Lead qualification is a critical step in any sales and marketing process, ensuring businesses prioritize prospects most likely to convert. Traditional methods, however, are time-consuming, resource-intensive, and prone to delays. Companies often rely heavily on cold calling to qualify leads, which presents several challenges.

Challenges with Traditional Lead Qualification

• Time-Intensive Cold Calling: The process typically begins with lead generation through ads or influencer campaigns, where potential customers fill out forms. These details are collected in tools like Google Sheets and handed to sales teams for manual follow-up.

• Lengthy Follow-Up Process: Sales representatives call each lead to assess their interest, gathering information through structured conversations. Based on these discussions, they update the Google Sheet with the lead's level of interest and intent.

• Scalability Issues: For large-scale campaigns, such as managing 500–1,000 leads, the process can take a week or longer with a small team. This inefficiency increases costs and delays decision-making.

Solution

Let’s understand this with an example. For Instance: There is a travel agency who is running ads on instagram and tiktok. Now, they have received 1000 leads. A traditional method will involve giving these leads to the sales persons and they calling them one by one making the whole processing and time consuming(considering the people are efficient every minute) but with Svalync you can automate the whole processes.

Step 1: Fetching Lead Data from Google Sheets

The first step involves selecting the Google Sheet node in Svalync and configuring the "Get Sheet" action. This node retrieves lead information directly from the sheet. A test run ensures the data is correctly fetched, preparing it for the next steps.

Step 2: Activating the Voice AI Agent

Svalync's Voice AI Agent conducts human-like conversations with leads. Using the Google Sheet data, the agent initiates batch calls. The task for the AI agent is defined through a custom prompt tailored to the specific campaign.

Once the batch is processed, the system generates a batch ID and other key details for tracking.

Step 3: Summarizing Call Outcomes

After the calls are completed, the next node fetches detailed summaries of each interaction. These summaries include critical information as defined in the prompt such as:

• Customer preferences

• Intent to purchase

• Specific requirements

Example Summary:

Here is a concise and insightful summary of the call: The user contacted Tripping Cube to plan a trip to London around October 1st, 2025, for a group of 4 people with an estimated budget of $5,000 per person, totaling $20,000. The assistant, Karen, gathered essential trip details, including destination, travel dates, and budget, to begin exploring options for the user.

Step 4: Extracting Insights Using LLMs

The batch call responses are processed with advanced language models like Claude and Chatgpt. These models extract actionable insights and present them in a structured JSON format. This ensures data is easy to interpret and integrate into other systems.

Example Output:

Step 5: Updating the Google Sheet

Finally, the enriched lead data is updated back into the original Google Sheet. This step ensures that sales teams have access to complete, organized, and actionable information to plan their next steps effectively.

Conclusion

By automating the lead qualification process with tools like Svalync, businesses can drastically reduce the time and effort required to manage leads. The integration of Voice AI and advanced LLMs ensures high-quality, personalized interactions at scale.

For instance, what previously took a week with manual efforts can now be accomplished in hours. This not only accelerates the sales pipeline but also allows businesses to allocate resources more strategically, ultimately driving higher ROI.

You can also watch a demo video here

If you are looking to use this workflow in your project, Clone the template here