Can We Create A Universal AI Employee? with Surojit Chatterjee, CEO of Ema

Richie Cotton: Hi Surojit, welcome to the show.

Surojit Chatterjee: Thank you. Very excited to be here.

Richie Cotton: Excellent. So people have been talking about AI agents being the next big thing for a few months now. Can you tell me what's the coolest example of an AI agent you've seen so far?

Surojit Chatterjee: There are a lot of cool AI agents out there, a lot of claims. I'll give an example from maybe our own customer base. We have AI employees which are actually slightly more complicated than AI agents. They are a collection of agents. But we have AI employees today working in customer support. helping resolve 80 percent or over, sometimes 90 percent tickets for our customers at a proficiency level better than humans.

So to me, that's, pretty amazing and shows what's the, potential here. We are seeing a lot of applications in customer support, HR, employee experience, sales and marketing automation. And across the board, horizontally, in lots of different interesting use cases. 

Richie Cotton: So that's pretty cool. Sort of 80 to 90 percent of customer support tickets being resolved by an agent. And you mentioned that it's also sort of HR, marketing. Are there any other areas of business or any of the particular types of AI agents that you think are common?

Surojit Chatterjee: Look, at this point, it's kind of proven that customer support is, actually works, companies are using it. We are addressing a lot of that. demand ourselves and there are other companies, other competitors as well. We see wherever there is opportunity for eliminating repetitive tasks, tedious tasks, there is a role for AI agents today.

And this task doesn't need to be repetitive in the sense of traditional like RPAs. type of task, like robotic automation, they can be repetitive in the sense of they may require some amount of human judgment, but they're the same similar task over and over again. And today these agents can reason, can make decisions independently, can make judgments, may require human guidance, sometimes may require human approval, but all that seems to be increasingly manageable and tractable. 

Richie Cotton: that's interesting. You're wanting the agents to have some kind of reasoning power beyond just being a sort of dumb chatbot. So we'll maybe get into like what sort of reasoning capabilities possible later. But for now, I'm curious why you might want to create agents. So, I mean, it seems like obviously a bot is going to be cheaper than a human for a lot of cases, but is it purely about cost savings or are there other business benefits to making use of agents?

Surojit Chatterjee: think the first inspiration was for many enterprises as cost saving. But increasingly, we are seeing customers are coming to us for, Hey, what insights can you derive from all my data? What kind of additional innovation your agents can do? So I think will not be limited to cost saving. It will be about creativity, innovation, building new things, finding insights that really benefit the business.

We are seeing demand for all such applications. Of course, cost saving is somewhat easier to prove than what's the impact of an insight and so on. 

Richie Cotton: Okay, so I like the idea that you're wanting agents to be creative. Do you have any examples of what sort of creative tasks an agent might be able to solve?

Surojit Chatterjee: Yeah, so creative within some bounds, of course, you do not want, particularly in the area we are in B2B applications enterprise, so you want creativity, but within within the guardrails of the company. And some examples will be like generating, for example, a blog, books, generating, writing responses, like creating a business proposal, which is creative again within bounds, you won't manufacture things that don't exist, but it requires kind of creativity to answer. specific questions maybe your customer is asking. So today we are automating responding to RFPs. Large top, like, Fortune 500 professional services companies are using us to automatically generate the first draft of the response to RFPs and then iterate and create a final draft with, minimal human labor or human effort.

Often these RFPs are now generated at a speed 10 to 20 times faster than they were generated before. So this is pretty game changing. Like think about it. Like most, many companies make revenue by responding to RFPs. And there is like a win rate, 30 percent win rate, maybe. If you respond to more RFPs, you may, at a high quality, you may actually get more revenue.

So a lot of these tasks are also, the tasks that agents are doing, are not just saving costs, but generating more revenue. We see the same thing with customer support, for example. Consistently, the support, like automation, actually. gives higher CSAT than what your human agents would do. We have a customer, Honeyview, their large B2C lending company, 60 million users or so. They are seeing that normally their CSATs were like 40, 45 percent, which in BFSI is pretty common. And that's going up to 75 percent or so. Not just automation, it's also better customer experience.

Richie Cotton: I love that idea of automating boring tasks by using AI agents. Are there any other particular sort of tasks that you think humans wouldn't want to do that you think are gonna be taken away by ai like that?

