Pramin Pradeep on: AI-Driven Quality Assurance | Ep 1206

Summary

Podcast Summary: The Digital Executive - Ep 1206 Pramin Pradeep on: AI-Driven Quality Assurance
Date: March 2, 2026
Host: Brian Thomas (Coruzant Technologies)
Guest: Pramin Pradeep, Co-founder & CEO of Botgage AI

---

Episode Overview

This episode delves into the rapid evolution of quality assurance (QA) for software development. Pramin Pradeep, CEO of Botgage AI, explains the necessity for autonomous, AI-driven QA in modern software teams. He contrasts traditional QA methodologies with Botgage’s innovative hybrid model, explores the increasing pressure of rapid release cycles, and shares insights on building true AI-first QA platforms from the ground up.

---

Key Discussion Points & Insights

The Fundamental Shift in Quality Assurance

[02:03]

• Changing Release Cycles: Traditional release cycles spanned months, allowing for structured QA. Now, fast-moving SaaS environments demand much higher release frequency—sometimes multiple times a day.
• The Automation Imperative: Pramin highlights how automation became essential as customer expectations rose and release cadences tightened. Early automation was niche; today, it’s the norm, but existing tools still struggle to keep pace.
• AI-Driven QA is Critical: Only AI, rigorously embedded within the software lifecycle, can enable the speed and breadth required for modern QA.

“Before 10 years, every application…release cycle…was happening once in six months, once in three months. However, as the development progress, the customer expectations started increasing…Because of that, automation become more prominent…right now the need of [the] hour is for the QA to cope up with the shorter release cycles with prominent automation in place. For that, only the AI can be implemented into it.”
— Pramin Pradeep [02:03]

---

Botgage’s Hybrid Model: AI Agents + Human Expertise

[04:19]

• Why Not Fully Autonomous?: Pramin explains that every SaaS product comes with unique customizations. Pure AI lacks the context for all nuances.
• Botgage Approach: They combine AI native testing agents with forward-deployed QA engineers. The agents learn and adapt to the app, while human engineers monitor, guide, and ensure accurate learning.
• Huge Speed & Coverage Gains: Pramin quantifies the improvement: what once took 4–5 months to reach 80% coverage can now be achieved in just 2 weeks, thanks to this hybrid model.
• Outcome: Teams can cut release cycles from weeks to days without sacrificing quality.

“Just handing over the agents to the company has not gone out because every SaaS or every software is different…a forward deployed engineer has to monitor…whether it is learning in the right format…So it’s very important to have both agents and the human interlock to make sure the customer is able to get the right output….with agents which you have built and the human in the loop, you’ll be able to do [4–5 months of work] in two weeks of time.”
— Pramin Pradeep [04:19]

---

Autonomous QA: The Innovation Bottleneck

[07:06]

• Coding Speed vs. Release Bottleneck: While the speed of coding (with tools like copilot and prompt-based development) has exploded, actual customer releases now bottleneck at QA.
• Customer Tolerance: Users will not tolerate buggy releases; one bad experience and they may switch competitors.
• Integrated QA Framework: Fast coding must be matched by equally fast, end-to-end regression and quality checks—this is where autonomous QA steps in.
• Feedback Loops: The aim is not just faster coding, but getting reliable features to customers quickly and capturing user feedback.

“Writing the code is much important. However, the most important part is shipping to the customer to get the feedback loop established. For that you have to tightly integrate the autonomous QA also into the frame.”
— Pramin Pradeep [07:06]

---

Building True AI-First QA Platforms

[09:16]

• Pitfall: Layering AI on Legacy: Simply adding AI as a superficial layer to old infrastructure adds complexity and doesn’t address core challenges like self-healing or dynamic learning.
• AI-Native Design: Pramin stresses the need for AI-first companies to build QA infrastructure and algorithms from scratch, optimized for autonomy and adaptive learning.
• Botgage’s Edge: Its platform was conceived during the LLM (Large Language Model) era and designed from the ground up to harness AI for dynamic, resilient QA.

“You cannot modify the existing infrastructure and try to make it AI by just adding a layer…You need to build everything from scratch…That’s what Botgage is. Botgage is an AI born company…all the code, not only the LLM infrastructure layer but also the algo to support that has been written from scratch to enhance the agent from the first learning approach.”
— Pramin Pradeep [09:16]

---

Notable Quotes & Memorable Moments

• “With agents which you have built and the human in the loop, you’ll be able to do it in two weeks of time.” [04:19] — Pramin Pradeep, on quantum leap in test coverage speed.
• “It is very important for any AI first companies to build from scratch than adding a layer on top of the existing infra.” [09:16] — On why legacy add-ons fall short.
• “Once the speed of coding is entertained by the ecosystem…they’re not able to release into production because of QA building the bottom line…customers…don’t accept bugs or any flow broken.” [07:06] — On QA as the bottleneck in fast-paced dev.

