Replit + Ertas

Build and deploy AI-powered applications on Replit using Ertas-trained models as the intelligent backend — from prototype to production in your browser.

Overview

Replit is a cloud-based integrated development environment that lets developers write, run, and deploy code entirely in the browser. With built-in hosting, database access, package management, and collaborative editing, Replit eliminates the friction of local development setup and makes it possible to go from idea to deployed application in minutes. Its AI-assisted coding features and template ecosystem make it one of the fastest platforms for prototyping web applications, APIs, and automation scripts.

For AI application development, Replit provides a particularly compelling environment. Developers can write Python or Node.js backends that call inference APIs, build React frontends for chat interfaces, and deploy the full-stack application — all without leaving the browser. Replit's always-on hosting means your AI application stays accessible 24/7, and its secrets management ensures API keys and endpoint URLs are stored securely. The combination of zero-setup development, instant deployment, and built-in AI coding assistance makes Replit the fastest path from a fine-tuned model to a user-facing application.

How Ertas Integrates

After fine-tuning a model in Ertas Studio, you deploy it to Ertas Cloud or any publicly accessible inference endpoint. From Replit, you connect to that endpoint using the OpenAI SDK — which works in both Python and Node.js Repls — and build your application around the fine-tuned model's capabilities. Because the OpenAI SDK is a standard dependency available in Replit's package manager, setup takes seconds rather than minutes.

This workflow is ideal for rapid prototyping and demo building. A developer can fine-tune a model on customer support data in Ertas Studio, then open Replit and build a fully functional chatbot application in an afternoon — complete with a web interface, conversation history, and streaming responses. When the prototype is ready, Replit's deployment feature makes it live with a single click, providing a shareable URL that stakeholders can test immediately. The speed of this cycle — from training data to deployed application — makes the Ertas-Replit combination particularly valuable for agencies, consultants, and teams that need to demonstrate AI capabilities to clients quickly.

Getting Started

1
Fine-tune your model in Ertas Studio
Train a domain-specific model on your data. Deploy the finished model to Ertas Cloud or a publicly accessible inference endpoint.
2
Create a new Repl
Open Replit and create a new project using a Python or Node.js template. Install the OpenAI SDK as a dependency.
3
Configure API credentials
Store your Ertas Cloud endpoint URL and API key in Replit's Secrets panel. Reference them as environment variables in your code.
4
Build your AI application
Write your application logic using the OpenAI SDK pointed at your Ertas model endpoint. Build a frontend with Flask, FastAPI, or Express for user interaction.
5
Deploy and share
Deploy your Repl with one click. Share the live URL with teammates, clients, or stakeholders for immediate feedback and testing.

python

# Replit: main.py
from flask import Flask, request, jsonify, render_template
from openai import OpenAI
import os

app = Flask(__name__)

client = OpenAI(
    base_url=os.environ["ERTAS_API_URL"],  # Stored in Replit Secrets
    api_key=os.environ["ERTAS_API_KEY"],
)

@app.route("/chat", methods=["POST"])
def chat():
    user_message = request.json["message"]
    response = client.chat.completions.create(
        model="ertas-support-7b",
        messages=[
            {"role": "system", "content": "You are a helpful customer support agent."},
            {"role": "user", "content": user_message},
        ],
        temperature=0.3,
    )
    return jsonify({"reply": response.choices[0].message.content})

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8080)

Build a customer support chatbot API on Replit powered by an Ertas-trained model deployed to Ertas Cloud.