Surojit Chatterjee: Yeah, look, the business today, people spend a lot of time just keeping the lights on. Fifty, sixty percent of time of average kind of employees are spent in just keeping the lights on, which is tedious tasks, repetitive tasks that could potentially be delegated to an AI agent or an AI employee. A lot of these tasks will involve. using some kind of unstructured data to derive insights, using unstructured data to understand what actions to be taken. And that's, what AI agents are able to do.

Richie Cotton: I like that idea of just anytime you've got some sort of boring task or maybe just like you need that little bit of insight than having an agent to do that for you saves you of the trouble actually on. Agents replacing humans. I think that's maybe like one of the bigger worries that people have is that the job is going to be at risk.

Can you talk about like, how this might play out from career standpoint when you've got agents sort of. Possibly competing with you for parts of your job.

Surojit Chatterjee: I think actually agents not replacing humans, but agents making every employee a, like, super employee. It's like, will you have somebody come to work today and say, hey, I don't want to use computers. I'll just do everything on pen and paper. They will just not be very effective as an employee, even if they are very smart compared to other employees.

Because, you know, why do you want to multiply two large numbers with pen and paper when a calculator can do it? It's the same thing, Why do you want to spend much of your, work time on things that you don't like to do? It's like, these are kind of the part of your job.

You are actually better off that An AI employee colleague of yours does it under your supervision, under your instruction. So I think of this more like humans working with AI hand in hand rather than AI replacing humans. AI, just like every phase of every epoch of technology has done. It has just made humans more efficient, given freed up time for humans to be more creative and do what they want to do.

 I think we are going to enter that era now where a lot of the office work particularly the repetitive part of the office work can get done by employees so humans are freed up to do more value added, more creative tasks. I mean, if you look at small businesses, the biggest challenge they have is not that they have too many employees and who don't know what to do.

They have too few employees. They're overstretched, right? If you look at healthcare, for example. Everybody is stretched. And getting automation come in and take on repetitive part of your job is a good thing. not replacing anyone. It's freeing up people to do what they want to do.

Richie Cotton: definitely a big fan of having AI do things that I don't want to do. And I agree that a lot of a lot of teams across many companies they're overstretched and they just need to be more productive and making use of any technology. It's going to be incredibly helpful. Certainly, I think that's the case for like, most data teams.

This is never enough people. I'm wondering whether It varies is there a tipping point like, customer service where no company really wants to spend lots of money on this and there is just lots of, there can be lots of AI agents for this, is that going to play out differently, do you think?

Surojit Chatterjee: Look, I think in most of these roles, the future will be that it's majority done by AI. And with humans managing AI, humans giving instructions to AI, I also don't believe it will be a place where AI just runs over humans and just runs completely autonomously. But AI working, AI employees or AI agents working under the guidance of humans.

And a lot of the jobs will shift to kind of more value added part of the business. For example, building or conceptualizing new products. figuring out strategy, maybe doing more actual selling and actually talking to the customer than filling out spreadsheets after a customer call and so on.

Richie Cotton: Okay, yeah, that's definitely true is that the admin after a call to a customer is, is obviously, it's the tedious part that all the commercial teams forget to do and then you've got terrible data and it has knock on effect. And yeah, everyone grumbles,

Surojit Chatterjee: And then they would rather spend more time with customers. I mean, this is just an example and you can extend this example to other parts of the organization as well. If you're a salesperson you want to spend more time with customers. You want to solve customers problems. That's what gives you energy.

Understand their problems, figure out innovative ways to, bring a solution to their problems. But then they end up spending all the time in tools. Writing out reports, filling out spreadsheets. Imagine if someone could just do that, If every salesperson had an intern, Same is true for engineers.

Same is true for marketers. customer success people everybody who look, finance people, I would rather think of, okay, you know, how do I plan the budget for next year? Rather than entering hundreds of numbers in a spreadsheet and spend hours on that, like, can that be automated, in a way that's also, you know, highly accurate.

Richie Cotton: all right, this is going to really annoy like that one person who loves data entry and the one person who loves writing reports, but maybe for everyone else, it's net positive. All right, cool. So, you mentioned the idea of a universal employee and I noticed that this is part of your company mission is to create a universal AI employee.

So, how broad can AI employee be? I think a lot of agents are traditionally confined to very narrow tasks.

