Good morning. Thank you for being here.
There is no shortage of talk. regarding its crucial potential.
I'm excited to move beyond that hype. And during what we have actually done here.
We have created a system. That is designed to support our project managers, PIs, and dual-time statistical methods.
My goal today is to focus on the theory and practical solution.
So, I want to keep this high level analysis.
how this goes after it works rather than going to the code.
So, just a heads up. This presentation is a global presentation which is a lot of sci-fi visuals to help demonstrate the data journey and the future we are building.
So in today's talk. With the AI come later.
many good life. We will look at the specific problem.
that the BIA can be financially. and independent and the methodology which I will walk through.
And I'll be actually done from the raw data into actionable insights?
Then we will look at the… And then the code of the AI engine, the IT infrastructure, and how we optimize the model.
And finally. framework we use to ensure our AI is fair and unbiased.
Before I start, I want to say that this implementation is a direct result of the operating system, and it has been a great opportunity.
To apply the training to our department specific challenges.
So about the business problem. We are looking at one of the specific project management model.
No, not the resource management. Since 2020, we have maintained.
detailed history below. And today, we are tracking over 200 active issues.
That is almost 17,000 words I've written down in.
Currently, our project managers, PI, growth package managers are maintaining this log as well as driving the project delivery.
at the same time. So when I then had to manually.
go through all of this. And decide what Mr. Immediate attention. It creates what I call as the parietal gap.
And this gap has got two major risks. So one is the latency.
Um, this is a typical delay. This is difficulty about, um… about the issue being updated and stakeholders actually see it.
In the multi-million project, time is a luxury which will always go high.
And the second most risk is subjectivity. We are often seen a class of perspectives. For example, a growth party manager may naturally see an issue in their domain a top priority.
Right? The project manager, looking at the project launched, may clarify that issue as a medium or locality. So without the standards.
We do not have the ranking become more inconsistent. Therefore, our PIs and project managers need to see the big picture instantly without spending… I mean, without spending too much of.
No, um… Okay.
So, that challenge we are struggling between that gap.
transferring all the technical manual logs into an immediate actionable.
Okay.
This is the entire map. AI TikTok.
The model we use the AI model is 12.5 billion. This isn't a simple question and answer data, which are multi-stage hybrid pipeline where Python and AI work together.
We can do the workflow displayed in 15 stages. One with the data from any extraction, and it used to be analyzed test.
I'll talk about this too in a moment. What made this workplace pressure is how different groups work together.
We have a Python J. Uh, managing the high precision tasks, like pulling the data.
I do external and data privacy marketing. Meanwhile, the AI handles the language that is the attraction, the utility and the creation and sectorization.
They're all familiar with each other. The entire process is controlled by the front model.
that defined the rules of engagement, telling the AI exactly how to think to ensure it means our specific challenges.
data privacy market later on, but in the next few slides.
We will drop through the drum and the real engine and the disket.
Now we are looking at the plumb. So, if you look at the map, what we just saw on last slide here.
So this is a blueprint of an entire system.
These people are caught playing by putting all of the rules or prompt into this very young.
This meant our system very modular. So, if we need to all get anything in the background or anything related to the, you know, the instruction, and we only update in the prompt area to the left remaining the same.
But yeah. attracted the wrong.
We used example to teach the AI. also known as the fine-tuning.
And this gave the AI exactly what item to look for.
But if you have the analysis, that is the gut model, we provide a clear instructions so that the AI knows what to do.
And Trump is… the prompt is split into four parts. The system prompt and the user prompt, which I'll show you in the next slide.
the system prompt defines the AI program and how it needs to act.
And the user program is a set of tasks that tells API is what to do.
So what made this area unique is the helper function.
We have built into it. They work like a system. Before the AI looked into the world, these tools.
extraction, cleaning, and other tasks like privacy marketing. And once the AI is done, these students wrote the project data back into our database.
By doing the heavy work of formatting here, we allow the AI to process only on the region and maintaining laying on campus.
So, to make model work, we balance the AI with our local hardware and our data types. First, we used a method called quantitative or optimization, which runs the model at a reduced size.
We've helped the system to run faster on our local cluster.
If the mathematical way of making the model smaller. So it uses the less memory while giving the high qualities.
The raw data comes from the toaster. Um, and what will be the function?
And so we are on the other side. We have the prompt that I didn't talk about earlier, victim under the problem.
