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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