> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pylar.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Sales Assistant

> Build an AI agent that analyzes sales pipeline, identifies opportunities, forecasts revenue, and tracks deal progress

## Overview

A Sales Assistant powered by Pylar analyzes sales pipeline data, identifies opportunities, forecasts revenue, and provides insights to help sales teams close more deals faster.

## What the Agent Needs to Accomplish

The agent must:

* Analyze sales pipeline and deal stages
* Identify high-value opportunities
* Forecast revenue based on pipeline data
* Track deal progress and identify stalled deals
* Provide insights on win rates and conversion metrics
* Recommend next actions for deals
* Analyze sales performance by rep, region, or product

## How Pylar Helps

Pylar enables the agent by:

* **Unified Pipeline View**: Combining CRM data, sales activity, and historical performance
* **Real-time Analysis**: Querying current pipeline status and forecasts
* **Multi-Source Integration**: Joining CRM data with product catalog and customer data
* **Pattern Recognition**: Identifying successful deal patterns
* **Actionable Insights**: Generating recommendations based on data

```mermaid theme={null}
graph LR
    subgraph "Data Sources"
        CRM[CRM]
        PRODUCTS[Product Catalog]
        HISTORY[Sales History]
    end
    
    PYLAR[Pylar<br/>Unified Views]
    
    TOOLS[MCP Tools<br/>analyze_pipeline<br/>forecast_revenue<br/>identify_opportunities]
    
    AGENT[Sales Assistant]
    TEAM[Sales Team]
    
    CRM --> PYLAR
    PRODUCTS --> PYLAR
    HISTORY --> PYLAR
    PYLAR --> TOOLS
    TOOLS --> AGENT
    AGENT --> TEAM
    
    style PYLAR fill:#FF4017,stroke:#CC3300,color:#fff
```

## Without Pylar vs With Pylar

### Without Pylar

**Challenges**:

* ❌ Multiple systems (CRM, sales tools, analytics)
* ❌ Complex API integrations for each system
* ❌ Manual pipeline analysis and forecasting
* ❌ Difficult to correlate deals with customer data
* ❌ Time-consuming report generation
* ❌ Limited real-time insights

**Implementation Complexity**:

* 3-4 different API integrations
* Custom pipeline analysis logic
* Manual forecasting calculations
* Complex data correlation
* \~4-5 weeks development time

### With Pylar

**Benefits**:

* ✅ Single endpoint for all sales data
* ✅ Real-time pipeline analysis
* ✅ Automated forecasting
* ✅ Unified customer-deal view
* ✅ Easy to update analysis logic
* ✅ Built-in analytics on sales patterns

**Implementation Complexity**:

* Connect 3-4 data sources (1 hour)
* Create sales pipeline views (2.5 hours)
* Build MCP tools with AI (1.5 hours)
* Connect to agent builder (15 minutes)
* **Total: \~5 hours**

## Step-by-Step Implementation

### Step 1: Connect Data Sources

Connect your sales data sources:

1. **Connect CRM** (Salesforce/HubSpot - deals, opportunities, contacts)
2. **Connect Sales Activity** (Email, calls, meetings tracking)
3. **Connect Product Catalog** (Product pricing, configurations)
4. **Connect Customer Data** (Account information, historical purchases)

### Step 2: Create Sales Pipeline Views

**Pipeline Overview View**:

```sql theme={null}
CREATE VIEW pipeline_overview AS
SELECT 
  d.deal_id,
  d.deal_name,
  d.account_name,
  d.owner_name,
  d.stage,
  d.amount,
  d.probability,
  d.expected_close_date,
  d.created_date,
  DATEDIFF(d.expected_close_date, CURRENT_DATE) as days_to_close,
  -- Weighted pipeline value
  d.amount * (d.probability / 100) as weighted_value,
  -- Deal age
  DATEDIFF(CURRENT_DATE, d.created_date) as deal_age_days,
  -- Activity metrics
  a.email_count,
  a.call_count,
  a.meeting_count,
  a.last_activity_date,
  DATEDIFF(CURRENT_DATE, a.last_activity_date) as days_since_last_activity,
  -- Risk indicators
  CASE 
    WHEN DATEDIFF(CURRENT_DATE, a.last_activity_date) > 14 THEN 1
    ELSE 0
  END as stalled_flag
FROM crm.deals d
LEFT JOIN sales.activity_summary a ON d.deal_id = a.deal_id;
```

**Revenue Forecast View**:

```sql theme={null}
CREATE VIEW revenue_forecast AS
SELECT 
  DATE_TRUNC('month', expected_close_date) as forecast_month,
  stage,
  COUNT(*) as deal_count,
  SUM(amount) as total_pipeline_value,
  SUM(amount * (probability / 100)) as weighted_forecast,
  AVG(probability) as avg_probability,
  SUM(CASE WHEN stage = 'Closed Won' THEN amount ELSE 0 END) as closed_won_value
FROM pipeline_overview
WHERE expected_close_date >= CURRENT_DATE
GROUP BY DATE_TRUNC('month', expected_close_date), stage
ORDER BY forecast_month, stage;
```

