How To Get Funding For AI Startups

– Are you relying on big firms to provide you with data?When it comes to data, I realized that both methods could be interesting for a startup, but investors will always prefer data independence over.

The simple fact that you have built a unique dataset is highly appreciated and valuable for an investor.

 A unique dataset is a real asset for a startup.

In addition to being unique, your data sets, of course, need to be relevant to the challenge to be overcome.

A unique dataset will prevent other players with more resources to gather more data faster and consequently improve their algorithms’ performance.

If you have just started, I recommend you to come up with creative ways of creating and obtaining meaningful datasets; try partnering with unique organizations in exchange for your AI solution!Other solutions exist such as APIs, open source, or private databases, that you can purchase.

As mentioned before, why not explore the potential for mutually beneficial partnerships and innovative business models (revenue sharing) that allow access to proprietary of hard-to-access data.

Investors also take into consideration whether a company works with fast-moving or static data.

Algorithms for fast-moving data, such as the real-time images processed by a self-driving car, are often much more complex.

Another important thing that I have noticed when dealing with VCs is that it is important that the startup demonstrates an ability to continually enhance its performance based on the uniqueness of its data.

It is a huge plus when your startup can showcase the ability to quickly process training data and optimize efficiently its algorithms while systems become more robust.

Most investors do rely on technical experts and industry advisors that can determine whether the startup is properly managing data architecture, data collection, storing, parsing, etc.

The ability to create and benefit from feedback loops is also appreciated.

 Indeed, close integration of user feedback into the product allows for superior model performance and, more broadly, a better product experience.

More users, More Data, Better Products.

Investors tend to particularly appreciate when the user experience adapts to the type of data required to improve the algorithm’s performance.

This feature is still a work in progress for most startups.

 Finally, I recommend startups not to focus on AI infrastructure.

I believe it will remain a field dominated by much larger firms such as Google, Microsoft, and Amazon.

If the solution already exists, then you would benefit more from building on top of it.

Building an AI startup takes time and it is easy to lose sight on your customer and some business metrics that are key to attract VCs.


Reposted with permission.

Bio: Alexandre Gonfalonieri is an AI consultant and writes extensively about AI.

 Related: var disqus_shortname = kdnuggets; (function() { var dsq = document.

createElement(script); dsq.

type = text/javascript; dsq.

async = true; dsq.

src = https://kdnuggets.



js; (document.

getElementsByTagName(head)[0] || document.


appendChild(dsq); })();.. More details

Leave a Reply