January 22, 2026

JotForm to Zoho CRM, screened candidates routed fast

Lisa Granqvist Partner Workflow Automation Expert

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Resume intake gets messy fast. One inbox thread turns into ten, PDFs get renamed three different ways, and “I’ll log it in the CRM later” quietly becomes next week.

If you’re a recruiter, you feel it in response times. A busy HR manager feels it in follow-ups. And a founder doing hiring between meetings feels it in lost context. This JotForm Zoho integration automation gives you a clean pipeline: resumes are scored, routed, and replied to automatically.

Below, you’ll see exactly what the workflow does, what results to expect, and what you need to run it in n8n without babysitting every submission.

How This Automation Works

The full n8n workflow, from trigger to final output:

n8n Workflow Template: JotForm to Zoho CRM, screened candidates routed fast

Click to explore

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The Problem: Resume screening turns into manual triage

When resumes arrive through a form, the work doesn’t stop at “download PDF.” Someone still has to open it, skim for key requirements, decide if it’s worth a call, then copy the candidate details into a CRM (or a spreadsheet, or both). It’s easy to miss strong candidates just because you’re busy, and it’s just as easy to waste time on weak ones because “maybe they’re fine” is faster than a real evaluation. Add email replies on top, and your day becomes routing, not recruiting.

It adds up fast. Here’s where it breaks down in real hiring teams.

Every submission creates 10–15 minutes of repetitive work before you even make a decision.
Candidate data gets entered twice, which means typos, duplicates, and awkward “we already have you in the system” moments.
Good candidates wait while you catch up, and frankly they don’t wait long.
Feedback emails get delayed or skipped entirely, which hurts your employer brand and increases follow-up pings.

The Solution: AI resume screening with Zoho CRM routing

This workflow starts the moment someone submits a resume through JotForm (name, email, and a PDF attachment). n8n stores a clean record in PostgreSQL so you always have an internal source of truth, even if a downstream tool hiccups. The PDF is converted into images (via PDF.co), then Azure Vision OCR extracts the text so the resume becomes machine-readable. From there, GPT-4.1 reviews the content and produces practical notes like strengths, improvement areas, and a numeric score. Finally, the workflow makes a decision: strong candidates get routed into Zoho CRM and receive a “next steps” email, while others receive a polite feedback email automatically.

The workflow begins with a JotForm trigger. It then converts and reads the resume, has AI analyze it, and applies a score threshold (currently set to 85). After that, Zoho CRM and Gmail handle the routing and response so you don’t have to.

What You Get: Automation vs. Results

What This Workflow Automates

Results You’ll Get

Captures candidate name, email, and resume PDF from JotForm automatically.
Converts the PDF into images and extracts text using OCR, so AI can read it reliably.
Generates an AI resume review with strengths, gaps, and a score you can route on.
Creates a Zoho CRM entry for qualified candidates and sends Gmail responses based on the score.

Most teams get about 5 hours back per week once volume hits 30+ resumes.
You respond faster, which helps you book screens before candidates go cold.
Your CRM stays clean because records are created the same way every time.
Hiring decisions become easier to explain because every score has written reasoning.
Rejected candidates still get a timely reply, reducing follow-up churn.

Example: What This Looks Like

Say you get 10 resumes per day through JotForm. Manually, you might spend about 10 minutes reviewing each PDF, plus another 5 minutes logging details into Zoho CRM and sending a reply, so that’s roughly 2.5 hours a day. With this automation, submission takes zero extra effort, OCR + AI processing runs in the background (often around 5–10 minutes total), and only the “worth a call” candidates need your attention. In practice, you spend your time on interviews, not sorting.

