How do you train an AI chatbot on our private company data?

We convert your manuals and articles into mathematical vectors, store them in a database, and use RAG (Retrieval-Augmented Generation) to feed relevant facts to the AI.

How do we secure user data sent to the OpenAI API?

We use enterprise endpoints that guarantee user prompts are never used to train public AI models.

OpenAI & LLM API Integration Services

Custom chatbot prompts, text parsing, and secure vector setups.

Integrating large language models (LLMs) can automate B2B customer support, content creation, and data parsing. CodeHTML connects custom web applications with OpenAI (GPT-4), Anthropic (Claude), and local vector databases to build smart search tools and assistants.

Core Features

OpenAI and Anthropic API connections.
Custom system prompts and context steering.
Semantic document search (Vector databases).
Streaming server responses for instant chats.

Developer Reference Code

// api/chat.js
import OpenAI from 'openai';
const openai = new OpenAI();

export async function POST(req, res) {
  const response = await openai.chat.completions.create({
    model: 'gpt-4',
    messages: [{ role: 'user', content: req.body.prompt }],
    stream: true
  });
  // Stream response back to React frontend
}

Quality Guarantee

We optimize prompt tokens and caching to keep your monthly API costs low while maintaining fast response speeds.

Frequently Asked Questions

Q: How do you train an AI chatbot on our private company data?: A: We convert your manuals and articles into mathematical vectors, store them in a database, and use RAG (Retrieval-Augmented Generation) to feed relevant facts to the AI.
Q: How do we secure user data sent to the OpenAI API?: A: We use enterprise endpoints that guarantee user prompts are never used to train public AI models.