datuum.ai helps with data migration, M&A processes by recognizing data in sources, mapping it to the final database, and automatically ETL pipelines generation. It reduces 80% of Data Engineers’ time.
Current Status
We have 3 completed projects and 1 project of 1-year length for now.
3 customers are in pipeline waiting for a new product version to start cooperation.
20000 USD per month in revenue as of today
Market
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. Market exposure can reach 50k companies and TAM can reach per our understanding 85B USD.
Problem or Opportunity
Usual data migration process for Enterprise level clients 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, 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 pops up here 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.
Solution (product or service)
Our product leads to a reduction of 80% of the time on data-related processes—far more impactful than manual approaches. With a help of AI datuum.ai recognizes data semantically from a variety of sources, map it to final database structures, and automatically generates ETL pipelines.
Competitors
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 alike 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.
Finance
Business model: Monthly or Annual Platform subscription fee
Monthly subscription fee will average to $20K with estimated average LTV $240K for 2022.
Business model
Business model: Monthly or Annual Platform subscription fee
The monthly subscription fee will average $20K with an estimated average LTV of $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.
Offer for investor
That will be determined by the results of the conversation we may have.
