Datagran

In today’s world, building machine learning (ML) systems is a necessity for organizations of all sizes.

However, 50% of companies say they struggle to put machine learning into production, while 80% of big data projects fail.

These problems arise because putting ML models into production is one of the most difficult tasks in data science.

With such a challenging process, it’s usually only big companies that have the resources and budget to use machine learning.

The Solution:

At Datagran, we are making machine learning accessible for everyone.  

We collect and organize data, build machine learning models and automate workflows - without engineering. 

We like calling ourselves zapier.com for Machine Learning.

Zapier enabled developers to easily send data from one app to another. We do the same, but with the ML layer on top.

Our patent-pending system gives businesses the speed they need to become more successful.

________________________

🚀 Since 2017, we have helped more than 3,000 companies worldwide automate machine learning.

________________________

🦸 But don’t take our word for it. Here’s what our customers say:

________________________

📈 Industry-leading traction

Our business model is helping us generate more revenue, with profit margins of more than 80%.

We are growing rapidly and witnessed 180% YoY growth.

________________________

💰Our unit economics are unheard of:

Our current churn rate is 0%.  

Based on the current clients, our LTV is $96k with a CAC of just $1,500 for enterprises. 

The CAC for small businesses is a mere $30 with an LTV of $12,000

________________________

🏷️ Pricing Model - Free forever or pay-per-use 

Customers can sign up for free to test and try the product. While they can use the product for free for life, they will need to upgrade to paid tiers to access more features and get more value.

We base our pricing on customer usage. There are three main variables that affect pricing: the number of data rows, users, and machine time.

Our bottom-up strategy begins with the client trying the product. If we identify that the customer should be an enterprise client, we contact that user to hand-hold them through onboarding. Our minimum enterprise pricing is $1,500 per month, which includes a set of usage limits.

________________________

Competitors in this field include DataRobot, Alteryx, Azure, and Amazon, among others.

Currently, almost all tools in the market tackle the modeling side. Datagran focuses on Operations of the Data—what is known as DataOps or MLOps.

The market indicates that most companies will want to go into our space, but our IP is protected.

________________________

At Datagran, we aim to become the default platform for students and SMBs, which is a market currently overlooked by all other players.

We currently operate in a huge global AI market, which is projected to reach $266 billion by 2027.

Datagran is not alone in this market, yet our proposition stands head and shoulders above our competitors, including DataRobot, Alteryx, Azure, and Amazon.

Currently, almost all tools in the market tackle the modeling side. Datagran focuses on operations of the data, which is known as DataOps or MLOps. The market indicates that many companies will want to follow us into our space – but our IP is protected.

With this unique proposition and an army of investors behind us, we are now ready to take a bigger chunk of this massive market.

________________________

Since 2017, Datagran has raised close to $5 million through multiple funding rounds.

Earlier notable angel investors include 

• Telefonica, 
• Quake Capital, 
• Beresford Partners, and 
• C-level executives from Uber and Bain & Company

Here's why Brenton Cromwell, Senior Data Scientist at OpenDoor, invested in Datagran:

We are now opening our doors for one last time to the Wefunder community as we continue to improve and grow our product, expand our team, and build our user base to acquire more enterprise users.

By early 2023, we will be at a breakeven point and will need no more funding.

This means that now is the best time to invest.

______________________

At Datagran, we are fast becoming the default machine learning platform for data scientists, students, small teams, and small businesses around the world.

• Businesses

Datagran enables businesses to make better-informed decisions faster using their data.

• Data scientists

Datagran helps data scientists deploy complex models in minutes.

• Small businesses and students

Datagran gives students access to data tools that were too inaccessible before.

But just because we want to help more SMBs and students, we don’t overlook the huge enterprises that also benefit from using Datagran.

______________________

New Features
We will be adding more data sources and destinations. We will add AutoML features as well as additional capabilities to give users flexibility. For example, we will soon release a feature for Data Scientists to reduce model Drift in production—something that is very well ahead of current market solutions.

Go-to-platform 

Finally, we will aim to become a platform where developers can create their own elements to deploy end-to-end workflows that are personalized for every need.

Sales Expansion 
We will expand our sales team to acquire more enterprise users. At the same time, by maturing the product and investing in SEO and events, we expect to increase our self-serve offering.

______________________

Let’s look at a quick case study.

Before Datagran, Starbucks had to use FIVE different tools to aggregate, upload, analyze, predict, and act on data.

• Using 5 different tools
• Had difficulty dealing with scaling, scheduling, and building APIs
• 1 month to try to have a churn model that is in the hands of the people that will activate it like Marketing

This cumbersome process resulted in difficulty dealing with scaling, scheduling, and building APIs, and it took one month to produce a churn model that could be acted on by the marketing team.

End-to-end workflows that increased the speed to production without the need for big data teams.

By switching to Datagran, Starbucks could collect and organize data, build machine learning models, and automate workflows all without engineering and in one cohesive system.

________________________

What Datagran can do for businesses?

• Democratize data
• Increase speed-to-production
• Build ML pipelines fast, without engineering
• Build flexible visualizations in one place
• Create a dashboard for every internal “client”
• Easy collaboration boosts goal accomplishment

Datagran automatically runs your data model on info, and moves the output between your business apps effortlessly—so you can focus on what’s important.

End-to-end workflows that increased the speed-to-production without the need for big data teams. 

______________________

Now you know how big we can become, don’t you want to know how Datagran works?

1. ORGANIZE YOUR DATA PROJECTS

Users can create data projects around teams and give access to the people who need it when they need it. Users avoid having to download and upload data every time they have a new team member or project goal.

2. CONNECT YOUR SOURCES
We integrate seamlessly with an extensive suite of data sources, guaranteeing enterprise-grade security, and best-in-class customer success.

3. SEE REAL-TIME DATA WITH A FLEXIBLE BI

Users can create SQL queries or select filters to visualize data in a table, pie, or bar chart. They can save visualizations as a block to have a 360-degree view on boards.

4. RUN PIPELINES

Users can aggregate, clean, deduplicate, visualize, create triggers, run algorithms and take action with our multiple operators. 

Datagran allows users to build simple queries or run complex machine-learning algorithms over specific data sets without having to write a single line of code.

5. THE FLEXIBILITY YOU NEED

We provide an IDE (VS CODE), jupyter notebook, SQL, or even low code tools to provide the flexibility everyone needs

6. SEND THE OUTPUT TO BUSINESS APPLICATIONS

Users can easily send the output of their models to the applications they use daily, without having to spend time building APIs in real-time or batch.