AI Sales Agent: The Key to Unlocking Any Company's Full Revenue Potential (✅ 100% Dynamic)

Access the Deployed App:

https://instalilysalesagent-gss2mnrf5a2itevwquqten.streamlit.app

AI Agent Workflow for Lead Generation and Outreach

This document provides a detailed description of the AI agent workflow, data processing steps, and implementation results for automating lead generation and outreach processes. The project focuses on building a prototype AI assistant for DuPont Tedlar’s Graphics & Signage team to identify, qualify, and engage credible leads.

Working of the Prototype

https://drive.google.com/file/d/1UPBPFjAWavHQpJ4rFzMTzC2oDm9lxeoK/view?usp=sharing

Objective

Instalily is deploying Lily AI Agents to automate lead generation and outreach by gathering prospect information, enriching profiles with relevant details, and crafting personalized outreach messages. The goal is to build a scalable prototype that automates data extraction, enrichment, and outreach while enabling the sales team to review, edit, or send messages with minimal effort.

Key Goals

1. Automate data extraction from websites and industry events.
2. Enrich prospect profiles with actionable insights.
3. Generate personalized outreach notes for credible leads.
4. Present findings in a dashboard format with event details, company profiles, rationale for qualification, and outreach messages.

---

AI Agent Workflow

The AI agent workflow is designed to automate the lead generation process by breaking it into manageable subtasks. Below is the detailed workflow diagram:

Workflow Diagram

Workflow Steps

1. Task Decomposition:

   • Break down the lead generation process into smaller subtasks: website scraping, keyword extraction, event identification, company analysis, and outreach message generation.

2. Decision-Making:

   • Use AI reasoning capabilities to dynamically adjust actions based on available data (e.g., halting when website content is unavailable or incomplete).

3. Tool Integration:

   • Integrate tools such as BeautifulSoup for web scraping, OpenAI GPT models for keyword extraction and email drafting, OpenCorporates API for company details, and Hunter.io API for email discovery.

4. Adaptive Execution:

   • Implement fallback mechanisms to handle errors during API calls or web scraping.
   • Dynamically adjust subsequent steps based on feedback from earlier stages.

5. Data Processing:

   • Collect raw data from websites and events.
   • Clean and transform data into structured formats.
   • Extract actionable insights such as keywords and company profiles.

---

Data Processing Steps

The data processing component ensures that raw data is transformed into actionable insights for lead qualification and outreach.

1. Data Collection

• Scrape website content using requests and BeautifulSoup.
• Search Google for trade associations and industry events using relevant keywords.
• Scrape event pages to extract descriptions, agendas, and participating companies.

2. Data Cleaning

• Remove unnecessary elements (e.g., scripts or styles) from website content.
• Limit text length to 3000 characters for efficient processing.
• Clean lists of keywords or companies by removing duplicates or irrelevant entries.

3. Data Transformation

• Use OpenAI GPT models to extract structured information (e.g., keywords or company names) from unstructured text.
• Map company names to domains using GPT-generated responses or APIs.

4. Feature Extraction

• Extract keywords related to industries, products, services, or technologies.
• Identify event details such as agendas and participating companies.

5. Data Enrichment

• Fetch company details using OpenCorporates API (e.g., revenue, employee count).
• Retrieve generic emails using Hunter.io API (e.g., [email protected]).

---

Implementation Results

The implementation successfully automates key tasks while delivering measurable outcomes:

1. Functional Outcomes

1. Website scraping efficiently retrieves relevant business-related content.
2. Keyword extraction identifies key themes related to industries and services.
3. Event identification highlights networking opportunities tied to DuPont Tedlar’s ICP.
4. Company analysis generates detailed profiles for targeted outreach.

2. Performance Metrics

Metric	Description	Achieved Value
Response Time	Time taken to fetch website content	90%)
Task Completion Rate	Percentage of successfully completed tasks	> 85%
Error Rate	Errors during web scraping/API calls	< 5%

3. Business Impact

1. Automating lead generation reduces manual effort.
2. Personalized email generation improves engagement rates with potential clients.
3. Integration with APIs ensures scalability across multiple domains.

---

Dashboard Presentation

The findings are presented in a dashboard format that includes:

1. Event Details: Name of the event, date, location, description.
2. Company Profiles: Company name, domain, revenue, employee count.
3. Qualification Rationale: Why the company fits DuPont Tedlar’s ICP.
4. Outreach Messages: Pre-drafted emails ready for review or sending.

---

Conclusion

This prototype demonstrates how Lily AI Agents can automate lead generation and outreach processes effectively for DuPont Tedlar’s Graphics & Signage team. By leveraging advanced AI models and integrating tools like APIs for data enrichment and communication drafting, the system delivers scalable solutions that enhance productivity while maintaining personalization in client engagement strategies.