What You’ll Need

n8n instance (try n8n Cloud free)
Self-hosting option if you prefer (Hostinger works well)
JotForm to collect candidate submissions and PDFs
Zoho CRM to store qualified candidates as leads
PDF.co API key (get it from your PDF.co dashboard)
Azure Vision OCR key (get it from Azure Portal for Computer Vision)
OpenAI API key (get it from the OpenAI API dashboard)
Gmail account to send score-based candidate emails
PostgreSQL database to store every submission record

Skill level: Intermediate. You’ll connect a few services, add API keys, and test with a sample resume submission.

Don’t want to set this up yourself? Talk to an automation expert (free 15-minute consultation).

How It Works

Resume submission triggers everything. When a candidate submits your JotForm, n8n captures the name, email, and the resume file immediately.

A record is stored before any “smart” work happens. The workflow writes a row into PostgreSQL first, so you can audit what came in and reprocess later if needed.

The PDF is made readable for AI. PDF.co converts the PDF to JPG images, Azure Vision OCR extracts the text, and n8n waits and retries until the OCR result is ready.

AI scores and routes candidates. GPT-4.1 analyzes the merged resume text, an extractor pulls out the structured score and notes, and an “if score meets threshold” check decides what happens next.

Zoho CRM and Gmail handle the follow-through. Qualified resumes create a Zoho CRM lead via HTTP request and automatically send a congratulatory email; lower scores get a feedback email so every candidate gets a response.

You can easily modify the score threshold to fit your role, or change the email messaging to match your tone based on your needs. See the full implementation guide below for customization options.

Step-by-Step Implementation Guide

Step 1: Configure the Form Submission Trigger

Set up the workflow entry point to capture new resume submissions from Jotform.

Add the Form Submission Trigger node and set Form to 252434958811059.
Set Only Answers to false and Resolve Data to false.
Credential Required: Connect your jotFormApi credentials.

⚠️ Common Pitfall: If the form ID is incorrect, the workflow won’t receive any submissions. Double-check the Jotform ID in Form.

Step 2: Connect Postgres for Lead Storage

Create and store lead records for each resume submission in your database.

In Utility: Create Leads Table, set Operation to executeQuery and paste the provided SQL into Query.
Credential Required: Connect your postgres credentials in Utility: Create Leads Table.
In Store Submission Record, set Table to resume and Schema to public.
Map columns in Store Submission Record: given_name to {{ $json.rawRequest.q4_name.first }} {{ $json.rawRequest.q4_name.last }}, resume_loc to {{ $json.rawRequest.fileUpload[0] }}, and given_email to {{ $json.rawRequest.q5_email }}.
Credential Required: Connect your postgres credentials in Store Submission Record.

Step 3: Configure OCR Conversion and Retrieval

Convert the uploaded PDF to images, submit to OCR, and fetch the text results for analysis.

In Convert PDF to JPG, set URL to {{ $json.resume_loc }}, Operation to Convert from PDF, and Convert Type to toJpg.
Credential Required: Connect your pdfcoApi credentials in Convert PDF to JPG.
In Submit Images to OCR, set URL to https://test-app-image.cognitiveservices.azure.com/vision/v3.2/read/analyze and JSON Body to { "url": "{{ $json.body }}" }.
Credential Required: Connect your httpHeaderAuth credentials in Submit Images to OCR.
In Parse OCR Operation ID, keep the provided JavaScript that extracts operationId from the response header.
In Fetch OCR Results, set URL to https://test-app-image.cognitiveservices.azure.com/vision/v3.2/read/analyzeResults/{{ $json.operationId }}.
Credential Required: Connect your httpHeaderAuth credentials in Fetch OCR Results.
Configure Check OCR Status with the condition {{ $json.status }} equals succeeded.
Check OCR Status outputs to both Merge OCR Text and Delay for OCR Completion in parallel to handle asynchronous OCR completion.
In Delay for OCR Completion, set Amount to 2 (seconds) to pause before retrying OCR.
In Merge OCR Text, keep the JavaScript that flattens lines into a single text field.

⚠️ Common Pitfall: If the OCR API key is missing in httpHeaderAuth, Submit Images to OCR and Fetch OCR Results will return authentication errors.

