros, and knowledge base articles.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate from eDesk to Pylon using CSV export?
Not for production use. eDesk's Search Download is Enterprise-only and capped at 1,000 rows. It excludes conversation history, attachments, and internal notes. You need the eDesk API (Enterprise plan required) to extract full ticket data, then load via Pylon's REST API or historical import endpoint.
What are the Pylon API rate limits for migration?
Pylon's Issues endpoints (GET and POST) are limited to 10 requests per minute. Account, Contact, and most other endpoints allow 60 requests per minute. Issue updates allow 20 per minute. This means loading 10,000 issues takes approximately 16.7 hours of continuous API writes.
How long does an eDesk to Pylon migration take?
It depends on volume. Loading into Pylon is the bottleneck at 10 Issues/min. A 5,000-ticket migration takes roughly 8+ hours of load time. A 50,000-ticket migration takes 3–4 days. Add 1–2 weeks for pipeline development and validation if building in-house.
What eDesk data is lost when migrating to Pylon?
Pylon has no native equivalent for eDesk Sales Orders, order tracking links, marketplace channel metadata, or order notes. This data must be archived separately or flattened into Pylon custom fields. Marketplace-anonymized email addresses also cannot be resolved to real contacts.
Can open eDesk tickets stay live after moving to Pylon?
Not through standard historical backfill. Pylon's documented migration path covers closed tickets and knowledge base content. Live open-ticket continuity requires a separate cutover or handoff workflow.