Footprint Analytics is devoted to making a structured knowledge platform that bridges the hole between Web2 and Web3 knowledge.

We concentrate on structuring knowledge. Regardless of the relative benefit of Web3 over Web2 in clear on-chain knowledge, sure challenges stay. These embody the nascent standing of the trade, a scarcity of standardized practices, and a scarcity of organized knowledge. Because of this, knowledge software turns into problematic.

For example, contemplate the situation the place you need to entry transaction knowledge on Opensea from a number of chains reminiscent of Ethereum, Solana, and Polygon. This course of includes understanding OpenSea’s enterprise mannequin, learning sensible contract code, and sequentially extracting transaction knowledge from every chain.

This course of is difficult. In the beginning, it’s difficult and liable to errors all through the info assortment course of. Second, it’s technically advanced, given the variations in ledger design and knowledge buildings throughout chains. Lastly, it results in a waste of sources. In a situation the place 1,000 individuals want this knowledge, they’d should undergo a equally advanced course of 1,000 instances. This important repetition considerably hinders knowledge assortment effectivity and wastes computing sources.

This brings us to the aim of Footprint Analytics: to summary knowledge from disparate sectors reminiscent of GameFi, NFTs, and DeFi and set up standardized knowledge practices for the Web3 trade. This, in flip, will allow builders and trade members to entry and analyze knowledge effectively and precisely.

To this point, we’ve launched platforms on greater than 20 blockchains, organized into three core segments:

1. Footprint Progress Analytics as an Business Resolution: Tailor-made options for Web3 initiatives in advertising development and operational analytics, just like a Web3 model of Google Analytics, driving initiatives in the direction of data-driven development.
2. Zero-Code Information Evaluation Instruments: Offering an expertise just like ChatGPT, this instrument permits customers to acquire knowledge evaluation experiences via easy queries and responses. Within the foreseeable future, the usage of on-chain knowledge will likely be vastly simplified – no difficult understanding of Web3 enterprise logic or superior programming abilities will likely be required, streamlining the transition from Web2 to Web3.
3. Free Unified API: By a unified multi-chain and cross-chain API, this function facilitates cross-chain knowledge entry throughout a number of chains, offering customers with a seamless expertise to retrieve knowledge from a number of chains for free of charge.

As a knowledge platform, Footprint is a pure match with AI. AI encompasses three key sides: computing energy, knowledge, and algorithms. Amongst these, computing energy is the muse that underpins AI mannequin coaching and execution. On the identical time, knowledge is the essence of AI, and algorithms dictate AI efficiency, together with mannequin accuracy and software effectiveness.

Of those, knowledge is undoubtedly a very powerful and indispensable. Information is the lifeblood of industries and initiatives, and its significance extends to key areas reminiscent of privateness and compliance, the place its worth is immeasurable. Information could also be past buy, given its involvement in privateness and compliance points. AI acts as each a client and a producer of knowledge.

Presently, Footprint’s software of the convergence of knowledge and AI encompasses a number of main points:

In the course of the knowledge content material era section, the contribution of AI inside our platform is crucial. Initially, we use AI to generate knowledge processing code, offering customers with a extra streamlined knowledge evaluation expertise.

Extra particularly, we’re driving innovation in two particular instructions.

First, we’re curating and categorizing reference knowledge. Taking lately deployed contracts on the blockchain for example, our AI can autonomously decide the protocol to which a contract belongs, the kind of contract, and even whether or not the contract falls underneath classes reminiscent of LP or Swap on Dex platforms. This clever structuring and classification vastly improves knowledge accessibility.

Second, we are able to generate higher-level area knowledge based mostly on our reference knowledge. For instance, we use AI to create knowledge inside domains reminiscent of GameFi, NFT, and so on., offering customers with richer knowledge sources. This method enhances the standard of knowledge content material and allows customers to higher perceive knowledge throughout totally different industries.

To enhance the front-end person expertise, we now have launched an AI-based clever evaluation operate. As talked about above, when customers interact Footprint for knowledge evaluation, they encounter an expertise just like a dialog with ChatGPT. Customers can ask questions and instantly obtain corresponding knowledge evaluation experiences. The underlying logic includes translating textual content into SQL queries, dramatically reducing the entry barrier for knowledge evaluation.

Lastly, with regards to person help, we’ve developed an AI-powered customer support bot. We feed AI with knowledge from Footprint, which spans GameFi, NFT, DeFi, and different areas, to construct a customized AI customer support bot for Footprint. This AI bot offers speedy help to customers by answering questions associated to the usage of Footprint, together with knowledge varieties, knowledge definitions, API utilization, and so on. This vastly will increase the effectivity of buyer help whereas decreasing the quantity of handbook work.

