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.
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 cases using vector search and RAG; similar cases identified through this method are integrated into the classification prompt, refining the AI's understanding and boosting classification precision.
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.
Clients reported a dramatic drop in receiving irrelevant leads, saving time and focusing attention on leads that truly needed their expertise.
With better-matched leads and improved efficiency, the platform's revenue rose significantly—demonstrating clear ROI for the new system.
Less time wasted on mismatched leads fostered improved satisfaction among clients and leads who could get help faster.
The parallel model testing framework ensures that new AI advancements can be integrated quickly, maintaining a competitive edge and safeguarding the platform against obsolescence.
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.