TL;DR
Madhav Krishna started with a virtual English teacher before pivoting to blue-collar hiring with Vahan. Here’s what his journey looked like:
01. THE BET
They built Lakshmi, a virtual English teacher.
People used it, but finding customers willing to pay consistently was the challenge.
02. THE MISTAKE
Training was a “vitamin, not a painkiller.”
It was useful, but finding a job was a more urgent need.
03. THE TURN
They moved from training to hiring.
When their WhatsApp chatbot struggled to convert users, they partnered with recruitment agencies that workers already trusted.
04. THE SCALE LESSON
AI can handle the grunt work, not every human interaction.
Vahan aims to automate 80–90% of the hiring process while keeping people involved where trust matters.
05. THE BUSINESS MODEL
Charge for the outcome, not just the lead.
Vahan moved from selling candidate profiles for ₹10–30 per lead to charging for successful placements.
06. THE PLAYBOOK
Five lessons for AI founders.
Solve an urgent problem. Look beyond the pilot. Understand user behaviour. Automate where it helps. Build around outcomes.
Building AI is only the start. Finding the right problem, earning trust and delivering an outcome people value is what makes a business scale.
What does it take to build and scale an AI hiring business which customers actually trust?
Madhav Krishna, Founder of Vahan, started with a virtual English teacher before pivoting to revolutionizing hiring for India’s 500M+ blue-collar workers.
Here’s what his journey reveals on scaling an AI business beyond the pilot stage.
THE BET
Madhav wanted to use technology to improve people’s lives. He saw how a lack of English-speaking skills could limit job opportunities, so he built Lakshmi, a virtual English teacher people could talk to over the phone, without needing a smartphone or internet connection.
The product reached people through training institutes and NGOs. Getting people to use it was easy. Finding customers who would pay for it consistently was the challenge.
THE MISTAKE
An opportunity with Uber made Madhav realize that companies could be good customers. But the team wasn’t getting long-term contracts.
Madhav began to see training as a “vitamin, not a painkiller.” It was useful, but not urgent enough for customers to keep paying for it.
For blue-collar workers, finding a job mattered more than spending months training for one. This helped Madhav look beyond training and focus on a more immediate need: hiring.
THE TURN
In late 2018, Madhav’s team pivoted from training to blue-collar hiring. It built a WhatsApp chatbot that recommended jobs and collected basic information. People applied, but conversions remained low.
The reason was trust. Workers often found jobs through friends, relatives and recruitment agencies, and needed confidence that an opportunity was real.
So, Vahan started working with recruitment agencies and built software to help them match candidates with jobs. Instead of replacing existing networks, the team found a way to help them work better.
THE SCALE LESSON
That shift also showed Madhav where AI could make the biggest difference. Hiring depended on human trust, so automating the entire process wasn’t the answer.
Vahan uses AI to call candidates, ask basic questions and understand their job preferences. The aim is for AI to handle 80–90% of the grunt work, while people step in when needed to build trust.
THE BUSINESS MODEL
Vahan initially sold candidate profiles to employers, earning around ₹10–30 per lead. But the model captured little of the value created when a candidate actually got hired.
The team realized employers would pay more for successful placements. Vahan moved towards charging per placement, earning thousands of rupees when a candidate was placed.
This shifted the business from selling access to candidates to charging for the hiring outcome employers wanted.
THE PLAYBOOK
Madhav’s journey offers five lessons for AI founders:
- Find a problem people need to solve now. A useful product isn’t always an urgent need.
- Look beyond the pilot. Early success matters, but recurring business is what makes the model sustainable.
- Pay attention to user behaviour. Understand why people aren’t converting before changing the product.
- Know where AI fits. Automate repetitive work while keeping people involved where trust matters.
- Build around the outcome. Charge for the value customers receive, not just the activity your product generates.
You don’t always need significant capital to start building with AI. Madhav’s advice is to build, test and look for early traction. Once you have conviction in the idea, you can hire a team and raise the capital needed to grow.
Powering the Business Behind the Growth
As businesses grow, they need reliable payment infrastructure to make every transaction easy for their customers.
PayU Payment Gateway helps businesses accept 150+ payment modes, with industry-leading success rates, 99.98% uptime and seamless integration across web and app platforms.
Know more about PayU solutions
NO ONE WAY TO SCALE
Madhav’s journey with Vahan shows how a change in direction can lead to a stronger business. By moving from English training to blue-collar hiring, he found a problem that was more urgent for workers and built a solution around how they actually find jobs.
A working AI product is only the start. To scale, you need a problem worth solving, customers who trust your solution and a business model that captures the value you create.
PayU powers growing businesses, so founders can focus on building what’s next.