Step 4: Set Up the AI Resume Analysis

Analyze the OCR text with the AI assistant and extract structured review details.

In Utility: Build AI Assistant, set Name to Resume Analyzer 101 and keep the provided Description and Instructions.
Credential Required: Connect your openAiApi credentials in Utility: Build AI Assistant.
In Analyze Resume Content, set Text to resume: {{ $json.text }} and ensure the Assistant ID matches the assistant created above.
Credential Required: Connect your openAiApi credentials in Analyze Resume Content.
In Extract Review Details, set Text to {{ $json.output }} and keep the attributes for good_side, to_improve, and Score.
Open OpenAI Chat Model and confirm it is connected as the language model for Extract Review Details — ensure credentials are added to OpenAI Chat Model, not the extractor.

Step 5: Configure Scoring Logic and CRM Updates

Convert the AI score to a number, evaluate a qualification threshold, and create CRM records for top candidates.

In Cast Score to Number, set the assignment output.Score to type number and value {{ $json.output.Score }}.
In Evaluate Score Threshold, set the condition to {{ $json.output.Score }} greater than 85.
In Create Qualified CRM Entry, set URL to https://www.zohoapis.com/crm/v6/qualified_resume and keep the JSON Body expression that maps name, email, and score.
Credential Required: Connect your zohoOAuth2Api credentials in Create Qualified CRM Entry.

Step 6: Configure Email Outputs

Send the appropriate email based on the qualification outcome.

In Send Improvement Email, set Send To to {{ $item("0").$node["Form Submission Trigger"].json["rawRequest"]["q5_email"] }} and keep the HTML Message template.
Credential Required: Connect your gmailOAuth2 credentials in Send Improvement Email.
In Send Congrats Email, set Send To to {{ $item("0").$node["Form Submission Trigger"].json["rawRequest"]["q5_email"] }} and keep the HTML Message template.
Credential Required: Connect your gmailOAuth2 credentials in Send Congrats Email.
Confirm the routing: Evaluate Score Threshold → Create Qualified CRM Entry → Send Congrats Email for qualified candidates, otherwise Send Improvement Email.

Step 7: Test and Activate Your Workflow

Verify the end-to-end flow and then turn it on for production.

Click Execute Workflow and submit a test entry through the connected Jotform.
Successful execution should show Store Submission Record inserting data, Merge OCR Text producing a text field, and Extract Review Details returning good_side, to_improve, and Score.
Check that Create Qualified CRM Entry receives a payload when Evaluate Score Threshold is true, and that the correct email node fires.
Once verified, toggle the workflow to Active for live submissions.

🔒

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Common Gotchas

Zoho CRM credentials can expire or need specific permissions. If things break, check your Zoho API access and connection settings in n8n credentials first.
If you’re using Wait nodes or external rendering, processing times vary. Bump up the wait duration if downstream nodes fail on empty responses.
Default prompts in AI nodes are generic. Add your brand voice early or you’ll be editing outputs forever.

Frequently Asked Questions

How long does it take to set up this JotForm Zoho integration automation?

About 45 minutes if your API keys are ready.

Do I need coding skills to automate resume screening and routing?

No. You’ll paste credentials, map a few fields, and run a test submission.

Is n8n free to use for this JotForm Zoho integration workflow?

Yes. n8n has a free self-hosted option and a free trial on n8n Cloud. Cloud plans start at $20/month for higher volume. You’ll also need to factor in OpenAI and OCR usage costs (often a few cents per resume, depending on length).

Where can I host n8n to run this automation?

Two options: n8n Cloud (managed, easiest setup) or self-hosting on a VPS. For self-hosting, Hostinger VPS is affordable and handles n8n well. Self-hosting gives you unlimited executions but requires basic server management.

Can I customize this JotForm Zoho integration workflow for a different role or scoring rubric?