Surojit Chatterjee: Yeah, so we think, and this is why we started this company, vision of the company two years back when we started, right when chat GPT was coming out, is creating this AI employees that can mimic human roles and take on a lot of the tedious tasks that humans do at Workplace today. And I think there is really wide application.

You can look at automating very large number of things today. And just to give some context, our AI employees are not just monolithic agents. They are a collection of agents. So some of the AI employees will have, you know, 10, 20, 30 different AI agents working together and orchestrated using our, proprietary model.

Some of those agents may also be procedural agents. It doesn't need to necessarily be AI agents. So it's a combination of different types of agents working together. And if you look at the scope today, we have customers in health care using us for automating, say accelerating a lot of, say, prior authorization for treatments and so on.

I talked about sales and marketing, like writing, automatically writing business proposals generating sales leads, reaching out to leads scheduling a meeting, all that can be automated and we have customers doing that. Customer support. Largely can be automated. HR and employee experience. A lot of, you know, day to day questions that employees will ask HR, right, that can all be automated.

In a way, I mean, some chatbots, etc. existed before. But now you can create automation that works very much like a human. So it will have a fluid conversation with you, not just fixed conversation based on keywords and so on. Like, it's not a frustrating, conversation with IVR or whatever you have, the previous generation.

we have, like, voice automation. Same thing, you can call and talk to almost like a human agent on the other side. Yeah, I can. Mimics how human will, speak in that role. I think the, scope is very, very broad. Every week we find newer use cases, working with our customers. I think this is a transformation moment for the entire industry.

It's like when computing came in, for the first time. You know, why could you use computers in the business? some people said originally, Oh, you can write some documents and maybe some spreadsheets. Those are still the most common applications, but then, you suddenly realize, oh, it can be used anywhere, everywhere, I think that's what will happen. It will also disrupt a lot of the existing stack and existing software. that many companies are using.

Richie Cotton: Okay this is fascinating stuff. I guess a lot to unpack there. So I like the idea that not all agents need to use AI. You can still have procedural agents, which is, doing if some condition, then do that. And you can mix these things together. Okay, so you mentioned the idea of using many agents together.

I'm not sure what it's called. It's like a team of agents, swarm of agents, something like that. 

Surojit Chatterjee: mesh of agents, but you can call it swarm.

Richie Cotton: of agents. Okay, that's a good phrase to know, mesh of agents. All right. in this mesh, like how complex they get are we talking like two or three agents together or is this like thousands? What, what's the deal?

How, how much does it scale?

Surojit Chatterjee: I mean, one day there'll be thousands. At least today, we have like tens of agents, like 30, 40 agents are not uncommon. for a very complex task. And just to give you some illustration, for example, you're writing a very long form document, like a response to an RFP. So some sections of the RFP will require, say, building a chart, or putting an image, and that's a separate agent, like building charts from data. Some sections may require going to the web and synthesizing some information and putting it there. That's a different agent, It knows how to go to the web, synthesize data, look for data, and create a concise text conforming the style of the rest of the document. Now there are those agents, but then there are some meta agents that actually observe other agents, how they work, So there'll be some agent checking that the entire document is consistent and looks like it's been written by like a single person, not different tones and style and so on, right? Not a hodgepodge. There'll be some agent we have also seen in a, there's a very common agentic design pattern called reflection.

So instead of one agent doing a work to stand alone, what if you have another agent observing the work of one agent and giving feedback, and that dramatically improves accuracy. This is actually intuitively not, not very hard to kind of grasp. This is how humans work, right? that's why you have like, auditors and somebody who checks the audit, right?

Cost checks. And that. increases accuracy, even though both of these people may have same credentials, you know, in software coding, people often do like peer coding, right? Two people coding together, Or somebody codes and somebody does a code review and that increases accuracy of the software. And then we have explored lots of different design patterns, this is one pattern. You can also have a pattern where there's one agent directing, like, six or seven or ten agents, right? This is a common, like, a manager versus employee design pattern. You can have multiple experts contributing, together to build something, to come up with something.

 So we explore agentic design patterns, and we have created the engine that explores them. kind of autonomously, automatically tries out many design patterns and figures out what, what will be best for solving a problem. I think this is, there is a lot of R& D to be done in this space. and a lot of cool innovation that will happen.

