Introduction

Final output

Here is how the system automatically analyses and updates the deal when a negative sentiment note is added.

The real cost of missed signals

1. Fetches deal details: Name, Stage, Amount, Close date, Owner, Existing risk.
2. Collects all deal notes: Every note linked to the deal.
3. Sends to Zia Assistant: With enhanced prompt and classification guidelines.
4. Extracts AI response: Structured sentiment, Risk score, and AI Analysis.
5. Parses & normalizes: Cleans up AI output to match exact field values.
6. Calculates risk trend: Compares current risk vs previous risk.
7. Updates deal record: Writes all 6 custom fields.
8. Sends email alert: Notifies deal owner if risk > 8.
9. Manages tags: Adds/removes High Risk Deal tag automatically.
   

Prerequisites

Before using the Zia Assistant API inside the Deluge function, make sure that AI is enabled in Zoho CRM.

To enable AI configuration, go to Setup > Zia > Models > Zoho Hosted LLM vendor.

Note: In V8, only the Zoho Hosted LLM vendor can be enabled and used.           

1. CRM Connection: Connections with appropriate scopes.
   

Step 2: Set up Zoho CRM Connection

1. Go to Settings → Developer Space → Connections.
2. Click Create Connection
3. Select your services.
4. Name the connection: zohocrm (used in this post)
5. Add the required scopes
6. Click Create and Connect
7. Authorize the connection
   

Step 3: Create the custom function

1. Go to Settings → Developer Space → Functions
2. Click Create Function
3. Display Name: Deal Notes Sentiment Analysis
4. Function Name: DealNotesSentimentAnalysis
5. Category: Automation
6. Return Type: void
7. Add Argument: dealId: Deals.Deal Id Type: int
   
8. Click Save

Step 4: Configure the Workflow Rule

1. Go to Settings → Automation → Workflow Rules
2. Click Create Rule
3. Module: Deals
4. Rule Name: Deal Notes Sentiment Trigger
5. When: A note is added or modified
6. Condition: All Deals (or customize as needed)
7. Instant Action: Associate the custom function →DealNotesSentimentAnalysis
8. Map the dealId argument to the Deal's Record ID
   
9. Click Save

What happens inside the custom function?

At this point, the workflow is fully set up. Whenever a note is added or modified in a deal, the workflow triggers the custom function. But what exactly happens inside this function?

Instead of going through the code line by line, let’s break it down into logical stages to understand how the system works end-to-end.

The snippets below highlight only the core logic for each step.

Note: The complete function code is provided in the next section for reference. You can use and adapt it based on your use case.

Step 1: Gathering complete deal context

The function begins by collecting key details about the deal:

1.  Deal Name 
2.  Stage 
3.  Amount 
4.  Close Date 
5.  Deal Owner 
6.  Existing Risk Score 

This ensures that the analysis is not done in isolation.

 The AI receives full deal context, which improves the accuracy of sentiment and risk evaluation.

dealData = zoho.crm.getRecordById("Deals", dealIdLong); dealName   = ifnull(dealData.get("Deal_Name"), ""); dealStage  = ifnull(dealData.get("Stage"), ""); dealAmount = ifnull(dealData.get("Amount"), "0"); closeDate  = ifnull(dealData.get("Closing_Date"), "Not defined"); // Existing Risk existingRisk = ifnull(dealData.get("Risk"), "0").toString().toNumber(); // Owner Info owner = dealData.get("Owner"); ownerName  = ifnull(owner.get("name"), ""); ownerEmail = ifnull(owner.get("email"), "");

Step 2: Reading All deal notes

Next, the function fetches all notes associated with the deal.

Instead of analyzing only the latest note, it:

1.  Collects every note.
2.  Combines them into a structured format.

This allows the system to:

1.  Identify repeated concerns.
2.  Detect patterns across conversations.
3.  Understand the overall direction of the deal.
   

dealNotes = zoho.crm.getRelatedRecords("Notes", "Deals", dealIdLong); notesContext = ""; for each note in dealNotes {  notesContext = notesContext + "- " + ifnull(note.get("Note_Content"), "") + "\n"; }

Step 3: Sending data to Zia Assistant

The collected deal data and notes are sent to Zia Assistant using a carefully designed prompt.

The prompt includes:

1.  Definitions of positive, negative, and neutral signals. 
2.  Real-world sales scenarios (pricing concerns, delays, objections).
3.  Clear risk scoring guidelines (1 to 10 scale).

This ensures that the AI response is:

1.  Consistent.
2.  Context-aware.
3.  Aligned with real sales behavior.
   

chatEntry = Map(); chatEntry.put("role", "user"); chatEntry.put("content",  "Deal Name: " + dealName +  "\nStage: " + dealStage +  "\nAmount: " + dealAmount +  "\nClose Date: " + closeDate +  "\n\n=== DEAL NOTES ===\n" + notesContext ); assistantMap.put("chat_history", {chatEntry}); assistantMap.put("prompt", "<custom sentiment + scoring prompt>"); response = invokeurl [  url :"https://www.zohoapis.com/crm/v8/zia/smart_prompt/assistant"  type :POST  parameters: payload.toString()  connection:"zohocrm" ];

This is where AI transforms raw notes into structured insights.

