datuum.ai helps organizations with data migration, merge and acquisition processes, and the creation of a single source of truth. Our product leads to a reduction of 80% of the time on data-related processes—far more impactful than manual approaches. It helps to recognize data in its sources, map it to final database structures, normalize it and automatically generate ETL pipelines.
3 completed projects and one-year length project as of now. 3 customers are in pipeline and waiting for a new product version to start cooperation.
20000 USD per month in revenue as of today
We believe that the product we are designing can fit any organization that goes through transformation processes. Though the primary target is consulting companies and vendors that provide data-based solutions (e.g., analytics, Data Science, etc.) Here we have focused on 2 domain areas: Healthcare and Veterinary where we have the biggest exposure due to the past experience of our team in the past.
Problem or Opportunity
Our client is Opti Dealer https://opti-dealer.pl/
The task was car dealerships' data integration into a SaaS analytics platform
We took 20 CRMs and placed their data into a single database. It took us 4 days to complete all of them, whereas it would take 1–2 months to do so manually per one system alone.
Solution (product or service)
The usual data migration process for Enterprise level client lasts 2-3 years involving a team of 6-10 Data Engineers. Such projects are long-lasting, include a lot of manual work in data processing, are extremely expensive, accompanied by a long recruiting process of finding and hiring data engineers that are extremely expensive and rare on the market. Another thing that pops up here is that these projects are not really interesting and companies either have to keep motivation high or rotate people all the time.
With Datuum.ai you won’t need a team of Engineers, 1 Data Engineer would be enough to handle complex data migrations, and the time for such project implementation will be reduced by 80% compared to a manual process. Data Engineers’ will be able to work on other projects and tasks that are more interesting.
We believe that as of today we have several main competitors, which are companies that build ETL processes and try to automate them in different ways. As an example, it can be Informatica, Fivetrain, Clover, etc. Those companies create semi-automated ways of data processing, they don’t have the tech base that we use. Here is one more company called Enentir that announced the project launch like ours, but we do not treat them as competitors as they performed in other leagues and still haven’t released a product to play with.
Advantages or differentiators
The biggest advantage of the product is that it is built by people who have in total 50+ years of experience in data management and data operations. We know what are the main pains and how they can be solved using the most advanced technologies. Another point here is that we are using neural networks that help in data recognition, educating it on millions of records from different domains. The bigger amount of data we run through the engine the more precise results we give to the end-user and the smaller amount of manual effort has to be spent on the project. If someone wants at some point of time to replicate the product they will have to pass all the way from the very beginning and teach the system from scratch.
Business model: Monthly or Annual Platform subscription fee
The monthly subscription fee will average $20K with an estimated average LTV $240K for 2022.
Money will be spent on
Mostly we need money to boost our system development and cover the demands from the market and current customers. Also, a part of the will be spent on Marketing, PR and Sales activities.