So to get that. We are… we have the coordination layer that we look at.
And this player, I thought that it will generate a report.
So, by keeping the rule top right under, we have created a system that is far, fair, and ethical.
So we will look at the third one, and we are equator.
So what will be the end lifetime for these main entity recognition?
So this is the area, the AI will that automatically find the label for key information from the raw technical laws.
into a defined type of things that you like to normalize in entities.
So, under using our tool. We move to the middle area, where we do not look only the worst.
We are finding relevant data. By the good mean then.
really done a lineup L into an ordered database.
So, why are we doing this? The reason is contact database.
Which we don't really have. I mean, we have a lot of documents, we have a report in the past, but they are not easily searchable.
This book, we are building a base for the social knowledge graph.
Yeah. So this mixture of the data we find in 2020 or previous data says this filter PI and PM in 2026 and beyond.
We went on to return to… Once we have the tactile data, we must call the project management time.
from the prong, we have a defined setup instruction telling the AI what to predict between 1 to 10, depending on the nature of the issue.
And this is happening fast during the harvest and harvest up manually work.
In the past, ranking them was a matter of opinion.
So one percentage tells you is a high risk, while the other says. So our AI chopped up by us.
If you look at every issue on a clear 1 to 10K.
But we do not drop the AI blindly. We also will be a guardrails.
within the… our road, we have mandatory minimum. For example, if the AI finds a risk to a milestone or a lockup.
Lots of each job. It cannot just blur that debt level.
This is how we put our expertise around AI and logic.
The one thing you need here is the AI will look.
Only, uh, about degrading and the reasoning. And the Python would do the math.
So I think making the entire project faster with minimum memory and of any component requirement.
So, from this, we have 3 distinct regions. One is the boat.
The other one with the project manager will look at the implementation summary.
Oh, right? And for the important focus. We need, uh, the check summarization.
Now you are also the next pillar, the IT infrastructure and optimization.
So, our current infrastructure business. It runs on our local cluster, it uses the power to execute like NVIDIA 100 or Tesla 300.
So you have this tool using condo system. Which manages to work across the world, neglecting the common voice.
If he popped up this… The data leakage.
Unlike the consumer AI tools like ChatGPT or Google Gemini that generic data to the cloud, our system is entirely local because our logs are sensitive.
We went to know where they can't believe. Uh, internal network.
To help models run fast, we have reduced the time earlier, and we have to project the data in batches.
So, instead of making the model too heavy, you can clean and pack each.
We aim to build an AI application that runs completely through the cloud architect model.
By integrating Python kit to manage the logic firmware and to coordinate complex workflows.
We can leverage the technology. So we have the question a lot of active that is managed by the group called AI for quickly does it run by Jen and paying.
So we have the advantage of this is the rapid development. So you can scale without managing the physical hardware and reduce competency.
and allowing the developers to focus only on application logic.
Rather than being positive. So we have accumulates here?
One is the alpaca and the Rituna. The first one is just like a ChatGPT during Gemini and concluded the general model for.
What? Like for you. Yeah, the only thing is private and free, and it needs to pick up your login, and you can wait.
Oh, for the second link, if you want to run your own AI application on the fly, you can use the check-in link that is written now.
So we can join our group. Um, just, yeah, to find out more.
So just want to highlight that this tooling was qualified network.
Now we are at the final pair of the talk, Model Validation.
During the administrative stages, we have to decide which AI model to use for any adoption.
So, our first key indicator was the token group.
So, you will quickly put the token or the basic unit of test, or finger match or still available or fragment of words that AI used to process.
and generate the language, mathematically. So you are actually in your question on the three models.
So, when they take them out to… And the last is 22.5 that is about 14 billion parameters.
So, I couldn't give a total output.
Goodbye. Okay. So tutorial output of them… It must have done correctly.
And if it's important to clarify that. This higher broken output points when comparing with other models such as meaning the model is highly proof of models we do the heavy lifting. This is actually taking time to extract all the learned categories.
And provide a detailed reasoning will protect in our prompt.
And this is what we are looking for, the AI to understand both and how we work.
And this is where it was definitely chemical memo.
Despite being a 12 million parameter is the 10…
It may be taking the short term. or likely typing complex entity or maybe failing to provide an analytic.
often fails at this level of complexity.
And we will look at the model performed on different categories in the next slide.
