How to Build a Private AI Assistant for Your Business (Complete Guide)

Where users interact with your AI. We recommend Telegram for maximum security and flexibility.

• Telegram Bot API (or Slack/Teams SDK)
• Webhook receiver for incoming messages
• Message queue for handling concurrency

The brain of your assistant. Runs on your infrastructure (AWS/GCP/Azure or on-premise).

• LLM endpoint (OpenAI API, Anthropic Claude, or self-hosted Llama)
• Prompt engineering layer
• Context management system
• Rate limiting and security controls

Where your business knowledge lives. This is what makes the AI "yours."

• Vector database (Pinecone, Weaviate, or Qdrant)
• Document embeddings pipeline
• Semantic search for retrieval
• Auto-updating from your document sources

Ensures data never leaks and meets regulatory requirements.

• End-to-end encryption (at rest and in transit)
• Access control and authentication
• Audit logging for compliance
• Data retention policies (GDPR Article 17)

# Create bot via @BotFather on Telegram
# You'll receive an API token

# Set up webhook endpoint (Next.js API route)
// app/api/telegram/webhook/route.ts

import { NextRequest, NextResponse } from 'next/server';

export async function POST(req: NextRequest) {
  const update = await req.json();

  // Verify request is from Telegram
  const token = process.env.TELEGRAM_BOT_TOKEN;

  // Process message
  const message = update.message;
  if (message?.text) {
    await processUserMessage(message);
  }

  return NextResponse.json({ ok: true });
}

async function processUserMessage(message: any) {
  // Send to AI processing layer
  const response = await queryPrivateAI(message.text);

  // Send response back to user
  await sendTelegramMessage(message.chat.id, response);
}

// Embed your business documents
import { OpenAIEmbeddings } from '@langchain/openai';
import { Pinecone } from '@pinecone-database/pinecone';

async function ingestDocuments(documents: Document[]) {
  const embeddings = new OpenAIEmbeddings({
    apiKey: process.env.OPENAI_API_KEY,
  });

  const pinecone = new Pinecone({
    apiKey: process.env.PINECONE_API_KEY,
  });

  const index = pinecone.index('private-knowledge-base');

  // Process documents in batches
  for (const doc of documents) {
    const embedding = await embeddings.embedQuery(doc.content);

    await index.upsert([{
      id: doc.id,
      values: embedding,
      metadata: {
        content: doc.content,
        source: doc.source,
        timestamp: Date.now(),
      }
    }]);
  }
}

async function queryPrivateAI(userQuery: string) {
  // 1. Search your knowledge base
  const relevantDocs = await searchVectorDB(userQuery);

  // 2. Build context from retrieved documents
  const context = relevantDocs.map(doc => doc.content).join('\n\n');

  // 3. Query LLM with context
  const response = await callLLM({
    system: `You are a private AI assistant with access to company knowledge.

CONTEXT FROM KNOWLEDGE BASE:
${context}

INSTRUCTIONS:
- Only answer based on the provided context
- If information isn't in the context, say "I don't have that information"
- Never make up information
- Maintain confidentiality of all business data`,
    user: userQuery,
  });

  return response;
}

async function callLLM(messages: any) {
  // Use OpenAI, Anthropic, or self-hosted model
  const response = await fetch('https://api.anthropic.com/v1/messages', {
    method: 'POST',
    headers: {
      'x-api-key': process.env.ANTHROPIC_API_KEY,
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      model: 'claude-3-5-sonnet-20241022',
      max_tokens: 1024,
      messages: [
        { role: 'user', content: messages.user }
      ],
      system: messages.system,
    }),
  });

  const data = await response.json();
  return data.content[0].text;
}

• API calls are NOT used for model training (check terms of service)
• Data is encrypted in transit (HTTPS/TLS 1.3)
• Implement request/response logging for audit trails
• Set data retention policies (automatic deletion after X days)