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| -rw-r--r-- | idea/duree-pitch.org | 76 | ||||
| -rw-r--r-- | idea/flash.org | 36 |
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diff --git a/idea/duree-pitch.org b/idea/duree-pitch.org new file mode 100644 index 0000000..485dd39 --- /dev/null +++ b/idea/duree-pitch.org @@ -0,0 +1,76 @@ +#+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 diff --git a/idea/flash.org b/idea/flash.org new file mode 100644 index 0000000..1c392f0 --- /dev/null +++ b/idea/flash.org @@ -0,0 +1,36 @@ +#+title: Flash +#+description: a system for quickly testing business ideas + +- Each marketing iteration for a product requires some gear. A "gear" pack is just a yaml + file with all data for a single flash test. It will include ad content, + pricing info, links to necessary images, and so on. + - even better: store these in a database? Depends on how often we need to edit them... +- Data gets marshalled into a bunch of templates, one for each sales pipeline in + the /Traction/ book by Gabriel Weinberg (7 pipelines total) +- Each sales pipeline will have a number of integrations, we'll need at least + one for each pipeline before going to production. E.g.: + - google adwords + - facebook ads + - email lists (sendgrid) + - simple marketing website + - producthunt + - etc +- Pipelines will need to capture metrics on a pre-set schedule. + - Above integrations must also pull performance numbers from Adwords etc APIs. + - Will need some kind of scheduled job queue or robot background worker to handle this. + - A simple dashboard might also be useful, not sure. +- Metrics determine the performance of a pipeline. After the defined trial + duration, some pipelines will be dropped. The high-performing pipelines we + double-down on. +- Metrics to watch: + - conversion rate + - usage time - minutes spent on site/app + - money spent per customer + - see baremetrics for more ideas +- This can eventually be integrated to a larger product design platform (what Sam + Altman calls a "product improvement engine" in his playbook - PIE?). + - metric improvement can be plotted on a relative scale + - "If you improve your product 5% every week, it will really compound." - Sam + - PIE will differ from Flash in that Flash is only for the early stages of a + product - sell it before you build it. PIE will operate on existing products + to make them better. |