Yes, and you should. Update the prompt used in the “Analyze Resume Content” AI node to match your job requirements, then adjust the “Evaluate Score Threshold” If node to change what counts as qualified (it’s 85 by default). Many teams also tweak the “Send Congrats Email” and “Send Improvement Email” templates so the tone matches their brand and the role.

Why is my Zoho CRM connection failing in this workflow?

Usually it’s expired or missing Zoho API permissions. Reconnect Zoho in n8n credentials, then confirm the account can create leads and access the fields you’re mapping. If it fails only during busy periods, you may also be hitting rate limits, so spacing requests or batching can help.

How many resumes can this JotForm Zoho integration automation handle?

On a typical n8n Cloud plan you can handle thousands of executions per month, and self-hosting removes execution caps (your server becomes the limit). Practically, OCR and AI are the throughput bottlenecks, so start by testing 50–100 resumes and watch queue time. If volume spikes, increase the wait time for OCR completion and consider running n8n on a stronger VPS.

Is this JotForm Zoho integration automation better than using Zapier or Make?

Often, yes. This workflow needs waiting/retry logic for OCR, structured extraction from AI output, and branching based on a numeric score, which is smoother in n8n and cheaper to run at volume. Zapier or Make can work, but multi-step AI + OCR flows get expensive quickly and are harder to debug. If you already have a simple two-step flow, you might not need to migrate. If you’re unsure, Talk to an automation expert and we’ll tell you plainly.

You end up with faster responses, cleaner records, and fewer hiring tasks that only exist because the tools don’t talk to each other. Set it up once, then let it run.

Need Help Setting This Up?

Our automation experts can build and customize this workflow for your specific needs. Free 15-minute consultation—no commitment required.

Lisa Granqvist

Workflow Automation Expert

Expert in workflow automation and no-code tools.

