Bidirectional sync
Update both sides when either system changes — with loops that stay bounded.
DATA SYNC
Two systems, one truth — without nightly exports
CRM, support desk, spreadsheets, and warehouses drift the moment someone skips an export. WEXTL keeps records aligned with scheduled syncs, webhooks, and reconcile jobs that log every row they touch.
Deploys this workflow into your workspace — you'll connect your own accounts.
Bidirectional sync between CRM and help desk
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- Data Encryption
- 2FA
- Local Data Region
* Illustrative example, not measured customer data.
Data Sync automation, at a glance
Warehouse ETL
Move rows from sheets into tables and BI datasets on a schedule.
Webhook bridge
Turn inbound webhooks into CRM and ops records — bad payloads filtered out.
Nightly reconcile
Compare billing to ledger rows and alert on mismatches before close.
Sync patterns that scale
Keep CRM and support aligned
Aggregate changes from both systems and update records in a bounded loop.
- Trigger — a record changes on either side: a deal stage updates in the CRM, or a ticket's plan/tier field changes on the help desk.
- Information collected — record ID, the changed field(s), the timestamp of the change, and whether the other side has a newer un-synced change to that same field.
- Connected applications — Salesforce or Pipedrive for the CRM side, Zendesk or Freshdesk for the help desk side.
- Decision logic — checked against a source-of-truth map per field (billing fields only ever flow from the CRM, ticket status only ever flows from the desk) so both sides can never claim ownership of the same field at once.
- AI step — normalizes free-text fields (a support agent's note, an inconsistent CRM picklist value) into the structured value the receiving system expects.
- Human approval — a genuine field conflict (both sides changed the same field since the last sync) routes to a person instead of picking a side automatically.
- Actions — writes the update to the other system and stamps a synced-at timestamp so the loop recognizes its own change and doesn't re-fire.
- Exception handling — a record deleted on one side with no matching delete rule pauses that record's sync and flags it instead of deleting it everywhere by default.
Bidirectional sync between CRM and help desk
What data ops actually spends a day fighting
None of this needs a data engineer. It needs to happen every day, in four systems — CRM, help desk, spreadsheets, and the ledger — without anyone noticing when it doesn't.
A rep edits a deal at 9am; the help desk ticket for that account still shows the old plan at 2pm because nobody remembered to update it by hand. A Friday afternoon means someone exports a CSV from the CRM, cleans it in a spreadsheet, and uploads it to the warehouse — and if they're out sick, the dashboard is a week stale before anyone notices. A finance close starts with a $340 gap between what Stripe charged and what the ledger sheet says, discovered the morning of the board call instead of the week before.
- Step 1
CRM and help desk disagree
One system updates; the other finds out days later, if at all.
- Step 2
The Friday export ritual
One person's CSV habit is the only thing keeping the warehouse current.
- Step 3
Mismatches found at close
A billing-to-ledger gap surfaces the morning of the board meeting.
None of it needs judgment about which number is right. It needs someone — or something — checking every night.
Example: automate CRM to help desk sync
A concrete run, start to finish — the two-way sync a RevOps analyst clones from the template library and points at their own CRM and help desk.
Trigger: a record changes on either side — a deal stage updates in the CRM, or a ticket's plan or tier field changes on the help desk.
WEXTL checks: which system owns that field under the source-of-truth map, whether the other side has a newer un-synced change to the same field, and whether the record is on the exclusion list (test accounts, archived deals).
Human judgment
A genuine field conflict, a delete without a matching rule, or a record outside the sync scope routes to a person instead of a guess. The workflow proposes the update; a person resolves the conflict.
- Step 1
1. Change detected
A field updates in the CRM or a ticket updates on the help desk.
- Step 2
2. Look up the field owner, check for conflicts
Checks the source-of-truth map, then flags it if the other side changed the same field since the last successful sync.
- Step 3
3. Normalize the value
AI maps free-text notes or inconsistent picklists to the field the receiving system expects.
- Step 4
4. Check the exclusion list, hold conflicts
Test accounts and archived deals are skipped; a genuine conflict routes to a person instead of picking a side automatically.
- Step 5
5. Write the update
Pushes the change to the other system and stamps a synced-at timestamp.
- Step 6
6. Log the row and notify the owner
Every write is recorded for audit, and a Slack note reaches the record owner when a field they watch changes.
Runs overnight, timed to finish before the close meeting, and reads charges from the payment processor plus revenue rows from the ledger for the same date range.
Pairs rows by invoice or order ID, not by amount alone.
Groups likely-same mismatches — a rounding diff vs. a genuinely missing row — so the alert leads with what matters.
Every mismatch is proposed for a human decision, never silently written to either ledger.
