Dropdeck: A Closer Look

Fund. Get Funded. Make Informed Choices.

At DropDeck, our focus is on capital, pure and simple.
With a mass global issue in allocating and generating venture capital, there is a serious need for expertise in funding, routing, and allocating capital for innovation. The idea is to shift the attention from less efficient or zero-sum trading, and onto thriving companies looking to change up their industries, who are also in need of funding.

Between us and the people we’re trying to finance are a complex set of preexisting conditions for financing, international trade, and banking. We navigate these waters, drawing from a huge pool of finances left largely untapped by existing startup investment and SME lending platforms.

These are specific problems which we give our focus to, and it’s important to note that there is a marked gap between what these platforms offer VCs, and what those same VCs can realistically make use of. These applications allow users to see what amounts to a plain listing. It’s up to the user to make sense of the deluge of information and try to ferret out whatever useful data they can to secure decent funding.

The DropDeck Nine Step Process

Hunter – specially designated team members search out funding-worthy companies and post about them on DropDeck, by way of a recommendation to other users.

Evaluators – users whose job it is to “size up” the claims a company makes, and contribute to a series of scores and rankings of that entity, for the benefit of others.

The next step consists of two substeps:

Funder screening – Funders looking to make an investment screen companies presented to them on DropDeck, paying DDD tokens to dig deeper into these companies’ details.
Evaluators are rewarded – Every time funders pay to see these details, evaluators who contributed those details are rewarded with DDD.

Funders who want to make a commitment to a company send DDD to a smart contract, to get the ball rolling.

Local agents called delegates conduct due diligence on the company, at the funder’s request, if they haven’t already. Provided they check out, these delegates are approved by the company and the funders, alike, to be involved in the process.

Once delegates are chosen, the smart contract automatically directs DDD to the company and the Hunter.

The delegate makes legal arrangements between themselves and the company to enforce repayment at a later date.

Next, the company facilitates payment of DDD to a smart contract, based on the terms agreed to.

The last step takes two parts:

The smart contract routes a percentage of DDD to the auditor
Smart contract routes a percentage of DDD to the funders

Our methodology is simple, in theory, but grand in design and extremely effective. It starts with our high-end A.I. tech, a technology set so advanced it was added to a list of only 146 official teams, worldwide, to be supported by IBM and XPRIZE network mentors.

These algorithms, you may or may not be aware, are fully capable of operating in a vacuum, but truly shine when fed up-to-the-date information, and that has become our focus, during the development phase of this A.I.

We make it our business to source our data exhaustively from as many verifiable sources as possible, to create a more robust user experience. The idea itself is simple: more data makes for smarter algorithms, and as these become smarter, they become more sustainable and, eventually, scalable, as well. It’s a system that benefits everyone involved, as more data leads to more insightful algorithms, making the product better overall. A better product brings in more users, better profits, and, bringing it back to its roots, the opportunity to incorporate more data avenues.

Everybody wins when data sources become vast enough to create a seamless, powerful system like DropDeck.

A Note On Data

But what types of data are we incorporating?

The answer is, actually, a few different ones.

One of the most common types of data we make use of is also completely free. Crawled data taken from general Internet feeds is abundant and easy to find, but also comes with its own set of problems. The information suffers noise and lack of structure. It’s also non-proprietary, making it less valuable than if it came from a source where generating this kind of data was more of a focus.

Information gleaned from data partnerships, something else we use, is accurate and usually quite abundant, as well as being structured. It can be costly, but more than that, it’s also often exclusive and not always entirely relevant to our purposes. Metadata is another avenue we make use of, but its value to our services fluctuates depending on the level of associated user activity. There are obvious associated benefits with this information for uncovering usage patterns and authentic industry data.

The last data source we use to pull our A.I. algorithm’s base knowledge from is user-contributed data. That means surveys and censuses conducted among our user base to generate data based on your needs and preferences. This data is free and enjoys being strongly focused on the qualities we are gauging at the time. In terms of drawbacks, it being user-generated means that it’s not immune to manipulation and, depending on the source, not incentivized to be of the highest quality, either.

This last source of data is bolstered by our incentive ecosystem. Users need to contribute data in order to facilitate better selections and win rewards. It’s important to note that data contributors aren’t rewarded in the same way for their contributions – instead, they are paid based on the quantity of the data released.

As for quality, the viability of the data is left up to users, who will review, curate and rate its financial success.