TalentGum is a global edtech platform helping children build real-world skills like public speaking, debate, and creative writing. Designed for outcome-driven learning, the platform serves a growing base of parents across India and international markets.

TalentGum's growth is driven by conversations. Before enrolling, parents want clarity which course fits their child, how classes work, how demos are structured, and whether the program delivers outcomes. This makes conversation quality critical to conversion. Unlike impulse purchases, enrollment is a decision-led journey. Parents ask, evaluate, and decide.

The challenge

As TalentGum scaled its acquisition channels, inbound conversations increased rapidly across WhatsApp, Instagram, and website chat. The team handled over 13,000 support tickets and more than 62,000 customer messages in a single month. Most conversations weren't complex they were structured, repetitive, and predictable.

Parents asked about scheduling, demo classes, enrollment steps, course details, and basic support queries. Every message required a human to read, understand context, check internal rules, and respond. The system worked, but it didn't scale. That's why TalentGum initially introduced AI for lead qualification. The idea was simple: let AI collect basic details like the child's age, course interest, and location so the admissions team could focus on better conversations.

But once the AI started interacting with parents, something changed. Parents didn't stop after sharing information. They continued the conversation, asking follow-up questions about recommendations, demos, schedules, and policies. It became clear that the real bottleneck wasn't qualification it was the entire first layer of conversation handling.

How Sagepilot helped

Sagepilot first launched as a lead qualification layer inside TalentGum's inbound conversations.

The AI would greet parents and collect key information such as:

• Child's age

• Course interest

• Location

This allowed counselors to prioritise the right conversations.

However, once Sagepilot began interacting with parents, the system naturally started answering follow-up questions about courses, demos, and scheduling. TalentGum gradually expanded Sagepilot's responsibilities.

Today, Sagepilot handles conversations across WhatsApp, Instagram, Facebook, and website chat, managing both enrollment inquiries and operational support workflows.

Parents interacting with TalentGum can now:

• Get instant responses about courses, eligibility, and program structure

• Receive guidance on demo classes and enrollment steps

• Reschedule or manage classes without waiting for a human agent

• Resolve login or technical issues quickly

• Ask curriculum or course-related questions and receive structured responses

• Get consistent support across messaging channels

Because Sagepilot understands TalentGum's workflows and policies, it handles a wide range of structured requests automatically while escalating only complex cases to the human team.

The result

Within the first phase of deployment:

• 13,275+ tickets handled by Sagepilot

• 56,000+ AI messages delivered

• 99.9% AI deflection rate

• 10-second average response time

• 4.7 CSAT maintained

In total, Sagepilot handled 95% of customer support with the deployment of the AI employee. These operational improvements translated directly into scalable infrastructure. The AI layer now manages nearly all structured support conversations while human agents focus only on complex edge cases.

Looking ahead

At Sagepilot, we believe AI should begin with small automation layers and gradually expand into full operational ownership. TalentGum's journey reflects this evolution. What started as a simple lead qualification assistant quickly became the company's primary customer support infrastructure.

Today, Sagepilot handles the majority of inbound conversations, helping parents receive faster answers while allowing TalentGum's team to focus on meaningful interactions.
For TalentGum, customer support is no longer limited by team size.
For Sagepilot, this is what scalable AI infrastructure for education looks like.