 It's almost like You know, understanding how organizational design patterns work, If in human kind of organizations, enterprises, different types of organization has different types hierarchy or organizational design, And something may work for a certain type of organization. Let's say military is differently organized than an innovative software company, And there is there's reasons for that. And we, we think a lot of that organizational design literature and, studies are very useful to get insights on how agentic design patterns will work.

Richie Cotton: Okay. That's fascinating that you need to think about organizational hierarchy in order to make these meshes of agents work. And yeah, I can certainly see how In the real world, like, military hierarchy is very different from, I don't know, you have cooperative of artisans sort of thing.

So, yeah can you talk me through what are the most common architectures then for these agent meshes? 

Surojit Chatterjee: the most common architecture would be like a chain of agents, right? So one agent does some work. and you know, hands it over to another agent and so on. That's, that'll be the most simplest agentic design pattern. For almost all our AI employees we use something like a lot more complex kind of reflection is a very common pattern we use where my two agents or three agents are working together and observing each other's work and giving feedback that, improves.

accuracy quite a bit. There are, as I described, in the space of how this agents can be organized is a very interesting space of research that we keep spending a lot of time on.

Richie Cotton: I'm curious as to what counts as a successful agent. I presume there must be some kind of accuracy measure for, like, how well does it perform. But yeah, talk me through how do you know if your agent is any good or not?

Surojit Chatterjee: it's a great question because in many cases the definition of accuracy is not very, very. well established, Because we are sometimes doing generative work, not, not just work that, that has like a very fixed answer. So we often resort to like human evaluations, like a, a group of subject matter experts will evaluate.

It's almost like giving performance feedback to other humans. And so how do you know your marketing person is doing good work? , you ask other people who are working with the marketing person. You have some OKRs, et cetera, some, some like performance metrics. But before, beyond that, you also look at qualitative like aspects, How good is the writing and so on. Similar principles apply. In some cases there is clear cut, whether it's correct or not. that can be established. In that case, it's easier. In some cases, a procedural agent actually can evaluate an AI agent because the procedural agent can just check the final answer.

Is it this or that? In most cases, it's a human feedback based analysis.

Richie Cotton: Okay, so I suppose, yeah, it depends what your agent is trying to do, but if you've got something where there's a concrete answer, relatively easy to check if it got the right answer, if it's a bit more fuzzy, like it's something writing a report, then you're probably going to have to have a human read it and decide whether it was any good or not.

It seems like Most agents can need to have access to either commercial data or personal data in order to do their job. How can you make sure that this data is kept safe when you give it to the agent?

Surojit Chatterjee: we use a ton of safeguards today inside our application, but first taking a step back, this is a big concern every CIO has, every CTO CIO has, and if you are using agentic architecture. you need to make sure that your data is protected. A lot of your private data may be exposed to, like, public MLMs, and you don't know whether they are learning from that data, they're using that data, and so on.

So you need, you want to really dig deeper. What we do is, we actually automatically redact all PII, like, you know, personally identifiable information, or PHI, personal health information, Automatically from anything we are sending to any, any public LLM and we say we use a, is a combination of models. We also make sure anything we are generating, we always copyright check to make sure our clients don't get into trouble copyright issues and so forth.

Everything that we generate we code sources and explainable and so on. But I think the, data privacy is sacrosanct and you have to just understand how many agents, which agents is touching your data, how and what, what's happening. with your private data,

Richie Cotton: Okay, so it sounds like a lot of it is just about being mindful of. What data is going where and just being careful not to send it to send this commercial data to public AI service.

Surojit Chatterjee: Designed with privacy in mind. So if you are building an agent architecture, it cannot be an afterthought. Okay, what will, where will my data go?

Richie Cotton: Okay, does seem sensible to think about this at the start rather than at the end after you've built it and then you have to change anything. Do you have any other tips around keeping things private beyond just think about it up front?

Surojit Chatterjee: Other thing will be and which a lot of our customers are increasingly asking for is making sure everything is in your own cloud print, like air gapped. cloud prem solution. So it's not talking to the internet, not bringing so anything from the internet or not sending anything to the internet.

It's all in your private cloud. We do that all the time. Like you can get a, like a user open source for example, model installed, deploy on your cloud or create a private endpoint with, Azure open AI or any of the other models. And make sure Azure. deployment is not bringing in data from outside or sending data outside.

