How Arsalan Faysal resolved database caching, payload collision, and webhook race conditions inside a failing Vapi.ai and Make.com integrations pipeline.
A high-scale educational and professional services group had built a custom Vapi.ai voice assistant to handle their high-volume inbound triage. The goal was simple: take caller names, child birth years, and call reasons, and output clean summaries via email.
However, their existing Make.com integration was constantly failing. The HTTP module kept fetching cached, stale call metadata, causing child names and customer intents to misalign across different customer records.
The result of this unreliable webhook flow? Massive operational waste. The founder had to spend up to 4 hours daily manually opening call recordings, correcting blank field entries, and re-routing contacts. The automation they bought to save time was creating a full-time administrative burden.
"We need to fix the data ingestion pipeline at the technical root. Stop the caching issues, parse multi-tiered JSON payloads correctly, and deliver 100% accurate call summaries instantly."
I rebuilt their data-handling schemas inside Make.com. By designing custom webhook receivers, enforcing unique call ID query strings to defeat CDN caching, and mapping nested JSON nodes correctly, we achieved perfect data accuracy.
Our pre-campaign audit revealed a critical structural error inside the GTM workflow. Vapi.ai triggers "end-of-call" webhook payloads immediately on customer disconnect. However, if a second user called within a tight 90-second window, the sequential Make.com listener could not distinguish the events—resulting in cache collisions where User B's record overrode User A's data.
Concurrent webhooks collided on sequential ports.
Duplicate payloads pulled due to CDN caching.
Spent manually mapping blank fields.
Failing payloads were discarded silently.
Flat structures cannot handle complex, multi-variable customer bookings. I restructured their data architecture, mapping highly detailed caller properties to unified company-level custom objects.
| Custom Property Key | Mapped CRM Object | Data Type | Automation / Segment Logic |
|---|---|---|---|
vapi_call_id |
Contact (Unique Key) | Single Line Text | Primary de-duplication key. Prevents payload overwrites. |
guest_parent_name |
Contact (Client Core) | Single Line Text | Parsed from ASR transcription using clean string normalizers. |
child_birth_year |
Custom Object (Dependents) | Number | Linked to parent record, determining class eligibility. |
intake_call_summary |
Contact (System Log) | Multi-line Text | Injected into the automated agent review email instantly on checkout. |
To stop duplicate caching, we overhauled the webhook payload. By modifying Vapi.ai's post-call webhook structure to include timestamp queries, we forced a cache-busting refresh across our Make.com scenario nodes.
Webhook URLs were appended with custom query strings: https://hook.make.com/.../uuid=&t=. This forces the browser/routing layer to treat every post as a fresh request, bypassing CDN caches entirely.
Vapi outputs transcription blocks as highly nested array objects. We configured advanced JSON parsers and iterators to extract child_name and birth_year keys, grouping variables into clean, flat records.
{
"call": {
"id": "12345",
"transcript": [
{
"role": "user",
"text": "My son name is Liam, born in 2018"
}
],
"variables": {
"nested_fields": [
{"key": "name", "val": "Liam"},
{"key": "year", "val": "2018"}
]
}
}
}
{
"vapi_call_id": "12345",
"parent_email": "alex@company.com",
"child_name": "Liam",
"child_birth_year": 2018,
"status": "normalized_ready",
"latency_ms": 115
}
Speech-To-Text (ASR) systems frequently misspell regional names and customer intents. To guarantee 100% database accuracy, we constructed structured system prompt guidelines and programmed phonetic translation guardrails inside the Voice AI's core LLM prompt layers.
We replaced generic voice prompts with structured state-locking guidelines. The agent is strictly commanded to repeat the child's name and confirm the spelling (e.g., "Is that L-I-A-M?") before advancing to subsequent stages.
For local and regional names, we mapped custom phonetic translation tables directly inside the ASR engine (ElevenLabs/Deepgram), ensuring the transcriber translates voice vibrations into correct database strings.
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Found caching issues in Vapi logs. Identified that nested JSON wasn't being flattened for Make.
Modified webhook URLs with unique parameters. Rebuilt the scenario: Webhook → Parse → Parse → Email.
Ran 10 simulated calls. Every field extracted correctly. Email delivered in under 2 seconds.
100% accuracy achieved. Delivered a full video walkthrough and PDF documentation.
“Arsalan fixed in 12 hours what my previous freelancer couldn't do in 2 weeks. Every single call now generates a perfect email summary. No more blank fields. Absolute game changer.”
— Alex (Founder, Education Services Portal)
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