Nevertheless, it’s value noting that whereas AI purposes can improve productiveness and assist remedy most challenges, they is probably not omniscient. Based mostly on our knowledge processing expertise, AI can help in fixing roughly 70% to 80% of challenges.

From a broader perspective, whatever the area by which AI is utilized, a crucial consideration is the extent of acceptance of AI’s fault tolerance. Completely different software situations have totally different fault tolerance necessities. There’s a must stability the accuracy and reliability of AI towards individuals’s tolerance for error.

As an example, in healthcare, the choice to belief both AI or a doctor might contain trust-related challenges. Within the funding house, AI can present elements that affect the path of BTC costs, however individuals should still have doubts when making precise purchase or promote choices.

Nevertheless, exact accuracy is probably not paramount in advertising and operational analytics, reminiscent of person profiling and tiering, as a result of minor errors gained’t considerably influence. Because of this, error tolerance is extra readily accepted in these contexts.

Presently, Footprint is primarily targeted on knowledge in its efforts to combine AI with Web3, which presents its personal set of challenges:

First, the primary problem is knowledge era, particularly offering high-quality knowledge for AI to attain extra environment friendly and correct knowledge era capabilities. This relationship between AI and knowledge will be in comparison with the engine and gas of a automobile, the place AI is the engine and knowledge is the gas. Irrespective of how superior the engine, a scarcity of high quality gas will forestall optimum efficiency.

This raises the query of the way to generate high-quality knowledge, for instance, the way to rapidly and routinely generate knowledge in areas reminiscent of GameFi, NFTs, DeFi, and others. This consists of routinely organizing the info connections, basically creating a knowledge graph. Extra particularly, it includes figuring out elements such because the protocols to which contracts are related, the sorts of contracts, the suppliers, and different pertinent particulars. The principle objective of this course of is to constantly present the AI with high-quality knowledge to enhance its effectivity and accuracy in knowledge manufacturing, thus making a virtuous cycle.

The second problem is knowledge privateness. Whereas Web3 is basically dedicated to decentralization and transparency, the necessity for privateness might grow to be paramount because the trade evolves. This consists of defending customers’ identities, property, and transaction data. This case presents a dilemma: the transparency of knowledge on the blockchain step by step decreases, limiting the quantity of knowledge accessible to AI. Nevertheless, this situation will likely be addressed because the trade progresses, and homomorphic cryptography is a potential resolution.

In conclusion, the convergence of AI and Web3 is inherently intertwined with a core downside: knowledge accessibility. In essence, the final word problem for AI lies in its entry to high-quality knowledge.

I consider reaching important integration and adoption of Web3 and AI will depend on addressing two elementary challenges. First, there’s a necessity to offer enhanced companies to Web3 builders and builders, particularly in areas reminiscent of GameFi, NFTs, and social platforms. Second, it’s crucial to scale back the obstacles on the applying entrance to make sure a smoother person entry into the Web3 panorama.

Let’s begin with serving the developer group. On this space, two main sorts of purposes stand out.

One class is AI-powered improvement platforms. These platforms use AI expertise to automate the creation of code templates. Whether or not for constructing DEX platforms or NFT marketplaces, these platforms can intelligently generate code templates tailor-made to the particular wants of builders, considerably growing improvement effectivity.

In video games, AI can velocity up the creation of sport fashions and the era of photographs, thus accelerating the sport improvement and launch course of. These platforms have allowed builders to focus extra on creativity and innovation somewhat than extreme time on repetitive, primary duties.

The opposite class revolves round AI-powered knowledge platforms. These platforms use AI to autonomously generate domain-specific knowledge in numerous industries reminiscent of GameFi, NFTs, SocialFi, and DeFi. The objective is to decrease the brink for builders to make use of and apply knowledge, and simplify knowledge evaluation and use.

By AI, these platforms can routinely generate numerous knowledge units, enriching builders with wealthy knowledge sources and bettering their understanding of market tendencies, person conduct, and extra. By offering builders with complete knowledge help, these knowledge platforms take away knowledge utilization obstacles and catalyze creative purposes’ emergence.

Mass adoption has all the time been a key problem within the Web3 house. For instance, the market has lately seen the emergence of blockchain options with nearly negligible charges aimed toward growing transactions per second (TPS). As well as, options such because the MPC pockets successfully handle the first barrier to migration from Web2 to Web3 by overcoming migration challenges.

The answer to those challenges doesn’t rely solely on AI expertise however is intertwined with the holistic evolution and improvement of the Web3 ecosystem. Whereas AI performs a key function in bettering effectivity and decreasing obstacles, the underlying infrastructure and development of Web3 stay key elements in fixing the mass adoption downside.