Gone are the days when lead generation was only about blasting emails to a database and hoping for results—the classic spray-and-pray approach. Sales have now become more targeted, with companies demanding highly specific solutions. In an interview with Tech Achieve Media, Kapil Khangaonkar, Founder and CEO of Clodura.AI, shares his insights on how their platform is revolutionizing the lead generation process with AI-powered intelligence. Designed to streamline and automate the sales outreach process, Clodura.AI transforms traditional methods into an end-to-end, AI-powered journey. By leveraging advanced technology, the platform empowers businesses to identify the right companies, connect with key decision-makers, and personalize engagement, and all this is done while maintaining compliance with global data protection standards.
TAM: Tell us a little bit about Clodura.AI
Kapil Khangaonkar: This is essentially a lead generation and sales intelligence platform designed to streamline and automate the sales outreach process. Typically, when a sales team wants to reach out to potential clients, they need to follow several steps.
- Identifying Target Companies: The first step is to identify the companies to target.
- Identifying Key Contacts: Once the target companies are identified, the next step is to find the key people within those companies.
- Gathering Contact Information: After identifying the contacts, their details are collected.
- Executing Follow-Up Activities: This includes reaching out via email, phone calls, LinkedIn messages, and other channels.
This entire process is traditionally long, manual, and tedious. Our platform automates it end-to-end, significantly reducing effort and time.
One of our key features is the logic we’ve developed to help users identify the right companies to target. Our database includes detailed profiles—what we call a “kundali”—for over 50 million companies worldwide. This profile includes comprehensive data, such as the company’s website, articles, blogs, posts, hiring trends, and other activities.
Users can filter and analyze this data to narrow down the companies that match their criteria. Once the companies are identified, the platform seamlessly guides users through the rest of the outreach process. In short, our platform integrates and automates every step of the sales outreach journey, all in one place.
TAM: How does your AI-driven platform differentiate itself from traditional lead generation methods in terms of accuracy and efficiency?
Kapil Khangaonkar: In sales teams, you often have a mix of freshers and experienced professionals. Freshers, in particular, usually lack knowledge of the lead generation process and how to execute it effectively. One of the biggest challenges in this industry is the lack of formal education or resources on lead generation. For example, if you want to learn marketing, you can pursue an MBA, read countless books, and stay updated on the latest trends. Even for sales, while there’s no dedicated MBA, you can still find plenty of books and resources to improve your skills.
However, lead generation, bringing potential customers to the table before the actual sales process begins, is often overlooked. It’s not part of traditional sales training, nor is it taught in any course or curriculum. The knowledge is typically passed down informally, such as from a manager or a more experienced team member. This makes the process undocumented, inconsistent, and difficult to master.
We’ve addressed this gap using AI. Our platform leverages AI and machine learning to automate and enhance various stages of the lead generation journey. It incorporates the best practices we’ve learned over time, such as:
- Identifying the most suitable companies to target.
- Conducting thorough research on these companies.
- Shortlisting those with genuine requirements.
- Streamlining the follow-up process.
By integrating AI at every stage, we’ve made the process not only faster but also more effective. As a result, when you reach out to potential leads, your efforts yield significantly better outcomes compared to traditional methods.
TAM: What kind of data does your platform leverage to identify and qualify potential leads, and how do you ensure data privacy and compliance? Aren’t data protection laws counterproductive to the true potential that AI platforms hold?
Kapil Khangaonkar: Data privacy is absolutely critical in this space. We ensure full compliance with major data protection regulations, including GDPR, CAN-SPAM, the California Consumer Privacy Act (CCPA), and the Indian Data Protection Act (DPDP). Maintaining compliance across these frameworks is a top priority for us.
When we collect data, we notify users about the information we hold on them and provide a link where they can review it. If users prefer not to have their data published on the platform, they can easily opt out. We strictly adhere to global data protection policies to ensure that the data we provide to our customers is clean and fully compliant. While our customers are responsible for implementing GDPR and other relevant regulations on their end, we take every measure to ensure compliance as a data collector. Our processes are designed to meet the highest standards of data privacy and security, giving our customers a reliable foundation to work from.
As far as your second question is concerned, I don’t believe any of these data protection acts specifically target the lead generation business. This is a common misconception. Even GDPR explicitly states—though you may choose not to quote me on this—that if you have a legitimate intent to store data, you are allowed to do so.
The purpose of global data protection laws is primarily to safeguard sensitive personal information. For example, let’s say you go for a medical checkup, are diagnosed with diabetes, purchase diabetes medication, and later buy a pizza. If a single entity can track all this information, they could infer personal details, like questioning whether you should be eating pizza. This level of personal data collection and profiling is what these laws aim to prevent.
What we do is entirely different. Every contact in our system is a corporate representation, not personal data. A corporate email address, for instance, is typically owned by the company, not the individual. When we reach out, we’re addressing a professional title or role within the organization, not the individual personally.
This means the data we collect is corporate-level data, not personal data. Additionally, most of this information is publicly available. We do not obtain it from private or restricted sources. Using AI and machine learning, we aggregate and organize this publicly available data into a cohesive format.
Our platform sources data from over 10,000 different types of publicly available data sources. These data sets are often provided in heterogeneous formats. Our system standardizes them into a homogeneous, easily understandable format, making it simple for users to consume and act upon the information. In essence, our platform’s role is to streamline and harmonize diverse data, ensuring it is accessible and actionable for our users.
TAM: Can you share a success story or case study where your platform significantly boosted lead conversion rates for a client?
