How Persist AI is Preparing AI-Ready Data with Scispot

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How Persist AI is Preparing AI-Ready Data with Scispot

In the dynamic realm of biotech, the success of research and development relies on effective data management, streamlined workflows, and harnessing cutting-edge technology. Enter Scispot, a transformative platform that has revolutionized the landscape for companies like Persist AI. In this blog post, we delve into an insightful interview with Karthik Raman, CEO of Persist AI, as he reveals the profound impact of Scispot on their journey to success. By leveraging Scispot's configurable data and workflow management tech stack, Persist AI has eliminated data silos, enabling seamless visualization and machine learning readiness. Through AI-driven automation, Persist AI has disrupted the industry, drastically reducing the time it takes to develop long-lasting drug formulations. With Scispot's user-friendly interface and advanced features, Persist AI has accelerated their progress, fueled scientific advancements, and paved the way for a future where innovative biotech solutions thrive.

See how Persist AI streamlines their AI technology with Scispot's R&D software

Jessica: How has Scispot’s data infrastructure helped Persist AI become ready for artificial intelligence and machine learning?

Karthik: We collect tabular data, assay data, data from images that we’re taking, with all kinds of instruments, and all kinds of sources. The nice thing about Scispot is you can put it all in one place and you can cross reference it. So, if I do an experiment on one day, I can easily refer to data from a different day. We can link our experiment to our in-silico data, and the physical lab environment.

When thinking about where all of our materials are, for example, are they in the minus 20? Which shelf? Those kinds of things that maybe don't matter in a traditional information management system, but Scispot makes it easy for a bio company like us to utilize. In Scispot, you can separate data saying this box has X sample and it's in X location.

Jessica: Please tell us about your proprietary technology and how you are leveraging AI.

Karthik: Traditionally it takes pharma about five years to develop a long lasting drug formulation. We use AI driven automation in order to reduce the time it takes Pharma to build these long lasting injections. The goal is to reduce the overall time by about 50%. Drug formulation is like packaging, pharma companies will develop drugs, and then those drugs then need to be packaged in a way that can be delivered inside the body. The kind of package Persist AI develops is a package that you can give as a subcutaneous injection, and then it will slowly degrade in the body and release the drug over time.

Imagine instead of going to the doctor every week, you could go maybe once every three months or even once every year. It is important to test how different polymers and other additives change the rate of drug release and how much a drug is actually inside the package. To do this, Persist AI physically uses robotics to test a bunch of different packages with different polymers, and different additives. Then, we train our AI to use the patterns that we developed to predict how we can do this faster and faster for the next drugs that Pharma clients give us.

Jessica: What were you doing before Scispot and how have you improved since adopting our platform?

Karthik: Before adopting Scispot at Persist AI, we used Excel. When you’re a small company, you use Excel and Google Sheets as much as you can, but it only works to some extent. With Scispot, it becomes a lot simpler because you don’t need to manage multiple Excel sheets. You don't have to worry about giving your entire team permissions to modify each Excel sheet that you're working on. Scispot provides an all in one central hub and all permissions are managed through the Scispot platform.

Jessica: What made you choose Scispot versus other platforms?

Karthik: Excel might be the go-to choice for a company that consists of one or two people, but it presents some drawbacks, such as the lack of version control and audit trails which often result in multiple, unmanaged versions.. Well we needed a solution that would be able to handle more than this. Scispot is perfect for a company like us, who is growing and needing better data and workflow management tools than just Excel. That's why we chose Scispot.

Jessica: Have you noticed any changes in your R&D process since implementing Scispot?

Karthik: Since implementing Scispot things are way more organized. So, you know, every day we do a set of experiments, the experiments reuse templates of protocols that we've developed. All of this goes into a central lab sheet database on the platform. You can have different databases for different use-cases, which is helpful because some are customer facing, and some are internal facing. There's automated data transfer between different lab sheets, and there’s also automated ways of cleaning up our data. All of this was previously done manually, but now we are able to do everything within the Scispot interface which has made us much more efficient.

Jessica: How user friendly is Scispot? Did you find it easy to adopt or did you face any challenges with it?