So, it looks like a total number of categories, that is 11 categories, so to attract them seem to be very high. So, for those organizations or financial, etc.
And it is only about… for the opportunity to come.
And this is pretty detailed. If you look at the settings that go.
So, when I talk about the technical term between… Like, if we appreciate it or PCP, the specific particle is done.
So whereas the plan is able to tap all of the, to be able to follow up what we want.
price even for a brand.
And due to the gender. The total crop is about 500,000.
And the and we don't have any problem. The GPU infrastructure. We had a maximum output utilization and the average.
uploaded on 200.
Yeah. So here we are talking about the test tool, the GMUT story.
So, output of our analyzer. So, out of 211 individual issues, the AI has applied this logic and given its objective distribution. So if the 37% thinks about the high.
Priority product manager, and then they will get to decide whether to look about this now or later. These are the very low issues.
And… But it is something interesting happening here. We have something called unknown. So in the front, we have mentioned that if you don't find anything, so please let us know. So this is what has happened here.
It's like part of human power type. workflow.
So, talking about these methods.
That is because there is a problem with the AI and the second term we can understand. So you need to go back to the prompt and check it again, and then come back to it.
I understand this. This will have some tickets you can verify, but it is faster, so we need to check what is actually happening.
about eight minutes. So all the checkout being projected.
So in the… Second thing is, we have the AI running the two realities, extraction and the customerization.
So, this is your total amount, but what has happened at every level?
And the customization for the reporting purpose is, uh…
And very interactive and very detailed explanation.
Now, let's talk about the AI edition. We do not want to pass AI. We also want the AI to be fair and unfair.
So, the AI, the whole system used this with the entire system is back.
So how do we know that AI failed in one project or the another during the analysis?
How do you know if AI is domestic? So, without the AI decision to come out.
We need all these modules. Other privacy policy.
So, given the area which we are currently infested, the first thing is women in the room, like we have seen that it has detected some issues, so we need to find out. So this is the first step.
One of the things that to make sure the business and then you need to take a look at making the proper exam.
We do not let the AI go wrong, and we have to make sure.
There are some tech involved. As you can still need to become a person before.
And to make AI cheap and fair, we are using a… Python library and testing the environment of AI and models.
It is very powerful, we tell your child to see the balance between the unfairness. We can also generate reports and pick the best ways to stop any bias we find.
This ensures our system is stay neutral and treat every project with the same level of skills.
And then you have the threadback by the government.
And if I got the guidelines and what about what and what not.
You have to go through the lane and to understand how.
We found that everyone will understand compromise. And we have to download and use DLV.
We have the questionnaire. Where do we have the about 100 to 200 questions. We need to go through them with all the decoding of the project.
making sure all activities are met. And this, I mean, one of the reports that we generated from different lab. So the first one is called the past success rate. So one of the projects for the.
Because we have about 93% that we have team, or 2%.
The other one is we wanted to step with a… With that being completed or not, and whether there have been issues or not. So, this interactivity idea of what's happening, which is like a module.
And this is about the hallucination risk. the disruption check order things like the power supply and the error in validation.
And then we did the total, what is happening and then finally, he gets some sort of running the dictated one. So that means you have, there is some problem.
With the structure of the drug. So we need to check what happens and emerging from the detection. So that is what we are taking on.
And you do have the data governance report due to update project.
So finally, the conclusion. This is the roadmap.
What AI application, or in the pipeline, in the project office.
When they left German, you have something called a categorization.
where we start with the financial data. provided by universities. These universities have Dr. Old Forma or template.
So we need to tackle items like the effect of money or travel. So this will help detail project management, project managers, how to start manual work.
In the near future, we plan to… we aim to build the lessons learned.
building up. Um, so this will help us… Not only what a two-step project, but also to understand what, if anything had been missed. So, we can use it to study.
Lastly, in the long term. We have the potential for question and the opportunities are the generation, and this is what we are trying to build up.
So, this ends my today. Yeah.
Yes, please. It's. I have some questions.
So your question about should be talking about different models and with the altitude more tokens and made comments about making a decision on the quality of that.
How do you determine that more is better as far as the topics are concerned? Like you mentioned rattling, how did… how are you able to check that it wasn't just… Excuse me.
Andy Morden. So we see the harmony, um… The output that is driven out.
So then we look at the output of every what we want to to the region.
