A world of benefits.
Data Science utilizes scientific methods, algorithms,
and tools to extract valuable insights and knowledge
from data. Astrid team employ techniques like data
mining, machine learning, and statistical analysis to
extract meaningful information, drive data-driven
decision-making and build AI models.
Integrating data science techniques into Blockchain,
organizations can unlock new opportunities for improved
efficiency, enhanced security, and advanced
decision-making.
Data Science can predict trends, minimize risks while
maximizing returns, increase UX on dApps and allow you
to save gas fees. Let us show how we use Data Science
and how we think it could be useful for you and your
product.
How does Data Science works?
Data Gathering.
We extract the Data related to your request. We know all techniques available: fetching nodes data with Web3 libraries, making HTTP requests to servers, using SubGraphs or building crawlers. We’ll then store those data on any kind of technology, from centralized to decentralized ones.
Data Processing.
Data extracted need to be processed before applying any kind of algorithm. It means to normalize Data if they will be used by a Machine Learning model, for example. Different processing steps are applied based on the kind of analysis conducted.
Data Development.
Once data are stored and correctly processed, they are used to conduct analysis required. It could include development od Machine Learning or Deep Learning models, extraction of key-metrics or
How to implement Data Science into your Blockchain service and increase its value?
We conducted researches on several kind of Blockchain products and the most interesting results obtained have been discussed and published into Medium articles. Let us list some potential implementation with respective links to medium articles:
Automated Market Makers: The AI model provides optimal
liquidity allocation recommendations or strategies for
different trading pairs within the AMM. This includes
suggestions on how to adjust the liquidity pool
balances to improve trading efficiency and minimize
slippage.
Check AMMS liquidity optimization article here.
Lending Protocols: This is another euristic approach
similar to those ones used by risk managers like
Gauntlet. We won't use Multi-Agents simulators but
another approach based on Machine Learning to optimize
the borrow cap (i.e. minimize risks, maximize borrow
limit).
Check Lending Protocols BorrowCap optimization
article here.
NFTs: In many kind of dApps, the evaluation process of
an NFT worth is really hard. Using AI, we can
correctly estimate how much user is disposed to pay to
buy an NFT in a given market condition.
Check NFTs price evaluation article here.
We would love to hear your new idea.
Contact us and let’s discuss about it.
Thank you for contacting us, we will get back to you shortly.