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#+TITLE: Duree: automated universal database
#+SUBTITLE: seeking pre-seed funding
#+AUTHOR: Ben Sima <ben@bsima.me>
#+EMAIL: ben@bsima.me
#+OPTIONS: H:1 num:nil toc:nil
#+LATEX_CLASS: article
#+LATEX_CLASS_OPTIONS:
#+LATEX_HEADER:
#+LATEX_HEADER_EXTRA:
#+LATEX_COMPILER: pdflatex
#+DATE: \today
#+startup: beamer
#+LaTeX_CLASS: beamer
#+LaTeX_CLASS_OPTIONS: [presentation,smaller]
* Problem
Developers spend too much time managing database schemas. Every database
migration is a risk to the business because of the high possibility of data
corruption. If the data is modeled incorrectly at the beginning, it requires a
lot of work (months of developer time) to gut the system and re-architect it.
* Solution
- Using machine learning and AI, we automatically detect the schema of your data.
- Data can be dumped into a noSQL database withouth the developer thinking much
about structure, then we infer the structure automatically.
- We can also generate a library of queries and provide an auto-generated client
in the choosen language of our users.
* Existing solutions
- Libraries like alembic and migra (Python) make data migrations easier, but
don't help you make queries or properly model data.
- ORMs help with queries but don't give you much insight into the deep structure
of your data (you still have to do manual joins) and don't help you properly
model data.
- Graph QL is the closest competitor, but requires manually writing types and
knowing about the deep structure of your data. We automate both.
* Unsolved problems
- Unsure whether to build this on top of existing noSQL databases, or to develop
our own data store. Could re-use an existing [[https://en.wikipedia.org/wiki/Category:Database_engines][database engine]] to provide an
end-to-end database solution.
* Key metrics
- How much time do developers spend dealing with database migrations? What does
this cost the business? We can decrease this, decreasing costs.
- How costly are failed data migrations and backups? We reduce this risk.
* Unique value proposition
We can automate the backend data mangling for 90% of software applications.
* Unfair advantage
- I have domain expertise, having worked on similar schemaless database problems
before.
- First-mover advantage in this space. Everyone else is focused on making
database migrations easier, we want to make them obsolete.
* Channels
- Cold calling mongoDB et al users.
* Customer segments
- *Early adopters:* users of mongoDB and graphQL who want to spend time writing
application code, not managing database schemas. The MVP would be to generate
the Graph QL code from their Mongo database automatically.
- Will expand support to other databases one by one. The tech could be used on
any database... or we expand by offering our own data store.
* Cost structure
** Fixed costs
- Initial development will take about 3 months (~$30k)
- Each new database support will take a month or two of development.
** Variable costs
- Initial analysis will be compute-heavy.
- Following analyses can be computationally cheap by buildiing off of the
existing model.
- Customer acquisition could be expensive, will likely hire a small sales
team.
* Revenue streams
- $100 per month per database analyzed
- our hosted service connects to their database directly
- includes client libraries via graphQL
- may increase this if it turns out we save companies a lot more than $100/mo,
which is likely
- enterprise licenses available for on-prem
- allows them to have complete control over their database access
- necessary for HIPAA/PCI compliance
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