Hi n8n community! 
I’m building a behavioral intelligence integration between MindScan API and GoHighLevel (GHL) using n8n. Before I start building, I’d love your feedback on my workflow design and any potential loopholes I might be missing. ## 
Use Case When certain events happen in GHL (form submitted, call booked, payment received, tag added), we want to automatically run a behavioral scan on that contact via MindScan API and save the results back to GHL custom fields. ## 
Proposed Workflow ### Step 1: GHL Side (Trigger Events) - Advisor Application form submitted - Advisor Alignment call booked - Capital Engine application submitted - Payment received (Catalyst/Core/Circle Fellowship) - Manual tag added (for hiring) Each workflow sends a webhook to n8n with: → Name, Email, Phone, DOB, City, Country, Contact ID, Tags ### Step 2: n8n Workflow 1. **Webhook Node** — Receives contact data from GHL 2. **IF Node** — Validates DOB + City/Country exist - If missing → Stop workflow (optionally send internal notification) - If available → Continue 3. **Set Node** — Formats data for MindScan API, decides report type (Sales/Leadership/Hiring) based on tags 4. **HTTP Request Node** — POST to MindScan API, receives behavioral report 5. **HighLevel / Update Contact Node** — Saves key fields from MindScan report back to GHL custom fields ## 
Time Estimate - Minimum: ~3 weeks - Realistic: 4–6 weeks - With unexpected issues: 6–8 weeks ## 
Questions for the Community 1. **Workflow Design** — Does this approach make sense? Any missing nodes or better alternatives? 2. **Error Handling** — What’s the best way to handle: - MindScan API failures (timeouts, 5xx errors)? - Rate limiting (if scanning many contacts at once)? - Invalid/expired API keys? 3. **Credit Optimization** — MindScan docs say “stakeholders are never rescanned” but it’s unclear: - If we run a Quick Scan (1 credit), then later a Deep Scan (3 credits) on the same person, does it use cached data or charge full 3 credits? - Any experience with this API? 4. **GHL ↔ n8n Connection** — Should I use: - Webhooks (current plan), OR - Official GHL integration node with Private API key, OR - GHL API v2 with OAuth? 5. **Data Privacy** — Best practices for handling sensitive behavioral data (DOB, personality profiles) in n8n workflows? 6. **Testing Strategy** — How do you typically test API integrations before going live with client data? - Sandbox environment? - Dummy contacts? - Parallel testing? ## 
Tech Stack - GoHighLevel (CRM) - MindScan Intelligence API (Behavioral Intelligence) - n8n (self-hosted or cloud) ## 
What I’m Looking For - Workflow optimization suggestions - Common pitfalls to avoid - Best practices for API integrations like this - Anyone who’s worked with MindScan API before? Thanks in advance for your insights! 
Hey @AbdullahShah, while you wait for a response, here are some things that might help:
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@Dedi_srl, @jitesh, @Niffzy - you’ve helped with similar issues before, can you take a look?
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You’ve thought this through more than most pre-build posts, and the spine is right — GHL event → validate → Set picks report type → HTTP to MindScan → write-back is the correct shape. What I’d pressure-test before you write a node are the parts that get expensive quietly rather than loudly.
Privacy first, because it’s the one people skip. Behavioral profiles and DOB pass through n8n’s execution data on every run, and n8n has no per-field redaction — the HTTP node stores MindScan’s full raw response whatever you forward downstream. So for this workflow, not persisting is the only reliable lever: turn off saved execution data, and specifically for the failed runs too, since your dead-letter path is exactly where a timeout would otherwise write the full DOB into the executions table. A short retention on whatever you do keep is the backstop. Same execution-data persistence that’s straining your other batch run, seen from the privacy side.
Error handling and the credit question are really one problem: idempotency. The same event can arrive more than once — delivery retries, tag re-adds, workflow re-entry — and if each fires a fresh scan you pay a credit for a duplicate. That’s the node missing between your validate step and the HTTP call: a small state table keyed by contact + scan-type, checked before you call, so a repeat is a cache hit not a charge. That answers your Quick-Scan/Deep-Scan question on your side regardless of how MindScan bills it. Separate concern from dedup: for many distinct contacts hitting the API at once, cap concurrency and honor the 429 Retry-After rather than firing them all in parallel. For failures, route 5xx and timeouts into bounded retries and a dead-letter row that holds contact ID + error, not the raw payload — and treat an expired key as its own alert path so it doesn’t read as a transient blip.
On GHL↔n8n: webhook is right for the inbound trigger, but do the write-back through the API v2 / OAuth credential rather than a long-lived Private API key sitting in n8n. It’s also a privacy call — fewer standing secrets in the workflow, and credential failures surface as an auth error you can catch and rotate.
On testing: build against dummy contacts pointed at a throwaway sub-account, and assert on the write-back, not just the API call — this kind of integration breaks most often at the “save to custom field” step, not the scan.
The last one I built in this shape — a US healthcare intake system, same PII-and-rate-limit constraints — is written up at Work — Priyanshu Kumar.
The one choice that decides the rest: are you planning a datastore between GHL and MindScan, or trying to stay stateless? That fork decides whether idempotency and caching live in the workflow or have to be faked in GHL custom fields — worth settling before you build.
— Priyanshu Kumar
Hi Abdullah!
Your overall structure makes sense. We have built several workflows between GHL and n8n, and using GHL webhooks as triggers is a good approach.
I would add a few precautions before going live.
First, make sure the same contact is not scanned twice for the same event or report type. GHL can sometimes trigger the same action more than once, which could lead to duplicate scans and unnecessary credit usage.
I would also send clear information from GHL, not only tags, such as:
-
Event type
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Report type
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GHL Contact ID
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Event date and time
This makes the workflow easier to manage and reduces confusion when a contact has multiple tags.
For error handling, retries should only be used for temporary problems such as timeouts, rate limits or server errors. For missing data, invalid credentials or incorrect requests, the workflow should stop and send an internal notification.
I would also keep a simple external log showing whether the contact was received, scanned, updated in GHL or failed. Relying only on n8n execution history may not be enough when you need to investigate an issue weeks later.
When updating GHL, always use the Contact ID received from the webhook. Searching only by email or phone can create problems when duplicate contacts exist.
Webhooks are a good choice for triggers. For updating contacts, GHL API v2 through an HTTP Request node usually gives more control than the standard integration node. A Private Integration is fine for one location, while OAuth is better if the solution will be used across multiple client accounts.
If many scans may arrive at the same time, I would process them in small groups or through a simple queue instead of sending all requests at once.
Regarding MindScan credits, I would not assume that a previous Quick Scan automatically reduces the cost of a later Deep Scan. I would ask MindScan directly what happens when the same stakeholder ID is used again.
For privacy, avoid storing full reports in n8n logs, keep API keys inside n8n Credentials and save only the behavioral data that users actually need in GHL.
For testing, use dummy contacts, a separate GHL test location and an initial phase where the workflow records the result without updating real contacts.
Overall, the idea is good. The main areas to strengthen are duplicate prevention, error handling, activity logging and testing before using real client data.