Interview with Footprint Analytics CEO

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

We specialise in structuring information. Regardless of the relative benefit of Web3 over Web2 in clear on-chain information, sure challenges stay. These embody the nascent standing of the business, a scarcity of standardized practices, and a scarcity of organized information. Consequently, information utility turns into problematic.

For instance, contemplate the situation the place you wish to entry transaction information on Opensea from a number of chains corresponding to Ethereum, Solana, and Polygon. This course of entails understanding OpenSea’s enterprise mannequin, learning sensible contract code, and sequentially extracting transaction information from every chain.

This course of is sophisticated. At first, it’s sophisticated and liable to errors all through the information assortment course of. Second, it’s technically complicated, given the variations in ledger design and information buildings throughout chains. Lastly, it results in a waste of sources. In a situation the place 1,000 folks want this information, they’d should undergo a equally complicated course of 1,000 occasions. This vital repetition considerably hinders information assortment effectivity and wastes computing sources.

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

Up to now, we’ve launched platforms on greater than 20 blockchains, organized into three core segments:

1. Footprint Progress Analytics as an Business Answer: Tailor-made options for Web3 tasks in advertising progress and operational analytics, just like a Web3 model of Google Analytics, driving tasks in direction of data-driven progress.
2. Zero-Code Knowledge Evaluation Instruments: Offering an expertise just like ChatGPT, this software permits customers to acquire information evaluation stories by way of easy queries and responses. Within the foreseeable future, the usage of on-chain information shall be vastly simplified – no sophisticated understanding of Web3 enterprise logic or superior programming expertise shall 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 information entry throughout a number of chains, offering customers with a seamless expertise to retrieve information from a number of chains for gratis.

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

Of those, information is undoubtedly an important and indispensable. Knowledge is the lifeblood of industries and tasks, and its significance extends to key areas corresponding to privateness and compliance, the place its worth is immeasurable. Knowledge could also be past buy, given its involvement in privateness and compliance points. AI acts as each a shopper and a producer of information.

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

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

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

First, we’re curating and categorizing reference information. Taking not too long ago deployed contracts on the blockchain for instance, our AI can autonomously decide the protocol to which a contract belongs, the kind of contract, and even whether or not the contract falls below classes corresponding to LP or Swap on Dex platforms. This clever structuring and classification vastly improves information accessibility.

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

To enhance the front-end person expertise, we’ve launched an AI-based clever evaluation perform. As talked about above, when customers have interaction Footprint for information evaluation, they encounter an expertise just like a dialog with ChatGPT. Customers can ask questions and instantly obtain corresponding information evaluation stories. The underlying logic entails translating textual content into SQL queries, dramatically decreasing the entry barrier for information evaluation.

Lastly, in the case of person help, we’ve developed an AI-powered customer support bot. We feed AI with information from Footprint, which spans GameFi, NFT, DeFi, and different areas, to construct a customized AI customer support bot for Footprint. This AI bot gives rapid help to customers by answering questions associated to the usage of Footprint, together with information sorts, information definitions, API utilization, and so forth. This vastly will increase the effectivity of buyer help whereas lowering the quantity of handbook work.

Nevertheless, it’s value noting that whereas AI functions can improve productiveness and assist remedy most challenges, they might not be omniscient. Primarily based on our information 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. Totally different utility eventualities have completely different fault tolerance necessities. There’s a must steadiness the accuracy and reliability of AI in opposition to folks’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 area, AI can present elements that affect the course of BTC costs, however folks should have doubts when making precise purchase or promote choices.

Nevertheless, exact accuracy might not be paramount in advertising and operational analytics, corresponding to person profiling and tiering, as a result of minor errors received’t considerably affect. Consequently, error tolerance is extra readily accepted in these contexts.

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

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

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

The second problem is information privateness. Whereas Web3 is basically dedicated to decentralization and transparency, the necessity for privateness might turn into paramount because the business evolves. This consists of defending customers’ identities, belongings, and transaction data. This case presents a dilemma: the transparency of information on the blockchain progressively decreases, limiting the quantity of information accessible to AI. Nevertheless, this difficulty shall be addressed because the business progresses, and homomorphic cryptography is a attainable answer.

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

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

Let’s begin with serving the developer neighborhood. On this space, two main kinds of functions stand out.

One class is AI-powered growth platforms. These platforms use AI know-how 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 precise wants of builders, considerably growing growth effectivity.

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

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

By AI, these platforms can routinely generate numerous information units, enriching builders with wealthy information sources and enhancing their understanding of market developments, person conduct, and extra. By offering builders with complete information help, these information platforms take away information utilization obstacles and catalyze ingenious functions’ emergence.

Mass adoption has at all times been a key problem within the Web3 area. For instance, the market has not too long ago seen the emergence of blockchain options with just about negligible charges aimed toward growing transactions per second (TPS). As well as, options such because the MPC pockets successfully deal with the first barrier to migration from Web2 to Web3 by overcoming migration challenges.

The answer to those challenges doesn’t rely solely on AI know-how however is intertwined with the holistic evolution and growth of the Web3 ecosystem. Whereas AI performs a key position in enhancing effectivity and lowering obstacles, the underlying infrastructure and progress of Web3 stay key elements in fixing the mass adoption drawback.