building an AI backend for early-stage products
you found your million-dollar AI idea. you built a no-code minimum viable product and showed it to potential customers. the interactions validated a real problem they will pay to solve. and your mvp solves it!
so you set out to build a legitimate product. and you start hitting the limitations that the no-code mvp glossed over… things like personalization, rag pipelines, and llm orchestration. “details” like authentication, latency, and secure data access start to matter. you need to build a real backend.
what do modern early-stage AI app backends look like?
after turning multiple mvps into real AI apps, i realized that the underlying backend architecture was often the same:
1/ serverless orchestration layer (e.g., cloud run)
2/ authentication + user data layer (e.g., firebase)
3/ vector database + llm api layer (e.g., vertex AI & openai)
user → orchestration → firebase + vector dbs/llms → response
setting up each layer from scratch took days. this added weeks of repetitive work every time i spun up a new app.
so how did i accelerate development?
every user interaction hits the orchestration layer, which becomes the AI app’s core nervous system. it contains the logic and routing to pull the right data, build the appropriate context, call the llm, and write results back safely.
this cloud-native, service-driven architecture is already tuned for rapid prototyping. i don’t need to worry about setting up servers. i don’t need to train models (unless i want to).
but the real unlock was developing reusable tools instead of reinventing the wheel for each new app:
— cloud function templates prewired to connect to my resources and enforce tight data scopes
— flexible vector database and rag pipelines
— a shared firebase project with isolated data access for multi-app usage
end of the day? my cycle time to spin up net new AI apps dropped by 60%. from months to weeks.
what to do with all of this new free time?
now i spend that time differently… talking to customers and refining product strategy.
why? because increasing dev velocity ended up reminding me what really matters… building the right thing in the first place.
it doesn’t matter how fast your dev cycle is if you aren’t building the right thing. and that doesn’t happen by building fast. it happens by learning and iterating fast.
what are your best hacks for accelerating development, learning, and iteration velocity for greenfield AI products?