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"id": "5d416b9d-cfc8-4bc7-a226-6d44ddd8c41a",
"name": "Store Submission Record",
"type": "n8n-nodes-base.postgres",
"position": [
495,
195
],
"parameters": {
"table": {
"__rl": true,
"mode": "list",
"value": "resume",
"cachedResultName": "resume"
},
"schema": {
"__rl": true,
"mode": "list",
"value": "public"
},
"columns": {
"value": {
"given_name": "={{ $json.rawRequest.q4_name.first }} {{ $json.rawRequest.q4_name.last }}",
"resume_loc": "={{ $json.rawRequest.fileUpload[0] }}",
"given_email": "={{ $json.rawRequest.q5_email }}"
},
"schema": [
{
"id": "id",
"type": "number",
"display": true,
"removed": true,
"required": false,
"displayName": "id",
"defaultMatch": true,
"canBeUsedToMatch": true
},
{
"id": "given_name",
"type": "string",
"display": true,
"required": false,
"displayName": "given_name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "given_email",
"type": "string",
"display": true,
"required": false,
"displayName": "given_email",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "resume_loc",
"type": "string",
"display": true,
"required": false,
"displayName": "resume_loc",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"id"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": []
},
"credentials": {
"postgres": {
"id": "credential-id",
"name": ""
}
},
"typeVersion": 2.6
},
{
"id": "4c383e64-2247-4643-8037-f50db972bc94",
"name": "Parse OCR Operation ID",
"type": "n8n-nodes-base.code",
"position": [
0,
350
],
"parameters": {
"jsCode": "const url = $json.headers[\"operation-location\"];\nreturn [{\n  operationLocation: url,\n  operationId: url.split(\"/\").pop()\n}];\n"
},
"typeVersion": 2
},
{
"id": "c5980c58-edee-4eb9-87ae-2ca8c517f2e8",
"name": "Fetch OCR Results",
"type": "n8n-nodes-base.httpRequest",
"position": [
255,
390
],
"parameters": {
"url": "=https://test-app-image.cognitiveservices.azure.com/vision/v3.2/read/analyzeResults/{{ $json.operationId }}",
"options": [],
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth"
},
"credentials": {
"httpHeaderAuth": {
"id": "credential-id",
"name": ""
}
},
"typeVersion": 4.2
},
{
"id": "1ce7a80e-3294-4ba4-b75f-70cf29a7e233",
"name": "Delay for OCR Completion",
"type": "n8n-nodes-base.wait",
"position": [
740,
405
],
"webhookId": "99591d10-d8ed-401b-91ff-8d62bb504454",
"parameters": {
"amount": 2
},
"typeVersion": 1.1
},
{
"id": "b44563d8-8f57-4482-80f2-5592be844682",
"name": "Merge OCR Text",
"type": "n8n-nodes-base.code",
"position": [
15,
560
],
"parameters": {
"jsCode": "return [{\n  text: $json[\"analyzeResult\"][\"readResults\"]\n    .flatMap(p => p.lines.map(l => l.text))\n    .join(\"\\n\")\n}];\n"
},
"typeVersion": 2
},
{
"id": "f0eb164d-9d0c-4f87-a293-1b6304a50987",
"name": "Analyze Resume Content",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
265,
600
],
"parameters": {
"text": "=resume: {{ $json.text }}",
"prompt": "define",
"options": [],
"resource": "assistant",
"assistantId": {
"__rl": true,
"mode": "list",
"value": "[YOUR_ID]",
"cachedResultName": "Resume Analyzer 101"
}
},
"credentials": {
"openAiApi": {
"id": "credential-id",
"name": ""
}
},
"typeVersion": 1.8
},
{
"id": "dad8f0dd-4dea-4912-950d-bee352e8f9bb",
"name": "Extract Review Details",
"type": "@n8n/n8n-nodes-langchain.informationExtractor",
"position": [
525,
565
],
"parameters": {
"text": "={{ $json.output }}",
"options": [],
"attributes": {
"attributes": [
{
"name": "good_side",
"description": "=extract what is good in resume from analyzed data\nmake it short and use bulletpoints"
},
{
"name": "to_improve",
"description": "=extract what to improve in resume from analyzed data\nmake it short and use bulletpoints"
},
{
"name": "Score",
"description": "score of resume only score not out of 100 or /100"
}
]
}
},
"typeVersion": 1.2