So based on that, we checked with the other model.
And then finally. later on, like, about the tokenization.
to think about how we can use it in the future.
They've got these different models, and… See in some cases, they behave in a biased way at least to check.
Yeah, to be testing these models further. So I see you have this fairness check software. Did you use that only to be quite free, or were you able to look at the others? Because I think it'd be interesting.
But again, you didn't see any… Did they all perform similarly, or was ready out loud? I think it depends. If you look only from the… But in the Finland, you have different family type of function where you can check like hallucinations within the model, only the same model.
So, there are various types that we are looking at this, so we can see, like, um, the type of token that is being used, or if there is any hallucination, or if there's any other things, like not the.
For example… We will collect a name and we have the urge to have the AI is thinking about it. So that then we can do all that kind of stuff.
and you didn't see DC insignificant differences between the models, or… But I'm not similar. So when you talk about the, it's not much difference unless…
In the second chest, we start a little bit of, uh.
different. Like, actually, gamma model is having a good quality compared to being the type of data set that is being trained on. But when you compare with the 12 mod, um.
If they doubled, like, slightly better. But we actually talk to you more about it later. I mean, we have different type of, uh.
We're gonna have to make a pair that you have another 10, like, uh, AI4. So we can do some testing on stage as well.
I… I didn't quite understand how the validation works, where you're… from the point of view of the… um, human, in the loop, in that, um, is the, um, are the… do you have then people?
checking, or experts checking, um, that the output of the model.
For a subset, or, I mean, how do they… because they… if the people can do everything, then you don't need the AI. So how do you…
So, we have the… So for every call that the AIM or the BI will read that explanation journey. So that is a woman in the law.
So, for example, I mean, they don't go through all of the issues. So our ITI, we want to look at the top 10 issues or top three issues.
So, and that we filter them out, and then we will look at the explanation generally.
So, this is where the movement will look happening. So the PI will check that summary. Yeah. If it's marked something as not critical.
Yes. How do you check? Do you have some fraction of those also checks that they are, um, that they really are low priority? I mean, we had that incident happen because one of the actual issues is actually important, but.
If I go into very low. So that is very confident and help us to find out. I mean, there's some… there's some issue with the structure. So there are things that's happening. One is the problem with the changing format of the front.
And the second one, if you remember the product module, we need to define them.
You need to highlight what are the problems that we are facing, and we need to define clear. So then, for example, we have a setup example to highlight the bucket. For this issue, it needs to be the highest score.
So it's a PI or a PM. Okay, this is… the calculation is wrong. Then we go back to the prompt, and we add a jamble back to the… Um, but the problem. So then again we run a retexture model again.
Is that how it works. Yeah, what fraction… of your of all the things are checked by human.
At the moment, uh… And so far, the field project managers are doing is one of them because of their own checks.
Yeah, I mean… Because since we are implementing this for the first time.
So, they go through all of them. But when the next time, when the move is to get our data.
It doesn't run the whole thing once again, but it took a mean issue, and it's due to reports on that particular new issue.
So, for example, the students have added up, like, maybe two or three times in a week.
So sometimes it can be a lower issue at the highest kick running whenever the utilities are up and reduce the prompt in between.
to, um, at least we set up the same type of.
Every Friday to run the issues, and give a very short report saying that, okay, for this issue, what has happened this way, these are the problem, uh, these are discovered. And then we compare it with the entire data chain.
Thank you. Yes, sure. Oh, yeah. Sorry.
Do you get… do you get re… okay, I guess it's too popular. Do you get reproducible?
Results if you run this… Twice.
I mean, do you mean that, uh, whether being… So you run this loop, and then you get your text summarization, and your explanation summary. You do that the same twice on… the same input, do you get the same…
I mean, it runs like, uh… first, it's run on the experiential thermal.
I thought you knew my isolation with one deer explanation summary.
We'll come back to the test summarization. You have some set of things you take, do you get the same result if you do it twice?
Oh, okay, got it. I mean, when you run it, yes, yeah. And because that is where we define the program. We make sure that we… that we highlighted that mixture that doesn't change every time.
So you did that test before. When we get a generalized prompts.
And the general change in maybe 2 or 3 times. But then we defined, we made a series of checked up prompts. And then when we ran for the second or third time, it remained consistent. Unless when the project managers.
