Implement Data Science on
your Blockchain service.
See it growing day by day.

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.