AI-Powered Sales Intelligence Platform

Executive Summary

Rich Site Lead Generation partnered with our development team to build a cutting-edge, AI-powered sales intelligence platform designed to revolutionize their B2B lead generation and qualification processes. The resulting SalesIntellix platform leverages advanced machine learning, natural language processing, and multi-source data aggregation to identify, analyze, and score potential customers with unprecedented accuracy and efficiency.

The platform transforms a traditionally manual, time-intensive process into an automated, intelligent workflow that delivers actionable sales intelligence in minutes rather than days.

Client Overview

Company: Rich Site Lead Generation
Industry: B2B Sales Intelligence & Lead Generation Services
Challenge: Streamline and automate the identification, research, and qualification of potential B2B customers across diverse industry sectors

Business Challenge

Rich Site Lead Generation faced several critical challenges in their traditional lead generation process:

Manual Research Bottleneck

• Sales analysts spent 60-70% of their time manually researching companies across multiple data sources
• Inconsistent research quality due to human variability and time constraints
• Limited ability to scale lead generation efforts without proportional headcount increases

Data Fragmentation

• Customer intelligence scattered across ZoomInfo, news sources, web searches, and business directories
• No unified view of potential customers and their buying signals
• Difficulty correlating market events with purchase intent

Qualification Inconsistency

• Subjective lead scoring based on individual analyst judgment
• Lack of standardized criteria for evaluating purchase likelihood
• Missing timeline predictions for optimal outreach timing

Sales Enablement Gaps

• Generic sales pitches not tailored to specific company situations
• Limited understanding of decision-maker landscape within target organizations
• Reactive rather than proactive approach to market opportunities

Solution: SalesIntellix Platform

High-Level Technical Architecture

Technical Scope

1. Cloud Infrastructure (AWS CDK)

The platform is built on a modern, serverless AWS infrastructure using the AWS Cloud Development Kit (CDK) for Infrastructure as Code:

Infrastructure Stack Organization

├── CoreResourcesStack
│   ├── VPC & Networking
│   ├── ECS Cluster
│   ├── Cognito User Pool & Client
│   ├── ECR Repositories
│   └── API Key Secrets
│
├── DataStorageStack
│   ├── DynamoDB Tables (company-info, customer-search-tasks)
│   ├── S3 Data Bucket
│   ├── S3 Vector Bucket
│   ├── Athena Results Bucket
│   └── Athena Workgroup
│
├── DataAggregationStack
│   ├── Scheduled Fargate Task (hourly data aggregation)
│   └── Bedrock & DynamoDB Permissions
│
└── CustomerSearchUIStack
    ├── Fargate Service (Flask App)
    ├── Application Load Balancer
    ├── IAM Roles & Policies
    └── Environment Configuration

2. AI/ML Architecture

Foundation Model Integration

The platform utilizes Amazon Bedrock with the Claude Sonnet 4.5 model for advanced reasoning and analysis:

• Model ID: us.anthropic.claude-sonnet-4-5-20250929-v1:0
• Configuration: Extended timeout (10-minute read, 1-minute connect) for complex analysis tasks
• Integration: Strands Agent Framework for autonomous tool orchestration

AI Agent Capabilities

The intelligent agent operates with a sophisticated system prompt that enables:

1. Company Identification - Natural language understanding of target sector criteria
2. Multi-Source Intelligence Gathering - Autonomous coordination of 4+ external data tools
3. Structured Analysis Generation - Pydantic-validated output schemas
4. Scoring & Recommendations - Quantified likelihood assessments (1-10 scale)

Vector Embeddings

• Embedding Model: Amazon Titan Embed Text v2
• Dimension: 1024
• Distance Metric: Cosine similarity
• Use Case: Semantic search across historical analyses

3. Data Integration Layer

External Data Sources

Custom Agent Tools

@tool - find_company_news_articles(company_name)
@tool - get_company_information(company_name)  
@tool - scrape_yellow_pages_listings(query_description, location)
@tool - brave_web_search(query, num_results)

4. Web Application

Technology Stack

• Framework: Flask (Python)
• Authentication: AWS Cognito with JWT validation
• Templates: Jinja2 with Bootstrap
• Styling: Custom dark theme with responsive design

Key Features

5. Security Architecture

Authentication Flow

User → Login Page → Cognito Auth → JWT Token → Session Storage → Protected Routes

Security Controls

• User Pool: Admin-managed user creation (no self-signup)
• Password Policy: 8+ characters, uppercase, lowercase, digits, symbols
• MFA: Optional TOTP/SMS multi-factor authentication
• Secrets Management: API keys stored in AWS Secrets Manager
• Network Isolation: Private subnets with NAT egress only
• Transport Security: HTTPS with ACM certificates (optional)