Because the issue doesn't maintain for the entire project. So it keeps changing. But maybe after two or three weeks, they update the issue. So again, it launches, and then it also highlight, I mean, we wrote the prompt in such a way that it also highlights what is the previous code.
And this is the current door. So that is one of the steps.
Thank you for that idea. One of the checks that we are going to put. What was the previous, and if there is any changes that have happened. The kind of follow-up question, so… If you run this on an experiment like G. So gene is a publicly.
There is public information out there right? So there are articles of new scientists saying June is a deeply troubled project for, you know, 3 or 4 years ago. So in that sentiment analysis goes into the Llan, which then thinks.
things differently about that compared to, say, Hyper-K or, you know, Atlas or CMS, depending on publicly available information that it has. So if you try changing the experiment name? So you could change it to a generic one that doesn't exist. You could start calling it good experiment or bad experiment.
It actually makes a difference to your output. Good. Um… To try to avoid that. This is one of the fair Python model.
It has one feature that… We can find out whether the AI is biased or not.
One of the things that I want to touch in on. But the second one, again, back to the problem that we need to make sure, again, we define that.
particular, uh, prompting that, okay, we need to follow this.
Do not, you know, do not provide any other… idea, apart from this only.
There's a difference between purchasing the property knowing that the AI is, uh.
is actually doing. I mean, I always… You can send the prompts, be fair, but I don't know if you can verify that it's being fair without trying to trick it somewhere.
So, in that case, um, we… when we look at the result from the, you know, the PI or the project manager. That's where we look at the customerization.
Um, so we try and to retain any change in the behavior or something like that.
So that is one of the text that we look.
Yeah, alright. Yes. That was sort of overlap.
Sorry, this is a very specific question. If you're running only PPD AI machine that T has set up, I understand.
What sort of times do you run this? Because I'm also using this one.
So it's different. Sandeep's running these as condo jobs, right? Yeah. So they get submitted to the same type of machines. But because this machine is taken out of the queue, they're not competing. They're competing with other people submitting GPU jobs.
Okay, maybe I should do this. I'm just wondering whether we'd be competing.
So, uh, that's quite interesting that these are run as they can run on many machines, can they? I mean, we haven't only set up for 5 GPUs, so what I have done is, uh, in the bundle and did job.
Like every Friday about six o'clock in the evening at the end of the day.
So and I've tried to see if there is any other time.
I try not to interrupt or distract related to lab experiment or anything. It's a 12-minute job. I don't think it matters. Yeah, it's a 12-minute job once a week. Not a week.
Okay. But I think because of the condo job, it runs based on the priority.
Maybe. Sorry, they're just wondering whether that seems to be quite.
If you're always running it on a Friday evening, is that the best time to get… um, results, which people then don't have time to look at them until Monday. Normally, then… But the last week of Monday. So we have the system set up.
Like in this chat, you will be updated on Mondays. So, we collect all the data.
But it's happened until Friday. So, at the moment, nobody's looking on Friday, but the reports that we generate, it goes to the email, and they're looking at whenever… Um, and they have gone.
Um, yeah, so going back to the issues severity analysis stuff.
So the main purpose of that is to generate this summary document, is it, that would then be read by PIs and PMs, or is that the… What do you call it? Where is that for the task managers or work stream managers as well?
It's different. I mean, normally just… we call each other project stakeholders, so if you are involved in that.
are particular about package or something like that. So normally, this project managers will work along with the PIs or practice managers, and they coordinate with each other. So… What about the project manager? Is that information will be shared, uh, corresponding? Yeah, so, because in that case, yeah, so I work on one of the workstream managers for the CMS upgrade side for the hardware.
And so there we often we have chats with Lay every every month or two to update on the issues.
Um, and then we also update the risk register as a result of that, but then… What I was thinking was that at that stage then, we're effectively ranking the severity of each of the issues in the risk register itself, right?
Um… So then, yeah, we've kind of fed in all of the information. We know all of the information. So the main purpose is then for the DI to be able to read this as a summary, or.
Yeah, I'm even trying to think how it fits into the… into the workflow.
Yeah, I mean, that is what we plan at the moment, particularly the treatment because.
the project managers are focused more on the project and the strategy. So they are handling it by the time. At some point, we will work using AI on the risk register as well. But we have it in our primary software.
We have our own Monte Carlo analyze it and where did they fit into the information, and they calculate, and they come back to it.