---

Timestamps for Key Segments

• 00:00-01:29 — Introduction, guest bio, episode setup
• 02:03 — What’s broken in traditional QA, why quality as a service?
• 04:19 — How Botgage’s hybrid model works (AI + human QA)
• 07:06 — Autonomous quality as the next wave of innovation
• 09:16 — Building true AI-first QA vs. layering AI on legacy
• 11:16 — Closing remarks and gratitude

---

Conclusion

Pramin Pradeep makes a compelling case for why autonomous, AI-driven QA—designed from the ground up, not as a bolt-on—is essential for today’s high-velocity software teams. He sheds light on tangible gains in speed and reliability through Botgage’s hybrid model, emphasizing the importance of both cutting-edge AI and knowledgeable human oversight. The coming decade, he forecasts, will be defined not by how quickly we code, but by how quickly and reliably we ensure quality at scale.

Transcript

[A]

Foreign. Welcome to Coruscant Technologies, home of the Digital Executive Podcast. Do you work in emerging tech? Working on something innovative? Maybe an entrepreneur? Apply to be a guest at www.corazon.com brand welcome to the Digital Executive. Today's guest is Pramin Pradeep. Pramin Pradeep is the co founder and CEO of Botgage AI, a US based autonomous QA as a solution company redefining how modern software teams ensure quality at engineering speed. Using QA as a solution model. Bot Gage redefines quality assurance for fast growing engineering teams. It combines AI native testing agents with forward deployed QA pods to continually create, run and maintain end to end test with owned quality outcomes. With over a decade of deep experience in low code ecosystems and enterprise QA transformation, Praman has built his career at the intersection of automation and scalable software infrastructure. He previously helped scale a high growth startup from inception to 3 million in revenue contributing to its acquisition by Sauce Labs. Praman has worked with leading enterprises including Adobe, Infosys and unqork to streamline software testing and quality operations. Well, good afternoon Pramin. Welcome to the show.

[B]

Yeah, thanks Ram. Thanks for inviting me.

[A]

Absolutely my friend. I appreciate it. And I know you're generally based out of the Bay Area. San Francisco, California. I'm in Kansas City today. You're traveling. I understand. So we'll just jump into it here. Prominent. Let me ask you first question here. You describe Bot Gauge as an autonomous QA as a solution company. What's fundamentally broken in traditional quality assurance models that require rethinking quality as a service rather than a function?

[B]

Yeah. So to understand that we have to understand the history of the quality engineering or quality assurance as stage right. So here if you take before 10 years, every application which came into production, the release cycle, I.e. every changes, was happening once in six months, once in three months. However, as the development progress, the customer expectations started increasing. They want more and more functionality. They wanted to get into the latest update, the user friendliness, everything started coming into picture. Because of that, the competition increased and the release pressure started increasing for all the SaaS companies. So from once in six months it started reducing into once in two weeks, then once in a week and once in an hour. So because of that, automation become more prominent before tenure. If you ask anyone, they will try to say that okay, only few companies are doing automation, maybe through Playwright Script or maybe through Selenium and Open Source. However, right now the need of our is for the QA to cope up with the shorter Release cycles with prominent automation in place for that, only the AI can be implemented into it and it should inbuilt most rigorous way into any SaaS ecosystem. For that.

[A]

Thank you, I appreciate that. And you're absolutely right, I was a developer early in the days and looking back, our QA was pretty strict, right? We had some really structured release cycles and as you talked about, the release cycles were not very frequent. But as the demand for more enhancements, more functionality came about, QA it was hard to keep up with the qa and I totally get that. But what you've done is really brought an autonomous level of quality at a faster scale, a faster turnaround. So I appreciate that. And Promen Bage combines AI native testing agents with four deployed QA pods. How does that hybrid model improve speed, reliability and ownership compared to purely automated or purely manual testing approaches?