Data Flow & Processing

Search Workflow

1. User Input
   ├── Target Sector Description
   ├── Product/Service Being Sold
   ├── Business Size Filter
   ├── Location Filter
   ├── Revenue Range
   └── Number of Results (1-10)

2. AI Agent Processing
   ├── Parse natural language criteria
   ├── Execute Brave web search for company discovery
   ├── For each company:
   │   ├── Fetch ZoomInfo company data
   │   ├── Retrieve recent news articles
   │   └── Perform supplementary web searches
   └── Aggregate and analyze all gathered data

3. Analysis Generation
   ├── Interest Score (1-10)
   ├── Reasons FOR buying
   ├── Reasons AGAINST buying
   ├── General analysis summary
   ├── Timeline predictions
   ├── Talking points for sales calls
   └── Source article references

4. Output & Storage
   ├── Display interactive results
   ├── Store task in DynamoDB
   ├── Vectorize and store in S3 Vectors
   └── Enable export and sharing

Output Deliverables

Company Analysis Report Structure

{
  "report_id": "uuid",
  "generated_at": "ISO-8601 timestamp",
  "search_parameters": {...},
  "companies_analyzed": [
    {
      "company": {
        "name": "Company Name",
        "zoominfo_id": "...",
        "website": "...",
        "industry": "...",
        "employee_count": 500,
        "revenue": 50000000,
        "city": "...",
        "state": "...",
        "country": "..."
      },
      "interest_score": 8,
      "score_category": "High",
      "reasons_for_buying": ["...", "...", "..."],
      "reasons_against_buying": ["...", "..."],
      "general_analysis": "Multi-paragraph analysis...",
      "timeline_predictions": [
        {
          "predicted_date": "MM/DD/YYYY",
          "event": "...",
          "relevance": "..."
        }
      ],
      "news_articles": [
        {
          "title": "...",
          "url": "...",
          "source": "...",
          "published_date": "...",
          "summary": "..."
        }
      ],
      "talking_points": [
        {
          "topic": "...",
          "point": "...",
          "source": "..."
        }
      ]
    }
  ]
}

Business Impact

Quantitative Benefits

Qualitative Benefits

For Sales Teams

• Personalized Outreach: AI-generated talking points tailored to each company's situation
• Timing Intelligence: Timeline predictions for optimal engagement windows
• Competitive Insight: Understanding of barriers and objections before first contact
• Confidence in Prioritization: Data-driven scoring removes guesswork

For Sales Management

• Scalable Operations: Process more leads without proportional headcount increase
• Consistent Quality: Standardized analysis methodology across all leads
• Pipeline Visibility: Quantified scoring enables better forecasting
• Performance Analytics: Historical data enables process optimization

For the Business

• Faster Time-to-Revenue: Accelerated lead qualification shortens sales cycles
• Improved Win Rates: Better-qualified leads with tailored approaches
• Reduced Research Costs: Automation of previously manual research tasks
• Competitive Advantage: Access to comprehensive intelligence at scale

Strategic Value

1. Market Responsiveness: Real-time news integration enables rapid response to market events and buying signals

2. Knowledge Retention: Vectorized analysis history creates institutional memory that persists beyond individual employees

3. Continuous Improvement: Structured data collection enables AI model refinement and scoring calibration over time

4. Platform Extensibility: Modular architecture supports addition of new data sources and analysis capabilities

Technology Highlights

Innovation Areas

Modern Development Practices

• Containerization: Docker-based deployment for consistency across environments
• CI/CD Ready: Scripted build and deploy processes (build-and-push.sh, deploy.sh)
• Secret Management: No hardcoded credentials; all secrets in AWS Secrets Manager
• Observability: CloudWatch logging for debugging and monitoring
• Modular Design: Separated stacks enable independent scaling and updates

Future Roadmap Considerations

The platform architecture supports several potential enhancements:

1. CRM Integration - Direct synchronization with Salesforce, HubSpot, or other CRM platforms
2. Email Automation - AI-generated personalized email sequences based on analysis
3. Decision Maker Mapping - Enhanced contact identification and org chart visualization
4. Predictive Scoring - ML models trained on historical win/loss data
5. Real-time Alerts - Notifications when tracked companies have significant news events
6. Multi-tenant Support - White-label deployment for Rich Site Lead Generation's clients

Conclusion

The SalesIntellix platform represents a transformational leap in B2B sales intelligence capabilities for Rich Site Lead Generation. By combining advanced AI reasoning with comprehensive multi-source data aggregation, the platform delivers actionable insights that were previously impossible to generate at scale.

The cloud-native architecture ensures reliability, security, and scalability, while the modern development practices enable rapid iteration and feature expansion. Most importantly, the platform directly addresses the core business challenges of manual research bottlenecks, data fragmentation, and qualification inconsistency—delivering measurable ROI through dramatically improved efficiency and effectiveness.

Document Version: 1.0
Last Updated: January 2026
Classification: Client Case Study