AI Agent Use Case: Revolutionizing Sales Operations with Virtual Assistants

Client

To transform how we engage our website visitors and streamline customer support, we developed a multi-agent AI solution to enhance workflows and boost efficiency.

As an AI & Big data service provider, we recognize the significance of quality customer support at any stage of the clients’ relationships and development process. In search of an innovative tool to interact more intelligently and conversationally with InData Labs’ website visitors and potential clients, we opted for using the GPT model to train a personal sales assistant.

InData Labs implemented the intelligent chatbot trained on the proprietary website data about the services of InData Labs to deliver advanced conversational client support and generate more complex and nuanced automated responses based on the website visitor’s profile and request.

The business need behind this project was clear: inbound B2B leads have a well-documented decay rate — the probability of qualifying a lead drops dramatically after the first hour of contact. With a human-only support model, most website inquiries went unanswered outside business hours, and sales reps were spending ~30–40% of their time on leads that would never convert — wrong industry, wrong budget, wrong stage. This directly increased cost per qualified lead (CPQL).

Challenge: automated 24/7 personalized support for website visitors while reducing operational costs and improving conversion efficiency

The InData Labs team customized GPT to build our own AI agent for the sales workflow automation and customer human-like interaction. The solution handles more complex queries and provides personalized experiences based on collected data and analysis of the customer domain. It also supports the lead generation process by capturing and qualifying incoming leads.

Three core business pain points drove this initiative:

• Lost leads due to response lag—a significant share of website visitors leave without converting; after-hours inquiries were falling through entirely
• SDR bandwidth wasted on unqualified traffic—manual qualification inflated the cost per opportunity and slowed the pipeline
• Inconsistent messaging — first-contact conversations varied by rep, creating positioning inconsistencies at the most critical moment.

InData Labs, a large language consulting company, was tasked with customizing the latest advanced GPT model on the proprietary data to scale customer experiences and reduce bounce rate by offering personalized and convenient customer engagement.

Solution: AI agent: Enhancing sales efficiency with virtual assistants

The main objective of developing an AI agent was to deliver fast, personalized support to website visitors, leveraging the advanced capabilities of GPT-4 model. Another key goal was to automate the initial incoming lead processing to decrease the time between the visitor’s request for services and the first follow-up call.

InData Labs customized the existing OpenAI toolkit, vectorizing documents for use in GPT-4 to serve the business’s needs.

We also trained a set of AI agents that based on the company name provided by the website visitor, search specific data in the internal (for example, client’s industry, years of foundation, geography, and other) to perform lead scoring and deliver tailored information about InData Labs, including relevant AI use cases and links to specific website pages aligned with the visitor’s needs and interest.

Each agent capability maps directly to a business outcome:

Any business that is studying agentic AI use cases and also looking for ways to scale up the company, automate Q&A processes, and decrease operational costs should consider implementing GenAI solutions.

After performing thorough AI agent case study research, our team has built a ChatGPT-based virtual assistant with cutting-edge NLP technologies. The project was split into the following phases:

• Study agentic AI use case examples
• Set up algorithms and install the necessary libraries
• Prompt engineering of GPT-4 (OpenAI) for specific chat flow
• Training a set of AI agents to perform specific tasks
• Document entities are retrieved and vectorized for document search
• Integrating external services into the chatbot: email sending, CRM, Pipedrive system
• Preparing the CI/CD process via SAM and deploying a chatbot to an AWS serverless architecture.

Result: сost-efficient and personalized assistance with client queries of higher complexity

The result of our work is a multi-agent AI solution that consists of a fine-tuned GPT-based virtual website assistant and a suite of AI analytical agents that use deep learning algorithms and massive datasets to help InData Labs website visitors get assistance with both quick and complex queries about the services and expertise of the company, as well as with arranging calls with sales representatives based on location, lead scoring, and opportunity creation in CRM.

At a glance, the results are the following:

In business metrics, that means:

• Pipeline quality — sales team now works only with pre-scored, CRM-ready leads
• Cost per qualified lead (CPQL) — estimated 60–70% reduction vs. human SDR model
• Time-to-first-call — from days to same-session scheduling in many cases
• Pipeline coverage — consistent lead capture improves forecasting accuracy
• Zero CRM entry errors — all data structured and logged automatically.

Today’s high-end AI agents and virtual assistants open up new possibilities for providing top-quality customer service and bring benefits for both sides. ChatGPT integration solutions for your business will help handle client queries faster with reduced expenses. In turn, customers will have their specific questions answered and get to the development process more promptly.