> ## Documentation Index > Fetch the complete documentation index at: https://docs.momentum.io/llms.txt > Use this file to discover all available pages before exploring further. # Setting Up Autopilot Classic > A guide to configuring the classic, real-time Autopilot to extract and update Salesforce data from your sales calls. **You are here.** Triggers after each call/email to analyze its content in real-time. Triggers on a Salesforce event and analyzes all historical conversations. Triggers manually to run on a large set of records that match your defined criteria. # Setting Up Autopilot Classic This guide provides step-by-step instructions for configuring Momentum's classic Autopilot feature to automatically extract and update Salesforce data from your sales calls. ## How Autopilot Works Autopilot triggers after a single call or email, analyzes its content, and updates a Salesforce field. ```mermaid theme={null} graph LR A(Call or Email) --> B{Autopilot Trigger}; B --> C((Analyze Content)); C --> D[/Run AI Prompt/]; D --> E>Update Field]; ``` ## Prerequisites Before configuring Autopilot, ensure you have: * [Momentum AI installed with your preferred call provider](/how-to-install-momentum) * Admin access to your Momentum Organization * Salesforce integration active * AI license seats assigned to team members ## Step-by-Step Configuration ### Step 1: Access the Autopilot Section 1. Log in to your [Momentum admin dashboard](https://app.momentum.io/admin/autopilot-extractions) 2. Navigate to the **Autopilot** section in the left sidebar 3. Click **"New Autopilot Extraction"** to begin the setup process ### Step 2: Select Salesforce Object and Field **Choose the Target Object:** * **Opportunity**: Extract data to Opportunity fields * **Account**: Extract data to Account fields * **Lead**: Extract data to Lead fields * **Event**: Extract data to Event fields * **Contact**: Extract data to Contact fields. Momentum monitors self-referential statements from call participants and uses that context to inform CRM field extraction for each individual Contact. * **Custom Objects**: Extract to other object fields by navigating from one of the default objects above **Select the Target Field:** * **Standard Fields**: Choose from standard Salesforce fields * **Custom Fields**: Select custom fields you've created * **Picklist Fields**: Automatically limit outputs for fields with predefined values * **Text Fields**: Automatically limit outputs for free-form text fields and their max length and formatting * **Number Fields**: Automatically limit outputs for numeric data * **Boolean Fields**: Automatically limit outputs for yes/no answers * **Lookup Fields**: For lookup fields pointing to Contacts, Autopilot can suggest one of the external attendees from the call as the value (e.g., for "Primary Contact" or "Decision Maker" fields) **Example Configuration:** ```yaml theme={null} Object: Opportunity Field: Pain_Points__c (Custom Text Field) ``` ### Step 3: Write Your AI Prompt The AI prompt is the instruction that tells Momentum what data to extract from calls. **Prompt Best Practices:** * **Be Specific**: Clearly define what information you want extracted * **Use Questions**: Frame prompts as questions the AI should answer * **Include Context**: Reference the field name and expected format * **Set Boundaries**: Define what should and shouldn't be included **Example Prompts:** **For Pain Points:** ```text theme={null} "What are the main pain points or challenges the customer mentioned during this call? Focus on business problems they're trying to solve. Return a concise list of 2-3 key pain points." ``` **For Budget Information:** ```text theme={null} "What is the customer's budget range or budget constraints mentioned in this call? Include specific amounts, ranges, or budget-related discussions. If no budget information is discussed, return 'Not discussed'." ``` **For Timeline:** ```text theme={null} "What is the customer's timeline or urgency for implementing a solution? Include specific dates, quarters, or urgency indicators mentioned. If no timeline is discussed, return 'Not discussed'." ``` **For Competitive Information:** ```text theme={null} "Which competitors did the customer mention during this call? List specific competitor names and any context about their current solutions or evaluation status." ``` ### Step 4: Configure Conditions Set conditions to determine when this extraction should run. You can use Salesforce field conditions, Smart Tag conditions, or both. #### Salesforce Field Conditions Filter extractions based on CRM data like opportunity stage, account attributes, or custom fields. **Example Conditions:** ```yaml theme={null} Opportunity Stage: Discovery, Qualification Account ARR: > $50,000 ``` #### Smart Tag Conditions (Optional) In addition to Salesforce field conditions, you can use [Smart Tags](/smart-tags) to trigger extractions based on what was actually said during the call. This ensures your automations only run when specific topics, keywords, or themes are detected. **Example Smart Tag Conditions:** ```yaml theme={null} Smart Tag: Competitor Mentions = "Competitor X" Smart Tag: Pricing Objection = True Smart Tag: Feature Request contains "integration" ``` **Use Cases:** * **Competitive intelligence**: Only run a competitive analysis extraction when a specific competitor is mentioned * **Objection handling**: Trigger follow-up workflows when pricing or security objections are detected * **Feature requests**: Route product feedback extractions only when feature requests are identified Smart Tag conditions evaluate the tags detected on the current call. Make sure your [Smart Tags are