Step 4: Interpreting the AI response

Zia Assistant analyzes the input and returns a structured response containing:

1. Overall Sentiment (Positive / Neutral / Negative) 
2.  Risk Score (1–10) 
3.  AI Analysis (brief reasoning) 

The function extracts these values for further processing.

aiText = ""; if(response.get("assistant") != null) {  aiText = response.get("assistant").get("details").get("data"); }

Step 5: Normalizing the output

Since AI responses can vary slightly in format, the function standardizes the output:

1. Ensures sentiment matches exact CRM field values 
2.  Validates risk score within the 1–10 range 
3.  Cleans up any extra characters or formatting 

This step ensures clean and consistent data inside the CRM.

// Extract & clean risk riskLine = riskLine.replaceAll("[^0-9]", "", false); riskScore = riskLine.toNumber(); if(riskScore > 10) riskScore = 10; if(riskScore < 1)  riskScore = 1; // Normalize sentiment sentimentLower = sentiment.toLowerCase(); if(sentimentLower.contains("negative")) sentiment = "Negative"; else if(sentimentLower.contains("positive")) sentiment = "Positive"; else sentiment = "Neutral";

Step 6: Calculating risk trend

The system then compares:

1. Previous Risk Score 
2.  Current Risk Score 

Based on this, it determines whether the deal is:

1. Increasing in risk
2. Decreasing in risk
3. Stable

This adds an important layer of intelligence and not just what the risk is, but how it is changing over time.

riskTrend = "Stable"; if(riskScore > existingRisk) {     riskTrend = "Increasing"; } else if(riskScore < existingRisk) {     riskTrend = "Decreasing"; }

Step 7: Updating the deal record

Once all values are processed, the function updates the deal with:

1. Risk 
2.  Sentiment 
3.  AI Analysis 
4.  Previous Risk 
5.  Risk Trend 
6.  Alert Sent Flag 

At this stage, the deal record becomes a live reflection of customer sentiment.

updateMap = Map(); updateMap.put("Risk", riskScore.toString()); updateMap.put("Sentiment", sentiment); updateMap.put("AI_Analysis", analysis); updateMap.put("Previous_Risk", existingRisk.toString()); updateMap.put("Risk_Trend", riskTrend); zoho.crm.updateRecord("Deals", dealIdLong, updateMap);

Step 8: Smart email alerting

If the risk score crosses a threshold (Risk > 8), the system triggers an alert to the record owner to take immediate action.

An email is sent to the Deal Owner with:

1. Deal details 
2. Risk score 
3. Sentiment 
4. Risk trend 
5. AI analysis 

To avoid alert:

1. The system checks whether an alert has already been sent. 
2.  A new alert is triggered only if the risk increases further.
   

previousAlertSent = ifnull(dealData.get("Alert_Sent"), false); if(riskScore > 8 && (previousAlertSent == false || riskScore > existingRisk) && ownerEmail != "") {     // Send alert email     mailPayload = {         "from": {"user_name": ownerName, "email": zoho.crm.getOrgVariable("defaultMail")},         "to": {{"user_name": ownerName, "email": ownerEmail}},         "subject": "🚨 Deal At Risk: " + dealName,         "content": "Risk Score: " + riskScore + "/10<br>Sentiment: " + sentiment + "<br><br>" + analysis,         "mail_format": "html"     };     invokeurl     [         url :"https://www.zohoapis.com/crm/v8/Deals/" + dealIdLong + "/actions/send_mail"         type :POST         parameters: {"data": {mailPayload}}.toString()         headers: {"Content-Type":"application/json"}         connection: "zohocrm"     ];     zoho.crm.updateRecord("Deals", dealIdLong, {"Alert_Sent": true}); } else if(riskScore <= 8) {     // Reset alert flag     zoho.crm.updateRecord("Deals", dealIdLong, {"Alert_Sent": false}); }

Step 9: Dynamic tag management

Finally, the system visually marks high-risk deals:

1.  If Risk > 8 → Adds tag “High Risk Deal”
2.  If Risk reduces → Removes the tag automatically 

This makes it easy for users to:

1.  Identify risky deals instantly 
2.  Prioritize follow-ups 
   

if(riskScore > 8) {  invokeurl  [  url :"https://www.zohoapis.com/crm/v8/Deals/" + dealIdLong + "/actions/add_tags"  type :POST  parameters: tagPayload.toString()  connection:"zohocrm"  ]; } else {  invokeurl  [  url :"https://www.zohoapis.com/crm/v8/Deals/" + dealIdLong + "/actions/remove_tags"  type :POST  parameters: tagPayload.toString()  connection:"zohocrm"  ];

Complete custom function code

The complete Deluge script used in this implementation is provided below for reference and direct use:

Notes

1. To get better sentiment detection, use a clear and strong prompt in the Zia Assistant API.
2. The output depends on how well the prompt defines positive, negative, and neutral signals.
3. Zia looks at the overall trend of notes, not just one note.
4. If a deal had negative notes earlier but recent notes are positive, the risk (Risk field ) will decrease
5. If new negative notes are added, the risk will increase.
6. Sometimes Zia Assistant may not detect sentiment perfectly, as it is AI-based
   

Conclusion

This automation transforms deal notes from passive text into actionable intelligence. Instead of relying on sales managers to read hundreds of notes manually, Zia Assistant does it automatically detecting sentiment, scoring risk, tracking trends, alerting owners, and tagging high-risk deals all in real time.

We trust that this post meets your needs and is helpful. Let us know your thoughts in the comment section or reach out to us at support@zohocrm.com

Stay tuned for more insights in our upcoming Kaizen posts!

Happy coding!!!

Previous Kaizen: Kaizen #239 - Audit Log Monitoring with Proactive Alerts

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