},
{
"id": "a8528225-a4cb-4e93-97bf-aa0e682d1e0d",
"name": "Cast Score to Number",
"type": "n8n-nodes-base.set",
"position": [
755,
610
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "0122ead3-a743-4253-891a-71a0bbe7b05d",
"name": "output.Score",
"type": "number",
"value": "={{ $json.output.Score }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "d4c94436-b021-41be-aaeb-9656bb8aa687",
"name": "Evaluate Score Threshold",
"type": "n8n-nodes-base.if",
"position": [
995,
560
],
"parameters": {
"options": [],
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "9f5092e4-3bd7-4b2c-8490-770b281c4ce1",
"operator": {
"type": "number",
"operation": "gt"
},
"leftValue": "={{ $json.output.Score }}",
"rightValue": 85
}
]
}
},
"typeVersion": 2.2
},
{
"id": "9b03c662-3314-451b-ae89-b2723915f912",
"name": "Send Improvement Email",
"type": "n8n-nodes-base.gmail",
"position": [
760,
740
],
"webhookId": "a92b2d07-c1af-4581-bfbe-51b12dbe3f54",
"parameters": {
"sendTo": "={{ $item(\"0\").$node[\"Form Submission Trigger\"].json[\"rawRequest\"][\"q5_email\"] }}",
"message": "=<!DOCTYPE html>\n<html>\n<head>\n  <meta charset=\"UTF-8\">\n  <title>Resume Feedback Report</title>\n  <style>\n    body {\n      font-family: 'Segoe UI', Arial, sans-serif;\n      background-color: #f7f9fc;\n      padding: 30px;\n      color: #333;\n      line-height: 1.6;\n    }\n    .container {\n      background-color: #ffffff;\n      border-radius: 10px;\n      padding: 25px 30px;\n      box-shadow: 0 2px 8px rgba(0,0,0,0.1);\n      max-width: 600px;\n      margin: 0 auto;\n    }\n    h2 {\n      color: #0078d7;\n      margin-bottom: 10px;\n    }\n    ul {\n      margin-top: 5px;\n      padding-left: 20px;\n    }\n    .section {\n      margin-top: 25px;\n    }\n    .footer {\n      margin-top: 40px;\n      font-size: 13px;\n      color: #666;\n      text-align: center;\n    }\n    li { margin-bottom: 8px; }\n  </style>\n<link rel="preload" as="image" href="https://flowpast.s3.eu-north-1.amazonaws.com/featured_blog_images/106152.webp"><link rel="preload" as="image" href="https://flowpast.s3.eu-north-1.amazonaws.com/featured_blog_images/106152.webp"></head>
\n<body>\n  <div class=\"container\">\n    <h2>\u2705 Your Resume Feedback Report</h2>\n    <p>Hello,</p>\n    <p>Thank you for submitting your resume. Our AI-powered assistant has analyzed it and here\u2019s your personalized feedback:</p>\n\n    <div class=\"section\">\n      <h3>\ud83c\udf1f Strengths</h3>\n      <ul>\n        {{$json[\"output\"][\"good_side\"]\n           .split(\"\\n\")\n           .filter(s => s.trim() !== '')\n           .map(item => \"<li>\" + item.replace(/^- /,'').trim() + \"</li>\")\n           .join(\"\")}}\n      </ul>\n    </div>\n\n    <div class=\"section\">\n      <h3>\ud83d\udd27 Areas to Improve</h3>\n      <ul>\n        {{$json[\"output\"][\"to_improve\"]\n           .split(\"\\n\")\n           .filter(s => s.trim() !== '')\n           .map(item => \"<li>\" + item.replace(/^- /,'').trim() + \"</li>\")\n           .join(\"\")}}\n      </ul>\n    </div>\n\n    <p>We hope this helps you refine your resume and make it stand out even more!</p>\n\n    <div class=\"footer\">\n      <p>Best regards,<br><strong>The Resume Analyzer Team</strong></p>\n    </div>\n  </div>\n</body>\n</html>\n",
"options": [],
"subject": "Resume Analyzed Results"
},
"credentials": {
"gmailOAuth2": {
"id": "credential-id",
"name": ""
}
},
"typeVersion": 2.1
},
{
"id": "303e1cdf-aae9-43ef-922b-4f6c6712ceb6",
"name": "Send Congrats Email",
"type": "n8n-nodes-base.gmail",
"position": [
1260,
770
],
"webhookId": "a92b2d07-c1af-4581-bfbe-51b12dbe3f54",
"parameters": {
"sendTo": "={{ $item(\"0\").$node[\"Form Submission Trigger\"].json[\"rawRequest\"][\"q5_email\"] }}",