configured](/smart-tags#setting-up-a-smart-tag) in **Admin > Smart Tags** before using them as Autopilot conditions. ### Step 5: Configure Save Behavior Choose how the extracted data should be saved to Salesforce. **Save Options:** * **Confirm to Write**: Show extraction in Slack or web app for manual review before saving * **Write if Empty**: Only save if the field is currently empty, confirm to write otherwise * **Automatic Write**: Automatically save all extractions if new data is identified **Balancing Automation and Verification** While manual verification may seem like the safer approach, our extensive field experience has shown that requiring rep engagement often becomes the biggest roadblock to maintaining high-quality, consistent data. Sales reps are less likely to consistently review and approve extractions, which leads to gaps in data collection and outdated information. Instead, we strongly recommend pushing towards "Write If Empty" for most extractions after your initial testing period. This approach strikes the optimal balance between maintaining data integrity and maximizing the benefits of automation. For best results, we suggest a simple progression: begin with "Confirm to Write" during the first 1-2 weeks while you validate the extraction quality. Once you're confident in the results, transition to "Write If Empty" as your standard operating mode. For highly reliable extractions where the data changes frequently, you may eventually consider using "Automatic Write." Remember, focusing your energy on crafting precise, well-defined prompts will typically yield better results than relying on manual verification processes. Autopilot automatically truncates extracted values to fit the target field's maximum length in Salesforce. ### Step 6: Test Your Extraction 1. **Preview Your Extraction**: Click the "Preview" button before creating to test your prompt against past calls 2. **Review Sample Results**: Examine how your prompt performs on historical call data without affecting Salesforce or Slack 3. **Refine the Prompt**: Adjust the AI prompt based on preview results 4. **Create Live Extraction**: Once satisfied with preview results, create the extraction for production use Autopilot Configuration Interface ## Advanced Configuration ### Multiple Extractions for the Same Field You can create multiple extractions for the same field with different conditions: **Discovery Extraction:** * **Field**: Pain\_Points\_\_c * **Conditions**: Opportunity Stage = "Discovery" * **Prompt**: Focus on initial pain point discovery **Qualification Extraction:** * **Field**: Pain\_Points\_\_c * **Conditions**: Opportunity Stage = "Qualification" * **Prompt**: Focus on validated and prioritized pain points ### Include Historical Context with Lookback Autopilot Classic normally references only the incoming call, but Lookback gives the AI access to historical interactions so it can factor in prior conversations. **Configuration Modes:** * **Range Period**: Define a timeframe (e.g., last week, last 90 days, last quarter). Momentum pulls in relevant calls, emails, and texts from that window. * **Event Quantity**: Specify an exact number of recent interactions to include (e.g., last 10 touchpoints) regardless of when they happened. **Source of Data:** * Choose any combination of calls, emails, or SMS/text messages. **Example Lookback Configuration:** Example Lookback Configuration With the above configuration, when a new call triggers the extraction Momentum also considers relevant [historical conversational data](/conversational-data-sources) from the last quarter. Lookback is currently not configurable with Contact field extractions. ### Context-Aware Extractions Configure extractions that adapt based on call context: **Stage-Based Prompts:** * Use different prompts for different opportunity stages * Adjust extraction sensitivity based on deal maturity * Include stage-specific context in prompts **Account-Based Rules:** * Different extractions for enterprise vs. SMB accounts * Industry-specific prompts and conditions * ARR-based extraction rules ## Best Practices ### Prompt Engineering 1. **Start Simple**: Begin with basic prompts and add complexity 2. **Test Iteratively**: Refine prompts based on actual call data 3. **Use Examples**: Include examples in prompts when possible 4. **Be Specific**: Avoid vague language that could lead to inconsistent results 5. **Consider Context**: Account for different call types and stages ### Field Selection 1. **Prioritize High-Value Fields**: Focus on fields that impact forecasting and reporting 2. **Consider Data Quality**: Choose fields where manual entry is error-prone 3. **Start with Text Fields**: Text fields are easier to configure than picklists 4. **Validate Picklist Mappings**: Thoroughly test picklist field extractions ## Troubleshooting ### Common Issues **Extractions Not Running:** * Check Salesforce conditions are correctly configured * Verify calls are being recorded and processed * Ensure AI license seats are properly assigned **Incorrect Data Extraction:** * Review and refine AI prompts * Check that the right calls are being processed * Validate field mappings and conditions **Poor Data Quality:** * Improve prompt specificity and clarity * Add examples to prompts for better guidance * Consider using "Confirm to Write" for manual review * Review extraction logs for patterns * Refine prompts based on error analysis * Consider breaking complex extractions into simpler ones ## Support If you encounter issues during configuration, contact our support team: * **Slack**: Use your shared Momentum channel * **Email**: [support@momentum.io](mailto:support@momentum.io)