Richie Cotton: So I'm curious what happens when you have a problem. At some point, an agent's going to make a mistake. And with a human employee, most companies have some sort of procedures to deal with mistakes. What happens when your agent employee does something wrong?

Surojit Chatterjee: up front, you want to put safeguards, like what kind of mistakes will you allow? What kind of mistakes you not allow? For example, to make it real, many of our clients would say, okay, for sensitive categories, for customer support, Let the agent, let the AI employee create a draft response.

not actually send the response. Let, it be queued to a human employee. Let the human do a double check and verify accepted and sent. That's very explicitly figured out like what is your tolerance. and that may be like only 10 percent of the tickets or 5 percent of the tickets. Less than 85, 90 percent you are like, okay, I'm fine. For AI agent to automatically respond. Periodic checks, just like human employee, this is a new type of software. You have to kind of do spot checks, You have to make sure they're not going off the rail, and doing the right thing. In the end, humans also make mistakes.

So there are ways you will recover from human mistakes and you have to treat it the same way. If there are mistakes made, there are, there are, there is a course, there are ways to recover from the mistake. We are honestly entering a new realm here and a new type of software, new type of automation, new technology.

And we learn a lot in coming years. I think the nearest example is like self driving car. Today I think it's better than most humans, but it's also not 100 percent probably accurate. Once in a while make a mistake and you can take over. And humans can take over very easily. And you have to build your software that way, build your agentic architecture that way, so humans can take over easily. so I like this is a several step process there. So you're building guardrails into the agent to try and show high quality up front, or at least make it fail gracefully if it can't solve the problem, and then you want some kind of observability or monitoring to make sure can assess whether how often it's getting the right answer.

Richie Cotton: And then you have that sort of fallback of, well, if it does something wrong, then let's swap in a human as fast as possible. I'm not sure whether there's an equivalent of And AI getting fired, I guess, like if you humans do something terribly wrong, that's what happens. Maybe I suppose, yeah, the agent just gets turned off and just doesn't get used anymore.

So, uh, that's probably about the equivalent. So, we're talking about processes making good AI agents. I'm sure that once you introduce AI agents, it's going to disrupt a lot of your other business processes. Do you have any advice on the change management side of this? Like, how do you go about updating your processes to accommodate for AI agents?

Surojit Chatterjee: What we see today is most companies actually have very poorly written like SOPs or the SOPs they have written like processes they have documented are all still very few companies kind of keep them all updated, right? This is an opportunity. Now, AI employees or AI agents will go by what they can understand and learn from what's there.

Like, they cannot go inside someone's head and understand, okay, how do humans do this? So it's really important to have good documentation and keep updating the documentation. One good side effect we see of deploying AI agents is suddenly the organization wakes up and says, Oh, actually most of our SOPs are stale and it's all in people's head and it's like tribal knowledge.

They're like these three people who have this knowledge and what if you they are hit by a bus or something, then the organization would be in crisis, right? So it forces them to write down things understand the processes. But the, other interesting part here is the AI agents are learning your process and adapting to it.

Just very different than say, SaaS applications or previous generation of software where humans had to change their workflow. to adapt to a software. Like, you know, this is how software works, so you better change your workflow. So it's not so much about companies changing their processes, more about company documenting their processes and also understanding their processes very clearly.

Sometimes they, they don't have clear understanding or processes have just grown kind of automatically, organically.

Richie Cotton: That's really interesting. But then you need to be able to describe your processes in order to be able to teach an agent to do so, or to design an agent that mimics them. And it's wild how much, like how many things you do on a day to day basis that is just entirely in your head rather than being documented somewhere.

Maybe that's just me, maybe I just need to write down what I do more, but I think maybe a common problem. Okay, so, actually, I, I read a really nice blog post by Julia Wynne on, saying, I built this. Fantastic AI agent, but it took me a whole day to do to replace my existing process to build this thing.

So it seems like there's an upfront cost to creating agents in order to get the long term productivity boost. Do you have any advice on how you go about managing that initial hurdle?

Surojit Chatterjee: Yeah, I think there is upfront cost if you want to code up an agent. think one day is actually still very good. Many of our customers will say they have been trying for nine months or a year to make the agent because it's really a task for experts. I don't think it's it's anybody can go and make it on work.