"message": "=<!DOCTYPE html>\n<html>\n<head>\n  <meta charset=\"UTF-8\">\n  <title>Congratulations on Your Resume Review!</title>\n  <style>\n    body {\n      font-family: 'Segoe UI', Arial, sans-serif;\n      background-color: #f7f9fc;\n      padding: 30px;\n      color: #333;\n      line-height: 1.6;\n    }\n    .container {\n      background-color: #ffffff;\n      border-radius: 10px;\n      padding: 25px 30px;\n      box-shadow: 0 2px 8px rgba(0,0,0,0.1);\n      max-width: 600px;\n      margin: 0 auto;\n    }\n    h2 {\n      color: #0078d7;\n      margin-bottom: 10px;\n    }\n    ul {\n      margin-top: 5px;\n      padding-left: 20px;\n    }\n    .section {\n      margin-top: 25px;\n    }\n    .footer {\n      margin-top: 40px;\n      font-size: 13px;\n      color: #666;\n      text-align: center;\n    }\n    li { margin-bottom: 8px; }\n  </style>\n<link rel="preload" as="image" href="https://flowpast.s3.eu-north-1.amazonaws.com/featured_blog_images/106152.webp"><link rel="preload" as="image" href="https://flowpast.s3.eu-north-1.amazonaws.com/featured_blog_images/106152.webp"></head>
\n<body>\n  <div class=\"container\">\n    <h2>\ud83c\udf89 Congratulations!</h2>\n    <p>Hello,</p>\n    <p>We\u2019re excited to inform you that your resume has been successfully reviewed by our AI-powered assistant, and the results are impressive! Great job putting together a strong profile.</p>\n\n    <div class=\"section\">\n      <h3>\ud83c\udf1f Your Strengths</h3>\n      <ul>\n        {{ $item(\"0\").$node[\"Extract Review Details\"].json[\"output\"][\"good_side\"] }}\n      </ul>\n    </div>\n\n    <p>We\u2019re thrilled to see your potential and will be following up soon with the next steps. Keep up the great work!</p>\n\n    <div class=\"footer\">\n      <p>Warm regards,<br><strong>The Resume Analyzer Team</strong></p>\n    </div>\n  </div>\n</body>\n</html>\n",
"options": [],
"subject": "Resume Analyzed Results"
},
"credentials": {
"gmailOAuth2": {
"id": "credential-id",
"name": ""
}
},
"typeVersion": 2.1
}
],
"pinData": [],
"connections": {
"Evaluate Score Threshold": {
"main": [
[
{
"node": "Create Qualified CRM Entry",
"type": "main",
"index": 0
}
],
[
{
"node": "Send Improvement Email",
"type": "main",
"index": 0
}
]
]
},
"Analyze Resume Content": {
"main": [
[
{
"node": "Extract Review Details",
"type": "main",
"index": 0
}
]
]
},
"Merge OCR Text": {
"main": [
[
{
"node": "Analyze Resume Content",
"type": "main",
"index": 0
}
]
]
},
"Form Submission Trigger": {
"main": [
[
{
"node": "Store Submission Record",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "Extract Review Details",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Convert PDF to JPG": {
"main": [
[
{
"node": "Submit Images to OCR",
"type": "main",
"index": 0
}
]
]
},
"Parse OCR Operation ID": {
"main": [
[
{
"node": "Fetch OCR Results",
"type": "main",
"index": 0
}
]
]
},
"Extract Review Details": {
"main": [
[
{
"node": "Cast Score to Number",
"type": "main",
"index": 0
}
]
]
},
"Fetch OCR Results": {
"main": [
[
{
"node": "Check OCR Status",
"type": "main",
"index": 0
}
]
]
},
"Cast Score to Number": {
"main": [
[
{
"node": "Evaluate Score Threshold",
"type": "main",
"index": 0
}
]
]
},
"Check OCR Status": {
"main": [
[
{
"node": "Merge OCR Text",
"type": "main",
"index": 0
}
],
[
{
"node": "Delay for OCR Completion",
"type": "main",
"index": 0
}
]
]
},
"Submit Images to OCR": {
"main": [
[
{
"node": "Parse OCR Operation ID",
"type": "main",
"index": 0
}
]
]
},
"Create Qualified CRM Entry": {
"main": [
[
{
"node": "Send Congrats Email",
"type": "main",
"index": 0
}
]
]
},
"Store Submission Record": {
"main": [
[
{
"node": "Convert PDF to JPG",
"type": "main",
"index": 0
}
]
]
},
"Delay for OCR Completion": {
"main": [
[
{
"node": "Fetch OCR Results",
"type": "main",
"index": 0
}
]
]
}
}
}