# Rapid AI Prototyping with LangChain, Supabase, and FastAPI
**Note:** This article has been significantly updated to reflect the latest changes in technologies and best practices as of 2025.
Speed is leverage. The faster you can turn an idea into something real — something that people can actually use — the faster you find what works and kill what doesn't.
At Nought Digital, we build and test AI prototypes using a repeatable, modular system built around **LangChain**, **Supabase**, and **FastAPI**. This stack lets us go from concept to proof-of-value in under a week, without cutting corners.
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## Why This Stack Works
Each part of the stack does one thing really well:
- **LangChain** — the brain. Handles orchestration, agents, and memory.
- **Supabase** — the memory + state layer. Stores conversations, embeddings, feedback, and analytics.
- **FastAPI** — the glue. A fast, typed API layer to expose AI workflows safely to your product or client app.
Together, they give you the speed of a prototype with the structure of a production system.
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## Phase 1: The Concept
We start by defining _what_ the system needs to do — not the tech.
Example:
> "Can an internal support bot summarise customer issues and auto-tag them by urgency?"
That single sentence defines the scope for v1. Then we translate it into a **LangChain pipeline** that can be tested in isolation.
```python
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from openai import OpenAI
prompt = PromptTemplate.from_template(
"Summarise and tag this support message:\n\n{message}"
)
# Update to the latest model if available
chain = LLMChain(llm=OpenAI(model="gpt-6"), prompt=prompt)
result = chain.run(message="My order hasn't arrived after two weeks")
print(result)
That's a prototype. 5 lines of code. Instant feedback.
Phase 2: Data & Memory
We push test results, embeddings, and user feedback into Supabase.
That gives every prototype a memory — even before it becomes a product.
from supabase import create_client
# Confirm the latest method for client creation
supabase_client = create_client(supabase_url, supabase_key)
supabase_client.table("runs").insert({
"query": "order hasn't arrived",
"summary": "delivery delay",
"tag": "urgent"
}).execute()
This means by the time the client signs off, we already have production-ready telemetry.
Phase 3: Deployment & Feedback Loop
We wrap everything in FastAPI, define routes with schema validation, and expose endpoints that frontend devs or automations can call.
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class SupportRequest(BaseModel):
message: str
@app.post("/summarise")
async def summarise(req: SupportRequest):
result = chain.run(message=req.message)
return {"summary": result}
Deploy it to Vercel, Fly.io, or Cloudflare Workers — whatever suits the latency and scale. As of 2025, Vercel offers a free tier with limited bandwidth, Fly.io provides a pay-as-you-go model, and Cloudflare Workers offers a free tier with additional costs based on usage. Check for any updates in their deployment options and pricing models to ensure cost-effectiveness.
Then add feedback tracking (👍 / 👎 + comment) directly into Supabase, and you've got a closed learning loop.
Phase 4: Evaluate, Iterate, Repeat
We monitor:
- Response latency
- Token cost per request
- User satisfaction score
- Failure rates
If something fails, we can replicate and improve it fast — because the entire chain and its state are logged and replayable.
The Real Advantage
This setup lets small teams move like R&D labs.
- You can test 3 product ideas in a week.
- Discard the ones that don't resonate.
- Double down on the one that does.
- Deploy the working one straight into production.
It's not "move fast and break things."
It's move fast, measure everything, and keep what works.
Recent Advancements in AI Prototyping
Recent advancements in AI prototyping include improved model accuracy and reduced latency, making it easier to deploy AI solutions at scale. Tools like LangChain have introduced new features for better orchestration, and Supabase continues to enhance its real-time capabilities, making it an increasingly attractive choice for developers. Additionally, frameworks such as Hugging Face Transformers have seen significant improvements, offering faster inference times and more efficient model deployment options.
Final Thought
Building AI prototypes used to take weeks of messy notebooks, broken APIs, and brittle prompts.
Now, with LangChain + Supabase + FastAPI, it's a system.
Fast. Observable. Repeatable.
The real secret?
We treat prototyping like production — and that's why our prototypes become products.
For further reading, explore our articles on rapid AI prototyping and LangChain integration. Stay updated with the latest in FastAPI deployment techniques.
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