AI Agent for Lead Management
Conversational Voice & Chat Agent for a Call Center.
Summary
- For most b2c businesses the leads' outreach consumes a lot of resources and time, while the conversion rate requires intensive work to attract clients;
- Business optimization infers the goal of involving AI in those areas of the sales workflow that are reasonable, especially for initial communication to reduce the cost of leads processing;
- Our team developed a conversational AI agent capable of performing voice calls and chat interactions. The agent follows various conversational behaviors, adapts dialogue strategies based on client responses, and collects information to simplify the decision process and further workflow;
- The developed solution helped to significantly decrease the price of call center support while increasing leads coverage;
Tech Stack
Delivery Timeline
Real estate agencies receive hundreds of inquiries from property owners every day. Each inquiry requires a sales manager to invest significant time in initial contact: making phone calls, sending texts, and gathering basic information about the owner and the property. This manual process is time-consuming and distracts managers from more important tasks such as closing deals and maintaining relationships with existing clients.
As a result, the agency was looking for an efficient solution to automate these initial interactions without sacrificing the personal touch that leads expect.
Tech Challenge
- Natural Voice Interaction. The AI Agent must talk with a natural, human-like voice to avoid sounding robotic, which is typically associated with spam calls.
- Low Latency Communication. When handling the voice calls, the LLM-based agent must have an overall latency as speaking to a real person.
- Dynamic Conversation Management. Providing clear and concise instructions to the assistant to manage conversations effectively, dynamically changing states based on client responses.
- Information Gathering. Efficiently gather information about leads and their properties while maintaining conversational flow.
Solution
- Used ElevenLabs for advanced text-to-speech synthesis, providing the assistant with a natural and friendly voice that reduces the likelihood of being perceived as spam.
- For speech recognition, the solution includes a self-hosted version of a Deepgram to ensure low latency and high quality of a solution.
- A special decision maker model such as OpenAI 4o-mini, is used in the system to adjust the flow of the conversation in real time. This model analyzes interactions and determines when to change dialogue strategies to ensure smooth transitions and appropriate responses.
- Implemented LLM-based entity recognition to extract key information about leads and their properties during conversations, and automatically process and log data for follow-up interactions.
- Enabled the agent to dynamically retrieve relevant information during conversations, enabling more accurate and contextually appropriate responses.
Consider a typical interaction in which the assistant contacts a property owner interested in selling.
The assistant initiates the call with a warm greeting using a natural-sounding voice. As the conversation progresses, the assistant dynamically changes the dialogue flow based on the owner’s responses by providing more details if the owner seems interested or gently redirecting if the owner is hesitant.
Throughout the conversation, entity extractor defines important information like property location, size, and the owner’s preferred selling timeline, automatically updating the client’s database for future follow-ups.
The end of the call is an agreement about next steps with the real sales representative.
Impact
- The agent significantly reduced the amount of time sales managers spent on the leads outreach by automating initial communications and routine tasks such as taking notes, sending follow-ups and meeting links.
- Automating of lead interactions led to reduced operational costs and increased throughput by optimising business processes.
- The agent became part of the SaaS platform, helping similar companies with operations of their sales manager and call center operations.
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