Today Datagran reaches a big milestone.
Founded in 2017, Datagran trailed the path every single startup has to endure during the first few years: the quest to find product-market fit. It is well documented that to reach product-market fit, startups must listen to customers and pivot based on that feedback. Sounds simple, but it is not.
It takes time to truly understand customers’ needs and pains, so during the first 3 years of being in the market, we launched and tested several products which helped us understand where we needed to focus. To some people, that was a bad sign, for us, it was every successful startup’s organic process. After this initial journey, after many discussions, feedback from thousands of clients across different continents, hundreds of investor conversations, we are confident we have a product people truly need and want.
Instantly, we began thinking about what the next step should be. No doubt the next step is to keep building based upon our findings. Build until our product is stable and has the features people need. But there is not much we will learn from our customers after that journey. So now that we know what we want to build, and where our focus is, an important step we needed to take presented itself in front of us. One that would help us build with confidence for the next decade. We needed to protect our product.
On March 16, 2021, Datagran filed its first formal patent claim. Starting on March 16 we have full invention protection since it is not a provisional patent. For us, this was extremely important thanks to our company being the only one in the market with an offering like our product.
Why is Datagran different from other tools in the market?
In context, Datagran is the first tool that allows you to easily run Machine Learning models and send them to production with no need to code. This dramatically changes the way people interact with ML tools, empowering a new generation of professionals to build better products and companies. Imagine Datagran as the tool of record for all schools and universities across the globe to teach Machine Learning.
That said, Datagran addresses the problem 50% of companies have when dealing with Machine Learning. According to Gartner Machine Learning models are not sent to production due to the complexities to launch them. In simpler words, it is very difficult to create an ML Model, process Big Data and send the output to a business application like Salesforce, Intercom, RestAPI, etc. To be able to do that, companies need to hire costly professionals like ML Ops, Data Scientists, Developers, and Dev Ops and spend long hours, weeks, and sometimes months to complete and send the outputs of a model. With Datagran companies can now leave these complexities behind and focus on building models and sending the outcomes to their favorite apps instantly.
For businesses, this changes the way business and analytics teams interact because they can see and act based on their data today, not tomorrow, or next week or month. According to our findings, companies take an average of one to two months on average to deliver the output of a model in production to a business unit. That’s unacceptable.
There’s proof in the market that this kind of tool is needed, take www.zapier.com as an example. Last summer, Zapier reached $100 million in annualized recurring revenue; it passed $140 million by now. And in January, investors found a way into the business after just raising $1.3 since inception. Sequoia and Steadfast Financial bought shares at a $5 billion valuation from some of Zapier’s original investors.
Zapier is the tool preferred by many to automate workflows. Our main and only difference with Zapier is that we added an ML layer to this. We could say then that Datagran is the next generation of Zapier.
We hope that with this short overview you share our excitement. After a tough year for many, the future finally is looking brighter.
We are looking forward to what’s to come.