Product: parigot is a platform for building micro-service-based backends. It increases developer productivity by making it easier to develop, debug, deploy, and test micro-services.
This itself is a benefit to engineering productivity, but the real power comes from using AI (ChatGPT) to do the boring, repetitive work of building a micro-services backend.
Once you have your generated code, you can adapt it to the "business critical" parts that need to be written by a human. You can use parigot to test it, debug it, and deploy it.
Parigot is open-source software, but that is just the infrastructure, not the critical bits of how to teach an AI how to build a service. As it is now, parigot allows micro-services
backends to be deployed for about 35-50% the cost of our closest competitor while retaining healthy margins.
Use of the investor's money: Primarily hiring in two key areas, leadership and engineering. More senior leadership needs to be put in place to manage the company and make key strategic decisions on things like go-to-market, pricing, , marketing vectors, and a sales process. In addition, we are going to need key engineering talent to build out a more full-featured version of the platform and to tune the particulars of the AI models to get the best results.
Business model: Give away the open source version (http://github.com/iansmith/parigot) and then charge for extra features and services built on top of the same underlying, free, platform. This business model is frequently used by companies that are built on open-source projects. For example, hashicorp, elastic, databricks, and mongo-db are all large companies built using this model and each of these started from a free version of the technological core available on GitHub. Two additional services revenue streams are available: One, consulting services to help get the basics of a parigot project generated so the buyer's engineering staff don't have to understand how to use the AI generation. Second, paid support for customers that need stronger guarantees about availability, etc.
Business Strategy: "Land and expand." Like other tools that have previously been successful in this space, the model here is to convince engineers at companies building microservice-based backends to try this technology for free with the idea that they will then get the technology accepted into enterprises. This has a been a successful approach for many "tools oriented" services like Heroku ($212M cash acquisition), FireBase (rumored to be an $80M acquisition), GitHub ($7.5B acquisition) and JetBrains ($7B valuation in 2021). A complimentary approach to land and expand is to partner with firms that do software development for a fee. These firms are interested in using any development tool that allows them to get more value out of each employee, since this value is a key limitation to their firm's' ability to make a profit. With a partnership with such firms, the end-customer of the software is likely required to use the parigot team's infrastructure for deployments, creating A.R.R. for parigot.
Customer Value Proposition: The engineers are not the target for revenue growth, since they don't have the deep pockets but are the vector for introducing this technology into larger orgs. The orgs will quickly understand our two key value propositions:
* The rate that engineers can develop/test/debug micro-services directly impacts the cost of projects (internally or externally facing) in the larger organization. With engineering heads costing at least $10-15K/month, either a reduction in necessary headcount or the ability to repurpose existing staff makes the cost calculation for using parigot an easy one.
* Parigot-based can be deployed at much cheaper prices than the competition, e.g. 50-65% off the price of competitor Heroku. For larger companies, they can have deployment and other operational costs in the tens or even hundreds of thousands of dollars per month. Again, if parigot and its lower prices could cover even parts of their deployments, the cost savings would be dramatic.
Defensible Business Advantages: The ability to build micro-services backends based on AI is not a simply ability to acquire. It requires understanding the AI platform's strategic weaknesses and strengths and leveraging the strengths while addressing the weaknesses with your own (custom) code. In particular, a strong knowledge of computer science is critical here because judgements have to be made frequently about the time trade-off of getting the code quicker once the AI can generate it versus the time it will take to teach the AI what needs to be done.
Parigot's design means that applications built on it can run using substantially cheaper hardware resources in the cloud than other systems. In some cases, like cloud provider Azure, the reduction in cost to parigot to provide hosting services for an application would be 90% off the normal "by-the-hour" price. The ability use cheaper cloud resources offers the possibility of substantial margins while still undercutting the competition prices.
Founder: Ian Smith got his Ph.D in computer science in 1998 from Georgia Tech. His thesis advisor was Scott Hudson, now at Carnegie Mellon University. He has had two successful careers already, one as a scientist and one in software development. For the former, he spent ten years at research laboratories (Xerox PARC and Intel) and published 30+ academic papers with thousands of citations, plus 10+ patents. His second career was in software development, with diverse roles such as a software engineer and CTO of a 40 person engineering group... and everything in between (team lead, tech lead, engineering manager, vp of software).
Team: Other senior employees are currently being recruited, but these names cannot be disclosed currently.
Company: The founder has already secured a Delaware C-corp (Right Bank Software Society) with incorporation documents based on best startup practices and focused on the "cleanest" experience for investors. Efforts are in-progress to buy the necessary D&O insurance, as well as consulting services to help with software development in the immediate term.
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