Barclays Ideas Generator - Current Architecture & 2025 Transition Plan

Current Application Overview

The Barclays Ideas Generator is an enterprise-grade conversational AI platform that provides employees with access to multiple AI assistants for ideation and business support. The system uses a sophisticated cloud-based architecture with proper authentication and security measures.

Key Features

• Multiple AI Assistants: Users can select from different pre-configured AI assistants with unique personalities and capabilities
• Conversation Management: Persistent chat threads with history, titles, and deletion capabilities
• Tone of Voice Control: Selectable tone-of-voice options to customize AI responses
• Security-First Design: Bank detail masking, cybersecurity term filtering, and enterprise authentication
• Responsive Interface: Clean, modern chat interface with sidebar navigation

Current Architecture

Frontend (Web App) → GCF Proxy → Make.com Webhook → AI Service → Response Chain
                   ↓
            Microsoft Azure AD (Authentication)

Frontend Components

Core Files:

• index.html - Main application shell with Microsoft MSAL authentication
• js/script.js - Core application logic and AI interaction
• js/variables.js - Configuration variables (Make.com webhook URL)
• js/html.js - HTML templates for dynamic content
• style.css - Application styling

Key JavaScript Functions:

• sendMessage() - Handles user input and AI communication
• getAssistants() - Retrieves available AI assistants
• getConversations() - Loads conversation history
• maskUKBankDetails() - Security function for data sanitization

Backend Services

Google Cloud Function Proxy (GCF/index.js):

• CORS handling for cross-origin requests
• Authentication token management
• Request forwarding to Make.com webhook

Make.com Integration (js/variables.js):

• Primary AI processing endpoint: https://hook.us1.make.celonis.com/htn0fepeoai19d1unx6fqm5qd5ptk5px
• Handles conversation management, assistant selection, and AI responses

API Endpoints:

• ?GetConversations=True - Retrieve user conversations
• ?GetAssistants=True - Get available AI assistants
• ?GetMessages=True&ConversationID={id} - Load conversation history
• ?DeleteConversation=True&ConversationID={id} - Remove conversations
• Main chat endpoint with parameters: ConversationID, AssistantKey, TOV_Key, Message

Authentication & Security

Microsoft Azure AD Integration:

• Client ID: 9079054c-9620-4757-a256-23413042f1ef
• Tenant ID: e519c2e6-bc6d-4fdf-8d9c-923c2f002385
• Redirect URI: https://ai-sandbox.oliver.solutions/ideas-sparkplug/index.html

Security Features:

• Data Sanitization: Automatic masking of UK banking details (sort codes, account numbers, card numbers)
• Content Filtering: Cybersecurity term detection and masking
• Session Management: Secure cookie handling with HttpOnly, Secure, and SameSite flags
• CORS Protection: Configured allowed origins
• Authentication Required: All features require Microsoft AD login

Current Limitations

1. Indirect AI Integration: Uses Make.com webhook instead of direct OpenAI API calls
2. Complex Architecture: Multiple proxy layers add latency and complexity
3. Limited Control: Cannot easily customize AI behavior or access advanced features
4. Dependency on External Services: Reliant on Make.com and Google Cloud Functions
5. Authentication Overhead: Enterprise authentication may be excessive for development

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2025 Transition Plan: From OpenAI Assistants to Direct API Integration

Transition Overview

Goal: Migrate from the current Make.com webhook architecture to direct OpenAI API completions for better control, performance, and feature access.