**Opportunity Analysis View**:

```sql theme={null}
CREATE VIEW opportunity_analysis AS
SELECT 
  p.*,
  -- Win probability based on stage
  CASE 
    WHEN p.stage = 'Closed Won' THEN 100
    WHEN p.stage = 'Negotiation' THEN 75
    WHEN p.stage = 'Proposal' THEN 50
    WHEN p.stage = 'Qualified' THEN 25
    ELSE 10
  END as stage_based_probability,
  -- Recommendation
  CASE 
    WHEN p.stalled_flag = 1 THEN 'Re-engage customer'
    WHEN p.days_to_close < 7 AND p.probability < 50 THEN 'Increase activity'
    WHEN p.weighted_value > 100000 THEN 'Prioritize - high value'
    ELSE 'Continue normal cadence'
  END as recommended_action
FROM pipeline_overview p;
```

### Step 3: Create MCP Tools with AI

**Tool 1: Get Pipeline Summary**

* Prompt: "Create a tool to get sales pipeline summary by stage, rep, or date range"
* AI generates: `get_pipeline_summary(stage: string, rep_name: string, start_date: date, end_date: date)`

**Tool 2: Forecast Revenue**

* Prompt: "Create a tool to forecast revenue for a specific time period"
* AI generates: `forecast_revenue(months_ahead: number, include_probability: boolean)`

**Tool 3: Identify Opportunities**

* Prompt: "Create a tool to identify high-value opportunities or stalled deals"
* AI generates: `identify_opportunities(min_value: number, stage: string, stalled_only: boolean)`

**Tool 4: Analyze Sales Performance**

* Prompt: "Create a tool to analyze sales performance by rep, region, or product"
* AI generates: `analyze_sales_performance(group_by: string, start_date: date, end_date: date)`

### Step 4: Test and Publish

1. Test pipeline analysis
2. Verify revenue forecasting
3. Validate opportunity identification
4. Publish tools
5. Connect to agent builder

## Example Agent Interactions

### Scenario 1: Pipeline Summary

**User**: "What's our current pipeline status?"

**Agent** (using Pylar tools):

1. Calls `get_pipeline_summary(null, null, null, null)`
2. Responds:
   * "Current Pipeline Status:
     * Total Pipeline Value: \$2.4M
     * Weighted Forecast: \$1.2M
     * Deals by Stage:
       * Qualified: 45 deals (\$800K)
       * Proposal: 28 deals (\$900K)
       * Negotiation: 12 deals (\$500K)
       * Closed Won this month: \$200K"

### Scenario 2: Revenue Forecast

**User**: "What's our revenue forecast for next quarter?"

**Agent** (using Pylar tools):

1. Calls `forecast_revenue(3, true)`
2. Responds:
   * "Q2 Revenue Forecast:
     * Best Case: \$1.8M (all deals close)
     * Weighted Forecast: \$1.2M (probability-adjusted)
     * Committed: \$450K (closed deals)
     * At Risk: \$200K (stalled deals)"

### Scenario 3: Opportunity Identification

**User**: "Show me high-value opportunities that need attention"

**Agent** (using Pylar tools):

1. Calls `identify_opportunities(50000, null, true)`
2. Responds:
   * "High-Value Stalled Opportunities:
     * Enterprise Corp: \$150K, stalled 18 days, recommended: Re-engage
     * TechStartup: \$85K, stalled 12 days, recommended: Schedule meeting
     * ..."

## Outcomes

### Sales Performance

* **Pipeline Visibility**: 100% real-time pipeline visibility
* **Forecast Accuracy**: 25% improvement in forecast accuracy
* **Deal Velocity**: 20% faster deal progression
* **Win Rate**: 15% improvement in win rates

### Efficiency Gains

* **Report Generation**: 90% reduction in report creation time
* **Opportunity Identification**: 3x faster identification of high-value deals
* **Activity Tracking**: Automated tracking of sales activities
* **Team Productivity**: 2x more deals managed per rep

### Data-Driven Decisions

* **Pattern Recognition**: Identification of successful deal patterns
* **Risk Identification**: Early detection of at-risk deals
* **Performance Insights**: Clear visibility into rep and region performance
* **Optimization**: Data-driven improvements to sales process

## Best Practices

1. **Regular Updates**: Keep pipeline data current
2. **Activity Tracking**: Monitor sales activity regularly
3. **Forecast Reviews**: Review forecasts weekly
4. **Stalled Deal Follow-up**: Follow up on stalled deals within 48 hours
5. **Performance Analysis**: Analyze performance trends monthly

## Next Steps

* [Revenue Operations Agent Example](/examples/revenue-operations-agent) - Track revenue operations
* [Lead Qualification Agent Example](/examples/lead-qualification-agent) - Qualify leads automatically
* [Customer Support Agent Example](/examples/customer-support-agent) - Support customers effectively