[B]

Yeah, so I have been in this space for more than 10 years now. So seeing the journey from an open source automation to local automation using NLP through the AI world. Right, right now what is happening, just handing over the agents or agents to the company has not gone out because every SaaS or every software is different and go through a lot of customization. So the learning process is very important for any agents which get integrated into their ecosystem. So for that what we have done different is like we enhance our agents and deploy into their ecosystem. However, a forward deployed engineer has to monitor and monitor and analysis the agent such a way that whether it is learning in the right format, whether it's crossing the boundary condition or not, so all those things has to be constrained, all those constraints have to be kept in mind. That's why we not only deploy the agents, there's a forward deployed engineer which monitors end to end operation of the agents and also provide inputs if we are deviating from the path. So that's why it is very important to have both agents and the human interlock to make sure the customer is able to get the right output. So here the output is nothing but an increased coverage in a shorter period of time. Just giving you numbers in place, right? Consider they just go with the open source code local tool which is available in the market at least the customer is going to take around four to five months to reach 80% of coverage. However, with the agents which you have built and the human in the loop, you'll be able to do it in two weeks of time. So that's the kind of onboarding of test cases and the coverage we'll be able to implement in any ecosystem compared to the traditional methodology which is there in place. So it's an increased efficiency, increased coverage which will support their release cycles and they can reduce their release cycle from two weeks to two days with the kind of deployment which we do it in that infrastructure.

[A]

Thank you. And you're right, you highlighted that hybrid model. Efficiency, quality, faster turnaround times. That learning process for AI agents does take some time to learn and you want to make sure that it's accurate. And I like how you use this hybrid model of having that human in a loop, you mentioned that forward deployed engineer to actually monitor and make sure that it stays within its parameters. So I appreciate the insights. And Pramin, many engineering teams prioritize shipping features quickly. Why do you believe the next decade of innovation will be defined by autonomous quality infrastructure rather than faster coding alone?

[B]

Yeah, so that's a very interesting question, Brian, because right now, if you see from a development standpoint of view, there are multiple companies addressing that problem statement, like some call it, and white coding, right? Increased level of coding enhancement through prompts, everything which is happening. However, once the speed of coding is entertained by the ecosystem, right. However, they're not able to release into production because of QA building the bottom line, because customers, as you rightly know, they don't accept bugs or any flow broken. Once that is done, they'll just shift to competition. So that becomes the most important point. And end of the day it's not about writing the code. They have to release into the prod and customers should start using it for that end to end regression has to be done. That's why it's important to have an autonomous QA framework integrated into any infrastructure to support these release cycles in place. So what I want to tell the community, yes, writing the code is much important. However, the most important part is shipping to the customer to get the feedback loop established. For that you have to tightly integrate the autonomous QA also into the frame.

[A]

Thank you. Appreciate that. You did highlight some things that are happening at all different levels of sizes of organizations. But the speed of AI agents with this low code, this no code QA is definitely the bottleneck right now. And people can't wait for things. It's just kind of how we are in our human nature. But you highlighted the fact that having that develop and integrate that autonomous, those autonomous agents in that QA process will help speed this along. And obviously we want to have quality output, of course, so I appreciate that. And Pramin, the last question of the day as bot gauge scales following its recent funding round what will separate autonomous QA platforms that truly deliver outcomes from those that simply layer AI on top of legacy workflows?

[B]

Yeah, so it's like you cannot modify the existing infrastructure and trying to make it AI by just adding a layer too. Right. You need to build everything from scratch, especially if you're going for a first company for example the traditional players where their code is already written then and they're not able to even Let me touch on one of the major pain point in automation that is self healing maintenance when a element moves around or change or the flow changes. Right now the traditional framework cannot cope up with it. These kind of autonomous being the nature that means the initial algo has to be written for AI. That's why it's very important for any AI first companies to build from scratch than adding a layer on top of the existing infra. That's what BGAGE is. Butgage is an AI born company where we just started in the AI LLM era where all the code, not only the LLM infrastructure layer but also the algo to support that has been written from scratch to enhance the agent from the first learning approach. How can it refine from the or how can it refine to the most extreme level of learning, the end to end application in a shorter period of time.

[A]

Thank you, really appreciate that and you did talk a little bit about especially with your company, but AI companies in general and AI platforms it is best to build everything from scratch if you're building that type of infrastructure. As you know there are problems with adding multiple layers or adding AI layer just on top of legacy workflows. Obviously that is going to add more complexity and more problems down the road. So again I appreciate you teasing that apart for us and Pramin, it was such a pleasure having you on today and I look forward to speaking with you real soon.

[B]

Yeah, thanks for inviting me to this podcast Brian and appreciate yeah we'll catch in person after.

[A]

Bye for now.