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 concentrate on 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 trade, an absence of standardized practices, and an absence of organized information. In consequence, information utility turns into problematic.

As an example, take into account the state of affairs the place you need to entry transaction information on Opensea from a number of chains akin to Ethereum, Solana, and Polygon. This course of includes understanding OpenSea’s enterprise mannequin, finding out sensible contract code, and sequentially extracting transaction information from every chain.

This course of is sophisticated. Firstly, it’s sophisticated and vulnerable to errors all through the info assortment course of. Second, it’s technically complicated, given the variations in ledger design and information constructions throughout chains. Lastly, it results in a waste of assets. In a state of affairs the place 1,000 folks want this information, they’d must undergo a equally complicated course of 1,000 instances. This important repetition considerably hinders information assortment effectivity and wastes computing assets.

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

Thus far, we’ve launched platforms on greater than 20 blockchains, organized into three core segments:

1. Footprint Development Analytics as an Trade Resolution: Tailor-made options for Web3 tasks in advertising and marketing development and operational analytics, much like a Web3 model of Google Analytics, driving tasks in direction of data-driven development.
2. Zero-Code Knowledge Evaluation Instruments: Offering an expertise much like ChatGPT, this device permits customers to acquire information evaluation stories by means of easy queries and responses. Within the foreseeable future, the usage of on-chain information can be tremendously simplified – no sophisticated understanding of Web3 enterprise logic or superior programming abilities can be required, streamlining the transition from Web2 to Web3.
3. Free Unified API: Via a unified multi-chain and cross-chain API, this characteristic facilitates cross-chain information entry throughout a number of chains, offering customers with a seamless expertise to retrieve information from a number of chains without charge.

As an information 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 crucial and indispensable. Knowledge is the lifeblood of industries and tasks, and its significance extends to key areas akin 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.

At present, Footprint’s utility of the convergence of information and AI encompasses a number of main facets:

Throughout the information content material technology 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 beneath classes akin to LP or Swap on Dex platforms. This clever structuring and classification tremendously 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 akin to GameFi, NFT, and so on., offering customers with richer information assets. This strategy enhances the standard of information content material and allows customers to higher perceive information throughout totally different industries.

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

Lastly, in terms of person assist, 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 fast help to customers by answering questions associated to the usage of Footprint, together with information sorts, information definitions, API utilization, and so on. This tremendously will increase the effectivity of buyer assist whereas decreasing the quantity of handbook work.

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

From a broader perspective, whatever the area through which AI is utilized, a crucial consideration is the extent of acceptance of AI’s fault tolerance. Totally different utility eventualities have totally different fault tolerance necessities. There’s a must steadiness the accuracy and reliability of AI towards folks’s tolerance for error.

As an illustration, in healthcare, the choice to belief both AI or a doctor might contain trust-related challenges. Within the funding area, AI can present components that affect the course of BTC costs, however folks should have doubts when making precise purchase or promote choices.

Nonetheless, exact accuracy is probably not paramount in advertising and marketing and operational analytics, akin to person profiling and tiering, as a result of minor errors gained’t considerably affect. In consequence, error tolerance is extra readily accepted in these contexts.

At present, 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 technology, particularly offering high-quality information for AI to realize extra environment friendly and correct information technology capabilities. This relationship between AI and information might be in comparison with the engine and gasoline of a automotive, the place AI is the engine and information is the gasoline. Regardless of how superior the engine, an absence of high quality gasoline will stop optimum efficiency.

This raises the query of find out how to generate high-quality information, for instance, find out how to shortly and robotically generate information in areas akin to GameFi, NFTs, DeFi, and others. This contains robotically organizing the info connections, basically creating an information graph. Extra particularly, it includes figuring out components such because the protocols to which contracts are related, the varieties of contracts, the suppliers, and different pertinent particulars. The principle 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 essentially dedicated to decentralization and transparency, the necessity for privateness might develop into paramount because the trade evolves. This contains defending customers’ identities, property, and transaction info. This case presents a dilemma: the transparency of information on the blockchain steadily decreases, limiting the quantity of information accessible to AI. Nonetheless, this subject can be addressed because the trade progresses, and homomorphic cryptography is a doable answer.

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

I consider reaching important integration and adoption of Web3 and AI depends upon addressing two elementary challenges. First, there’s a necessity to offer enhanced providers to Web3 builders and builders, particularly in areas akin to GameFi, NFTs, and social platforms. Second, it’s crucial to scale back the limitations on the appliance 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 varieties of functions stand out.

One class is AI-powered improvement 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 particular wants of builders, considerably growing improvement effectivity.

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

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

Via AI, these platforms can robotically generate numerous information units, enriching builders with wealthy information assets and enhancing their understanding of market developments, person habits, and extra. By offering builders with complete information assist, these information platforms take away information utilization limitations and catalyze creative functions’ emergence.

Mass adoption has all the time been a key problem within the Web3 area. For instance, the market has not too long ago 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 tackle 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 improvement of the Web3 ecosystem. Whereas AI performs a key function in enhancing effectivity and decreasing limitations, the underlying infrastructure and development of Web3 stay key components in fixing the mass adoption downside.