And that's where our company, Emma, what we have done is. You can build this AI employees just with the conversations. We built a model with from a conversation, we can build the entire workflow. We can recruit the agents, figure out the right agent design pattern, automatically generate the orchestration code and so on.

So we make it really, really simple. And a business user can, can use it and can create. an agent, like a new AI employee within a few minutes, but in most cases it takes lot longer. Is the investment worth it? I think it's, you know, even if it takes long, it's still worth it because you are going to save tens of thousands of hours of human labor post that.

Richie Cotton: seems like there's a big difference than like, sometimes you can create something in five minutes. Sometimes it's going to take months. So you have to be a bit picky about which use cases you go for them. Do you have a sense of like what's simple to build and what the time consuming bits are?

Surojit Chatterjee: Yeah, I think building is not the the most difficult part. Making it work is the difficult part. you can probably build something quickly, but will it work at the level of proficiency that you expect from your humans in that role? And that takes time. Again, using the right technology cut down the time dramatically.

If you're building on your own, it may take for a long time, Unless you are really an expert, like a PhD machine learning or something. But if you choose the tool, the right tool, it may be way faster. And that's, that's kind of what we have been engaged in. So democratizing use of AI agents that anybody can use it.

Anybody can build one. And it doesn't take like tens of hours. it's just a few minutes and then making. The AI employee actually efficient and useful again, the approach we have is you just give feedback, just like you'll give feedback to a human employee. National language feedback, it absorbs the feedback and retrains automatically.

Richie Cotton: so, it seems like it's maybe you're gonna have to have some sort of iterative process where you build something and try and use it. And then you get feedback on where it works, where it doesn't. And just keep adding to it. So it's that sort of agile flow of continually improving it.

Surojit Chatterjee: Absolutely. It's, like a human employee. you hire a great person, but. They're great on paper. Will they be great in the role in your company? It requires a little bit of work, right? You need to give some feedback. You need to make sure they're, they're mentored well, they have all the information and so on.

And once they have ramped up well, they will do great.

Richie Cotton: Okay. So yeah, I like that. Even agents have to do some training and upskill themselves. It's not just humans. All right. So you mentioned two very different scenarios. So one is you've got a business person creating an agent and another one is like, Oh, I've got a PhD in analytics and they're creating agent.

I guess in each of these cases, what sort of skills do you need in order to make agents work?

Surojit Chatterjee: you know, in the case where you have like a PhD in machine learning or computer science you basically need to just understand you're building from scratch and maybe using some APIs available somewhere. The skill is actually today not widely available. Not, that many engineers are very good at it. 

But the basic level of prompt engineering, but you also need to understand, do you need to build a small model? Do I need to fine tune a model? What kind of agentic design pattern will be useful? You have to experiment a lot and so on. So that's on that bucket. And if you want to build on your own, it will require you to from the curating your data, understanding data.

a lot of data engineering skills to models, building small models, maybe, training system skills, building agents and making agents work together, orchestration code and so forth. On the other side of it, if you are using a tool like ours, the skill set required is minimal. It's basically, can you communicate with natural language business?

So you will give feedback to Emma and it's similar to how you'll give feedback to a human colleague or another human employee. So minimal, really minimal skillset required. In fact, we want you to focus more on the business side of it. So the business knowledge can be incorporated into. I employ very easily.

Richie Cotton: Okay. So, just to wrap up, tell me what you're most excited about in the world of AI agents.

Surojit Chatterjee: Look, I think the future will be, we'll have personal agents. I mean, I'm very excited about kind of next generation of devices that may come in, right? You know, mobile phones may have a bunch of agents that make my life simpler. Maybe booking travels, managing my calendar, all kinds of things that will happen. 

In the space we play, I think it's really exciting to see opportunity. There's so much inefficiency and so much opportunity to address that inefficiency with AI agents next few years. it's really game changing. I think we will see real kind of boost in productivity across the board in economies that adopt this technology faster.

This will be like the next, internet or next computing or maybe kind of almost the next industrial revolution.

Richie Cotton: Okay. That's big talk. It's like the next industrial revolution. So, yeah,

Surojit Chatterjee: Yeah.

Richie Cotton: Well, yeah, certainly exciting stuff. And that's an interesting idea of incorporating agents into things like mobile phones. So everyone's got them on hand at all times. super. Thank you so much for your time, Surojit.

Surojit Chatterjee: Thank you so much.