So because sometimes the issues are long or it changes all the time, very frequently. So there needs some shut up.
happening and then they decide what happened with something. So normally, I think some of these tools, we update the risk register or accurate so it's like… Okay, but then the future change that you mentioned around the AI and the risk register, would that be the one of these AI setups taking the risk register, the project.
We're extremely… the workstream managers have… Uh, updated with the project manager, and using that as input, and then using that to do some kind of analysis of likely future costs.
Yes, um, when we talk about the future, I mean… I was expecting my… Yeah, because… We take very tentative management. So we need to understand a bit more. Okay. Um, so once… once we get comfortable properly, and then we start working on the concept of AI model.
Okay, okay. I can agree to start to come to know about it. This is one of the pipelines that is happening in the future or not. So at the moment, we are looking at the gap.
That's a good idea. You need to analyze the data, the financial data.
So hopefully, maybe after this, we'll be looking at the risk register.
Okay, and that would be to to rank items based on the risk register. We do have to already we have to talk about software that will automatically calculate. But.
At the moment, uh, the project managers, they're feeding the numbers. Yeah. So… Okay, when when we collect that information.
At some point, we will identify the prompt and then make it work in other way. Okay. But you need to be a little bit careful telling that is about the risk. And because.
Yeah. So we need to find out like how they work together. I mean, to China, but it starts becoming the big pipeline. So we need to.
We need to look on that. Okay. Well, yeah, I mean, in a sense, I would thought the… the risk register is easier to analyze because it's effectively just a few why I remember is that it's a set of numbers of the probabilities in the cost ranges.
Uh, associated with risks. And so that's already quantitative, whereas.
So it could just be ranked based on that without an AI. Whereas the issue is the text, so… Maybe lend themselves more to a textile. Yeah, and one of the factors is the Monte Carlo.
Uh, which we haven't started working on that yet.
So we need to understand a bit about the Monte Carlo, and then the AI path.
Because… You need to run the model, uh, for the Monte Carlo iteration, like, and so this is where you don't really understand that Monte Carlo analysis from the AI point of view.
but would would you consider replacing that Monte Carlo analysis with an AI one instead?
No, I mean, if you are talking about the normal isolation, that can be done.
But we've gone to Montecoblo as well because. Because we want to, uh, let's say, unlike a delayed path, and detection, or the source, and what permit probably they are 50% or 90.
The running embedded pipe legend is working HGA, but when you're… to application of Monte Carlo is where it requires a little bit of detailed understanding. So which is why we are relying on our primary software to do that sort of analysis for us at the moment.
So, at the moment, that is why we are manually fitting into a training now.
Okay. We have a raised hand on Zoom. Chris or Christopher would like to speak.
Yeah, so I think… Yeah, it was just about the question before about potential bias on the sentiment analysis. Say, say if a project has got into the news. I think my understanding is that this is a completely closed system, so wouldn't actually have access to the new. So this wouldn't creep in.
Is that correct, Sandeep?
Um, so… So the the question was, depending on when the model was trained, that knowledge is part of the model, because it might have been based on text that is out in the open.
So, once it has no access to external systems and can't query the web, it might have been trained in that way beforehand, and it's a bias that is inherent to the model, but then needs to be the you need to figure out whether it's in there or not.
Sorry, paraphrasing. Yes, Chris is very happy.
Okay. Oh, we see here. Okay, so. Are there other questions?
I have one last question, maybe. I was curious how you measure the… how you quantify the input data. And I yeah, that's we lost this line, but… I think on the 1st slide or second one, you had the.
the number of words, so could you explain a bit what the meaning of that that is?
It's looked like a little number of words, but maybe it has another meaning, which… Yeah.
So what is this amount of data that you're using?
Well, that's available at the moment. Yeah, at the moment, we have.
When we work on the project, we entered all that information.
For example, there is true with an history or anything. So it attracts all of that information and it counts all of it. And we trust it for the.
I'm doing something to… So that is the input data from the resource log.
So you only use the issues now? At the moment, because we are focusing on the issue of management. So this is going to a specific model of the project management. So this is the data that we've got. It's on there.
This is only for the open distribution, so we have no issues. It is more than 1,000 or something like this is already open to about 200 pictures. I've got the entire project we are managing.
Oh, I see. Okay.
Okay. Thank you very much. So let's thank Sandeep again and we close.
Yeah, we explain.
Lots