Each diff is logged with a timestamp, and a Slack summary posts before the close meeting.
A mismatch marked 'known timing difference' doesn't re-alert every night.
Example: automate the nightly billing-to-ledger reconcile
The same discipline, applied to finance: compare two sources every night instead of discovering a gap at close.
Trigger: a scheduled run, timed to finish before the morning close meeting.
Sources compared: charges from the payment processor and revenue rows in the ledger or CRM for the same date range, matched on invoice or order ID.
Human judgment
The workflow never rewrites the ledger or the CRM on its own — every mismatch is a proposal a person confirms, adjusts, or marks as a known timing difference.
WEXTL is not trying to replace the person who owns data quality — it handles the row-by-row mechanics so that person can spend their time deciding what a conflict means, not chasing which system is right.
Who data sync automation is for
Not every team needs a dedicated sync workflow. It's built for the moments where two or more systems have to agree, and nobody wants to be the manual bridge between them.
RevOps analysts
— Own the CRM and every system that reads from or writes to it.Data or ops engineers without a dedicated pipeline team
— Building ETL by hand in scripts nobody else can maintain.Agencies running multiple client CRMs
— The same sync logic has to run per client account, not once.Finance ops closing the books
— Reconciling billing against the ledger before every close.IT admins bridging HR and finance systems
— New hire and termination events have to reach payroll and access systems the same day.E-commerce ops merging orders across channels
— Orders from a storefront, a marketplace, and a POS all need to land in one inventory count.
Connect the systems your data actually lives in
You don't pick your systems of record — the CRM, the help desk, and the accounting tool are already decided elsewhere. What you control is whether they agree with each other.
These are the integrations most data sync workflows on WEXTL actually connect. An HTTP step reaches anything else with an API.
CRM
Support
See the full catalog — every app we support, plus an HTTP step for anything else.Explore all WEXTL integrations
Warehouse loads without the Friday ritual
Someone still exports a CSV on Fridays so the warehouse table doesn't go stale over the weekend.
Schedule the pull, validate the rows against the destination schema, and load — with an alert if the source sheet is empty or malformed.
Step 1
Scheduled pull
Runs nightly instead of waiting for someone to remember.
Step 2
Row validation
Type-checks each row before it reaches the warehouse table.
Step 3
Load log
Every batch records row count and any rejects.
Webhooks that keep systems talking
Polling once an hour is how 'eventually consistent' becomes 'visibly wrong' to a customer.
Accept the event, validate the payload, write the record, and acknowledge — with retries queued if the destination is briefly down.
Instant trigger
Fires the moment the event lands, not on the next poll.
Signature checked first
Unverified payloads are dropped before they touch a record.
Retry with backoff
A brief CRM outage queues the write instead of losing it.
22+ apps for Data Sync
Integrate Data Sync workflows with the tools your team already uses.
Supported integrations
Data sync, by hand vs. with WEXTL
| Task | By hand | With WEXTL |
|---|---|---|
Keeping systems aligned | Manual exports and hope | Supported |
Partner webhooks | Custom code per partner | Guarded branches on the canvas |
Warehouse loads | Ad hoc scripts | Supported |
Reconciliation | Discovered at month-end | Nightly compare with alerts |
Frequently asked questions
Every shared field has exactly one owning system in a source-of-truth map (billing fields only ever flow from the CRM, ticket status only from the desk), and each write stamps a synced-at timestamp so the workflow recognizes its own change on the next pass instead of re-firing on it.
That’s a genuine conflict, and it routes to a person instead of getting picked automatically - the run shows both values and who changed each one, so the decision takes seconds instead of a Slack thread reconstructing what happened.
Yes - it runs overnight, timed to finish before the close meeting, matches charges to ledger rows by invoice or order ID, and posts the diff to Slack. It never rewrites the ledger itself; a person confirms, adjusts, or marks each mismatch as a known timing difference.
ETL is one-way and batch: sheet rows land in a warehouse table on a schedule, validated against the destination schema first. Operational sync is two-way and near-real-time, with a conflict map, because both systems are being actively edited by people at the same time.
That event type pauses instead of guessing a field mapping. It waits for a person to approve how the new field maps to your schema before it writes anything - a wrong guess here means bad rows silently entering a CRM or ledger.
CRMs like Salesforce and Pipedrive, help desks like Zendesk and Freshdesk, spreadsheets and Airtable, BI tools like Power BI, and accounting tools like QuickBooks and Stripe are native connections. An HTTP step covers any system with an API that isn’t built in yet.
Clone the CRM <-> help desk sync or the nightly reconcile template, and write down the source-of-truth map for each shared field - which system wins - before turning on writes in either direction. Guessing the map after the sync is live is how you get a dual-write loop.
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