Kapil Khangaonkar: One of our customers, a large $1.5 billion US-based organization, needed to target only companies selling to the US. Their product could only be used by companies with US-based customers, so they were specifically looking for such businesses.
Although they had access to every major platform available, none could provide them with this precise data—identifying companies exclusively selling to the US. Here’s where we stepped in. Using our comprehensive website data and AI capabilities, we analyzed company activities, not just storing data like Google does, but actually understanding what these companies do.
We ran backend algorithms to detect specific indicators like:
- Whether their pricing pages featured dollar signs or mentioned USD.
- Any mentions of imports or exports to the US.
- References to US-specific business operations.
This allowed us to create a tailored solution to identify companies selling to the US, something even their $1.8 billion competitor couldn’t achieve. This competitor, despite having 200 licenses with them, failed because they simply presented raw data without deriving actionable insights. Our approach, focused on understanding and extracting value from data, made all the difference.
Another major use case involved a 20-year-old company with a CRM containing around 3.5 crore (35 million) contacts. Over the years, data had been continuously added to their CRM without any cleanup. For instance, a contact added in 2015 might have switched jobs multiple times since then, yet their CRM still listed outdated details. This caused inefficiencies, as their sales teams were working with inaccurate or unreachable data.
When they approached us, we analyzed a sample of their contacts. We examined how company names and contacts were recorded, mapped historical data to current information, and updated their entire CRM. In just three months, we transformed their CRM, aligning 20 years’ worth of data to its current, accurate state—a task they couldn’t achieve manually in five years.
This was a significant success story for us, showcasing how our solutions go beyond lead generation. We also address peripheral challenges like ensuring data quality, enabling our customers to work with accurate and actionable insights across their systems.
TAM: What role does machine learning play in refining the quality of leads over time, and how does it adapt to changing market dynamics?
Kapil Khangaonkar: For us, data updating is of utmost importance. When we first started, we were compiling around one terabyte of data daily, but we could only process it every 30 days due to limited capabilities. Today, we’ve significantly scaled up—we now collect nearly five terabytes of data daily and process it almost in real time.
This means all the data collected each day is processed and updated into the system within the same day. While the system is refreshed daily, there’s also a 30-day cycle where all contact details, email addresses, and other information are fully refreshed. To ensure accuracy and relevance, we keep our engines running constantly to handle these updates.
Market dynamics also play a significant role. For example, during times of economic uncertainty, like a looming recession, there’s a lot of fluctuation. People may lose jobs, update their profiles on LinkedIn, join new companies, or sometimes be between roles. These changes happen frequently, and it’s critical for us to stay on top of them to ensure our data remains current and reliable.
TAM: As businesses grow increasingly diverse in their sales strategies, how does your platform cater to niche industries or highly specific target audiences?
Kapil Khangaonkar: If you look back to the period before 2017–2018, and even before the pandemic, lead generation was more of a “nice-to-have” process. Back then, you could procure a data sheet, send out bulk emails, and probably get some responses. But that approach doesn’t work anymore—people now receive so many emails that it’s no longer about quantity.
This shift highlights the state of the system today. Out of 100 companies, only about 5% generate enough inbound leads to avoid outbound efforts entirely. The remaining 95% of companies globally still rely heavily on outbound strategies to ensure predictable and consistent sales. With so many organizations doing this, email traffic has increased significantly, making quality the key differentiator.
Our platform addresses this by eliminating the need for manual contact research. For example, if you select 100 contacts in the platform, it will generate 100 personalized emails tailored to each recipient. It considers factors like the individual’s previous roles or recent company developments that might influence your conversation.
These personal triggers make outreach more human and engaging, as opposed to the outdated “spray and pray” method of sending the same generic email to 100 people and hoping for a reply. That approach doesn’t work anymore. We consistently advise our customers to prioritize quality over quantity. A smaller, more focused outreach strategy yields far better results than casting a wide net with impersonal messaging.
TAM: Against the backdrop of the recent comments made by Piyush Goyal at Startup Mahakumbh, what is the support that AI startups in India require to scale and thrive?
Kapil Khangaonkar: Managing delivery operations, especially ensuring that items reach your doorstep in under 10 minutes, is no easy task. It requires robust backend technology and significant coordination. Similarly, working on AI and advancing its applications has become absolutely essential. If you look at global trends, many countries are developing their own AI models. France has its model, China has its own, and, of course, the US is leading the way. Unfortunately, India doesn’t yet have a significant AI model of its own, which is a gap we must address moving forward.
The primary challenge here is funding. The typical Indian investor mindset focuses heavily on immediate returns—what they can gain month-to-month or day-to-day. Take the example of Sam Altman from OpenAI. He secured his initial funding in 2014 or 2015, yet OpenAI wasn’t profitable for years. The product launched only about a year and a half ago, after nearly a decade of development. During this time, his investors exhibited immense patience, which allowed true innovation to take shape.
You can’t expect groundbreaking advancements with minimal funding and short timelines. That’s not how innovation works. To truly foster innovation, the mindset of both investors and entrepreneurs needs to evolve. Investors need to embrace patience and long-term risk-taking, while entrepreneurs must expand their appetite for risk and failure.
Real innovation takes time, and it comes with setbacks. For every startup, there’s often only a 2% chance of success, and both investors and entrepreneurs need to accept this reality. Once this understanding sets in, we’ll see progress, albeit gradually.
While innovation is happening in India, it’s happening much faster elsewhere, which is why we see results from other countries more prominently. For instance, technologies like ChatGPT didn’t emerge overnight—they’ve been in the making for the past eight years. This is a vital perspective on the topic, emphasizing that progress requires patience, perseverance, and a willingness to take risks.
