How Chase Built and Scaled Vertical AI Agents to $50k/mo (Full Demo)

Key Concepts

AI agents, voice AI, CRM integration, sales automation, lead generation, niching down, scaling, pricing models, performance-based pricing, agent development, legality of AI, competition with funded startups, growth partner model, build and release model, iterative process, focus on revenue generation.

1. Overview of Chase's Vertical AI Agent Solution

• Main Function: Automates the entire sales process from lead acquisition to customer retention.
• Key Features: Speed to lead, follow-up, lead nurture, database reactivation, cross-sell, upsell, downsell, no-call no-show management, full CRM, AI voice, AI texting, pipelines, and sequences.
• Core Idea: Combines AI voice technology with CRM to create "AI employees" capable of handling various sales tasks.

2. Origin and Development of the Solution

• Chase's Background: Extensive experience in sales, including solar sales and door-to-door sales (Omaha Steaks).
• Problem Identification: Bottleneck in scaling sales teams due to human limitations in making calls and managing CRM.
• Voice AI as a Solution: Voice AI offered scalability and efficiency in automating sales processes.
• Initial MVP: Built on Air AI (third-party platform), but later transitioned to building proprietary solutions.
• Voice AI Agnostic: Uses multiple platforms, including two proprietary ones, to leverage the best features of each.

3. Building the MVP and Integrating CRM

• Initial Approach: Integrated a third-party voice AI platform with a CRM.
• Deep Automations: Built custom automations within the CRM to trigger events based on AI agent interactions (e.g., updating lead status, sending emails).
• AI Employee Concept: The combination of voice AI and CRM functionalities creates an "AI employee" capable of handling diverse tasks.
• Example: An AI agent can make a call, transcribe the conversation, update the CRM, and trigger subsequent actions based on the call's outcome.

4. Lead Generation Strategies

• Initial Client Acquisition: Manifested first clients through a cold caller who was impressed by the AI agent demo.
• Early Strategies: Partnerships and referrals.
• Current Effective Strategy: Targeted ads, particularly in the loan space.
• Niching Down: Focusing on specific industries (e.g., loan space) to target ads effectively and solve specific problems.
• Ads vs. Content: While content marketing (YouTube, podcasts) generates high-quality traffic, ads provide more targeted and controllable lead generation.
• Combining Ads and Content: Targeting people who watch organic content on YouTube to boost paid ad performance.

5. Scaling the Solution

• Team Expansion: Continuously expanding the team to handle increasing workload.
• Process Documentation: Documenting all processes in SOPs (Standard Operating Procedures) for consistent execution.
• Continuous Improvement: Aiming for 1% daily improvement in customer experience and client fulfillment.

6. Pricing Models

• Experimentation: Iterated through several pricing models.
• Early Model: $20,000 paid in full for six months.
• Current Model: Build fee ($5,000 - $10,000) + monthly fee ($1,500 - $2,000).
• Lower Tier (Starter Package): $650/month for a single agent with basic CRM and workflows.
• Higher Tier: More AI employees, more automations, and larger workflows.
• Done-For-You Package: $7,500 down + $7,500 - $10,000/month, becoming an extension of the client's sales team.
• Result-Based Pricing: Monthly fee + 10-20% revenue split, with performance-based criteria for both parties.

7. Comprehensive Demo of the AI Solution

• Client View: Access to an inbox, call center, and agent builder.
• Inbox: Displays communications (texts, emails) and integrates with the CRM.
• Call Center: Monitors calls and provides access to call recordings.
• Agent Builder:
  • GPT-4.0 based prompts for agent behavior.
  • Voice selection and testing.
  • Calendar integration for appointment booking.
  • Tool selection (e.g., updating CRM, searching the web, booking appointments, sending emails, transferring calls).
  • Custom tool creation using APIs (e.g., integrating with invoicing systems).
• CRM Integration: Seamless integration with the CRM, displaying all relevant client information.
• Active Tags: Using active tags in the CRM to activate agents.
• Example Client Process:
  • Lead comes in from an ad.
  • Instant follow-up with calls, texts, and emails.
  • AI agent books an appointment.
  • Appointment reminders and context injection (AI provides relevant information before the meeting).

8. Legality of AI Agents

• Legal Status: AI voice is legal as long as it complies with regulations for human voice (opt-in data, permission).
• Transparency: Some areas require stating that the agent is AI.

9. Competition with Funded Startups (e.g., 11X)

• Chase's Perspective: Focus on end-to-end solutions and client results, rather than just one aspect of the sales process.
• Bootstrapped Advantage: Solving real problems encountered firsthand, leading to more effective solutions.
• Iterative Development: Continuously refining the AI agents based on real client feedback.

10. Strategy for 2025

• Growth Partner Model: Two models:
  • Build and Stay: Ongoing management and optimization of the AI solution.
  • Build and Release: Building the infrastructure and handing it over to the client.
• Performance-Based Pricing: Focus on large monthly retainers and performance-based commissions.
• Downsell Strategy: Filtering down leads to a less hands-on team.
• Multiple Iterations: Reseller model, build-and-release clients, and growth partner clients.

11. Notable Quotes

• "Every lead needs a home all the time."
• "Sales tech built by salespeople."
• "Focus on the 20% that's going to move the needle 80%."
• "Just because you can doesn't mean you should."

12. Technical Terms and Concepts

• CRM: Customer Relationship Management - A system for managing interactions with current and potential customers.
• AI Agent: An artificial intelligence program designed to perform specific tasks, such as sales or customer service.
• Voice AI: Artificial intelligence that can generate human-like speech.
• MVP: Minimum Viable Product - A version of a product with just enough features to satisfy early customers and provide feedback for future product development.
• API: Application Programming Interface - A set of rules and specifications that software programs can follow to communicate with each other.
• SOP: Standard Operating Procedure - A set of step-by-step instructions compiled by an organization to help workers carry out complex routine operations.
• GPT-4.0: Generative Pre-trained Transformer 4 - A state-of-the-art language model developed by OpenAI.
• Context Injection: Providing relevant information to the AI agent before a meeting or interaction.
• NAND: (In this context) A custom tool or application.
• Webhook: Automated HTTP requests triggered by events in a system.

13. Synthesis/Conclusion

Chase's success with AI agents stems from a deep understanding of sales processes, a focus on solving real-world problems, and a commitment to continuous improvement. By integrating voice AI with CRM and offering flexible pricing models, Chase has created a powerful solution that automates sales tasks and drives revenue growth for clients. The shift towards performance-based pricing and a growth partner model positions the company for continued success in the evolving AI landscape. The key takeaways are the importance of niching down, focusing on revenue generation, and iteratively developing AI agents based on client feedback.