Sigmoidal Success Story
Holders after the implementation
What was the business problem?
Investing in crypto can often be quite overwhelming, especially for inexperienced investors. When looking for new opportunities or deciding whether a project is safe to invest in we have often left guessing, not knowing how to properly research the investment.
A client approached Sigmoidal to create a platform, which enabled its users to determine the reliability of specific tokens.
What savings and profits does the client achieve?
Sigmoidal delivered the AI Safety Audit platform, an industry-leading auditing tool for scanning tokens. The solution focuses on the research of social media, the team, and the use case of a given project. The safety audit tool provides all the important financial details of a token at a glance. The first thing we see are the taxes associated with buy and sell transactions. Fees associated with buying and selling are standard for most tokens on the Binance Smart Chain.
Through our proprietary work methodology and Sigmoidal Knowledge-sharing technique, the token created by our Client reached multiple of the initial ATH.
How did we accomplish it?
Sigmoidal engineered a Deep Learning-based platform enabling users to assess the different indicators of multiple CryptoCoins in BSC. Our engineers created an AI engine enabling tracking and analyzing different tokens across the network. The engine has a unique self-learning ability to constantly adapt to different market shifts. We based our solution on natural language processing, tracking signals and sentiments throughout social media platforms like Twitter and other websites. Our engine process the information to extract some crucial numerical data. There are a lot of parameters based on which you have to predict the token reliability:
- Number of transactions
- Liquidity pool
- Number of token holders
- Information on token creators
Acting upon this data, we employ full forecast and analysis. Sigmoidal solution performs full-spectrum analysis based on collected signals and sentiments, using complex deep learning models to analyze the source code of the tokens smart contract.
How did we boost the project with ?
We employed our Data-centric approach, preparing precise datasets defined iteratively with our domain experts, enabling us to use appropriate machine learning patterns.
Sigmoidal minimized Total Cost of Ownership, as we treat data as a integral part of the project. We set needed infrastructure and patterns early on, resulting in lower overhead cost of the project.
We deployed a machine learning model simultaneously as we built a scalable framework to support future modeling activities. This actively enabled us to construct complex solution with multiple features to be added in the production environment.