Karthik: Scispot was very easy to adopt, especially with the help of the team. The team is amazing. We have access to a dedicated scientist through Scispot who helps us whenever we need it. If we need any additional help, the resident scientist will bring in the CTO to help us and figure out the workaround for how we can improve our workflow, as well as what future features can be built on Scispot to help solve our problem.

Jessica: What specific features of Scispot do you find most valuable?

Karthik: Having a central database for all the information is very valuable. This allows us to store and organize all of our experimental data. The nice thing about Scispot is you have the structured lab sheet kind of data, but you also have a simple doc where you can put unstructured data, tables, and other things. 

Scispot has also built in platforms like Python, Jupyter notebook, and others so you can automate some of the tasks that are more repetitive. There's options for import and export of data, so it provides a great ecosystem for us with many features. It's hard to choose one specific feature because we use so many that are beneficial to our work.

Jessica: Have you been able to identify new insights or patterns in your data using Scispot? How has Scispot made your data more accessible and easier to analyze? 

Karthik: The Scispot database is meant for human readable information. There's a lot of data that we collect and it was previously difficult to develop patterns without using some Python. However, through Scispot, we can use Python to analyze the Labsheets database and develop our insights. We also build our ML models on the Scispot database through the API that exposes Labsheets.

Jessica: Have you experienced any cost savings or efficiency gains since implementing Scispot?

Karthik: Since implementing Scispot, there are definitely less mental challenges. It's great to know where everything is supposed to be when you use the Scispot program. Compared to managing all the data and workflows manually in Excel, it is much more efficient. In terms of cost, if you compare the price to any similar platform which includes traditional ELN and LIMS systems, many are designed for big enterprise companies, such as Benchling, which can get very expensive as you scale. With Scispot, it provides an affordable platform for us which still has all of the features we need and can be scaled in a cost effective way.

Jessica: Would you recommend Scispot to other AI enabled biotech companies? Why or why not?

Karthik: As a small bio company, your options can be limited. Scispot fills this critical gap between being a 1 person company and a 100 person company, where you need more space and tools for your workflows, but also need it at pricing suitable for startups. When growing fast as a small company, it is important to have access to a team like Scispot’s to assist in your growth and make sure you are able to use all of the features to the fullest extent while scaling your business. So the answer is yes,  you should definitely use Scispot and it will help you grow. The Scispot team works with you as you grow and the team rolls out new features every week.

Jessica: Can you elaborate on what type of impact you see after digitally transforming your entire R&D workflows? How will this benefit your company over the next few years as you continue to grow?

Karthik: What's interesting about our field is that currently most of this is done manually. We are one of the few companies applying robotics and AI to this workflow. As such, we are able to gather a lot more information. Everything has to be digitized in order for us to create the kind of AI driven insights that we want. Digitization is everything for us. There's no manual, so we have to use the Scispot database as much as possible to capture all of the information we're gathering.

The Scispot software is nice, but the team is even more awesome. It’s so great to have a personal scientist to help us solve problems we face. It is also great knowing that there will be more features coming out to enhance the platform and help support us in the work we do. We are very happy being users of Scispot. You can see, we've slowly expanded how many team members are onboarded, how many people are using the program and how often we use the program.

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Scispot's Impact on Persist AI's Success Story

In this insightful interview with Karthik Raman, CEO of Persist AI, we have gained a deeper understanding of how Scispot has played a pivotal role in transforming Persist AI's data management process, workflows, and R&D efficiency. By leveraging Scispot's configurable data and workflow management tech stack, Persist AI has successfully eliminated data silos and streamlined their operations. The integration of AI-driven automation has enabled them to revolutionize the development of long-lasting drug formulations, reducing the traditional five-year timeline to just a fraction of that. The centralization of data within Scispot's user-friendly interface has significantly improved data accessibility and analysis, empowering Persist AI to uncover new insights and patterns. Furthermore, Scispot's cost-effective platform has provided Persist AI with a scalable solution tailored to their needs as a growing biotech company. Overall, through their digital transformation with Scispot, Persist AI is set up for continued success and growth in the years to come, revolutionizing the field of pharmaceuticals with their innovative approach.

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