Revolutionizing a lead-generation platform with AI

A rapidly growing lead-generation platform was grappling with a significant problem: a high rate of mismatched leads. These mismatches—where a lead's selected category did not align with the category actually required to solve his problem—were causing both inefficiencies and dissatisfaction.

The client approached us to design a more robust system that could intelligently classify potential leads into the correct categories, reduce the incidence of mismatches, and facilitate a seamless lead intake process. In addition, they wanted the flexibility to experiment with evolving AI models, prompt updates and being able to easily serve new categories without disrupting their core lead flow.

Challenges

1. High mismatch rate: Clients were frequently receiving leads outside their department, leading to wasted time and lower platform satisfaction.
2. Complex classification needs: Industry issues vary widely, and an accurate classification system needed to consider nuanced language, local regulations, and specialized terminology.
3. Limited experimentation pipeline: The existing system did not allow easy testing of new AI models or iterative updates to prompts.
4. Rigid lead submission methods: Traditional online forms were cumbersome, and there was no infrastructure for alternative submission channels such as voice-based phone calls.

Our approach

Designing a flexible classification system

We built a system that uses AI to analyze user inputs (descriptions of the issues) and automatically categorize them into specific categories. To further enhance accuracy, the system also leverages a library of past opportunities using vector search and RAG; similar opportunities identified through this method are integrated into the classification prompt, refining the AI's understanding and boosting classification precision.

Parallel model experimentation

We developed a pipeline that runs multiple models in parallel, allowing for real-time comparison of accuracy, speed, and overall effectiveness. This enables quick iteration and adoption of improved models or updated prompts without disrupting the production environment.

Iterative model updates & prompt tuning

• Continuous improvement: By testing new models in parallel, we can track improvements and roll them out once they consistently outperform existing solutions.
• Prompt optimization: We regularly refine prompts to address new trends in user queries or changes in industry terminology, keeping the classification system current and accurate

Solution highlights

Reduced mismatched leads by 35+%

Clients reported a dramatic drop in receiving irrelevant leads, saving time and focusing attention on leads that truly needed their expertise.

Boosted yearly revenue

With better-matched leads and improved efficiency, the platform's revenue rose significantly—demonstrating clear ROI for the new system.

Happier clients and leads

Less time wasted on mismatched leads fostered improved satisfaction among clients and leads who could get help faster.

Scalable infrastructure for ongoing innovation

The parallel model testing framework ensures that new AI advancements can be integrated quickly, maintaining a competitive edge and safeguarding the platform against obsolescence.

Results & impact

Operational efficiency

• Time spent following up on the wrong types of opportunities dropped significantly, allowing clients to prioritize genuine leads.
• Increased profitability
• Attaching an accurate category label early in the process led to higher conversion rates and a direct impact on monthly revenue.
• Data-driven decision-making around model performance ensured the system remained at peak efficiency.

Enhanced user experience

• Prospects no longer endure lengthy forms or irrelevant follow-up questions; the AI interacts in a conversational manner.
• Upcoming voice integration (currently in development) will cater to those who prefer speaking over typing, further lowering barriers to entry.

Enhanced user experience

• Prospects no longer endure lengthy forms or irrelevant follow-up questions; the AI interacts in a conversational manner.
• Upcoming voice integration (currently in development) will cater to those who prefer speaking over typing, further lowering barriers to entry.

Long-term platform growth

• Clients, pleased with the improved quality of leads, stayed on the platform longer and recommended it to peers.
• The flexible experimentation environment positions the platform to stay ahead of competition by rapidly adopting or discarding new AI techniques and expanding into voice channels.

Looking ahead

With a solid AI-driven classification and data-collection process in place, the platform is poised to expand into additional specialties. The underlying architecture—designed for continuous model experimentation and easy prompt updates—will support these expansions seamlessly, whether it's a new chat flow, a refined prompt system, or a voice-based phone call integration.

A key next step is integrating location verification into the lead intake flow. This enhancement will enable the system to ask targeted, on-point questions when it detects potential inaccuracies or high error probability in a user's input. By validating regulatory elements early in the process, the platform will further reduce mismatches related to location-specific industry requirements and ensure leads are routed to relevant and qualified clients.

Ultimately, this project showcases the power of combining robust AI classification with an adaptive, user-centric intake process. By continually refining and innovating, the lead-generation platform stands ready to maintain its competitive edge, deliver exceptional value to clients, and ensure a consistently streamlined, efficient client experience.