Phase 1: Local Development Setup (Week 1)

1.1 Authentication Bypass for Development

Files to modify:

• index.html (lines 11-32, 68-127) - Comment out MSAL authentication
• Create development flag to bypass login requirements
• Replace thisUser with a default development user

1.2 Local Backend Creation

New files to create:

• server/ directory with Node.js/Express backend
• server/index.js - Main server file with OpenAI integration
• server/config.js - Configuration management
• server/routes/ - API route handlers
• server/package.json - Dependencies

Key dependencies:

{
  "express": "^4.18.2",
  "cors": "^2.8.5",
  "openai": "^4.0.0",
  "dotenv": "^16.3.1",
  "express-rate-limit": "^7.1.5"
}

1.3 Environment Configuration

New files:

• .env - OpenAI API key and configuration
• .env.example - Template for environment variables

Phase 2: API Endpoint Migration (Week 2)

2.1 Replace Make.com Webhook

Modify js/variables.js:

// OLD:
const make_url = "https://hook.us1.make.celonis.com/htn0fepeoai19d1unx6fqm5qd5ptk5px";

// NEW:
const make_url = "http://localhost:3000/api";

2.2 Create Local API Endpoints

Endpoint mapping:

Current Make.com Parameter	New Local Endpoint	Method
?GetConversations=True	/api/conversations	GET
?GetAssistants=True	/api/assistants	GET
?GetMessages=True&ConversationID={id}	/api/conversations/{id}/messages	GET
?DeleteConversation=True&ConversationID={id}	/api/conversations/{id}	DELETE
Main chat endpoint	/api/chat	POST

2.3 OpenAI Integration Strategy

From OpenAI Assistants to Chat Completions:

Current (via Make.com):

Assistant ID → Make.com → OpenAI Assistants API

New (direct):

// Direct OpenAI Chat Completions
const completion = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [
    { role: "system", content: assistantSystemPrompt },
    ...conversationHistory,
    { role: "user", content: userMessage }
  ],
  temperature: 0.7,
  max_tokens: 1000
});

Phase 3: Data Storage Implementation (Week 3)

3.1 Local Database Setup

Options:

1. SQLite (recommended for development) - File-based, zero-config
2. JSON files - Simple file storage for prototyping
3. PostgreSQL - Full database for production-ready features

3.2 Data Models

Conversations:

{
  id: string,
  title: string,
  assistant_key: string,
  tov_key: string,
  user_id: string,
  created_at: timestamp,
  updated_at: timestamp
}

Messages:

{
  id: string,
  conversation_id: string,
  role: 'user' | 'assistant',
  content: string,
  created_at: timestamp
}

Assistants:

{
  key: string,
  name: string,
  system_prompt: string,
  initial_message: string,
  model: string,
  temperature: number
}

Phase 4: Feature Enhancement (Week 4)

4.1 Assistant Configuration

Replace hardcoded assistant selection with configurable system prompts:

Current approach:

• Assistant selection via assistant_key
• Limited customization through Make.com

New approach:

const assistants = [
  {
    key: "creative_ideation",
    name: "Creative Ideation Assistant",
    system_prompt: "You are a creative business ideation assistant...",
    model: "gpt-4o",
    temperature: 0.8
  },
  {
    key: "analytical_advisor",
    name: "Analytical Business Advisor", 
    system_prompt: "You are a data-driven business analyst...",
    model: "gpt-4o",
    temperature: 0.3
  }
];

4.2 Enhanced Features

• Streaming Responses: Real-time message streaming
• Custom System Prompts: Easily editable assistant personalities
• Conversation Export: Export chat history as PDF/JSON
• Advanced Filtering: Better content filtering and compliance
• Usage Analytics: Track API usage and costs

Phase 5: Security & Compliance (Week 5)

5.1 Data Protection

• Maintain existing maskUKBankDetails() function
• Add configurable content filtering
• Implement request/response logging for compliance

5.2 Rate Limiting & Safety

// Rate limiting
const rateLimit = require('express-rate-limit');
const apiLimiter = rateLimit({
  windowMs: 15 * 60 * 1000, // 15 minutes
  max: 100 // limit each user to 100 requests per windowMs
});

// Content safety
const moderationResult = await openai.moderations.create({
  input: userMessage
});

Implementation Checklist

Week 1: Foundation

• Create new branch ideas-gen-2025
• Set up local Node.js backend structure
• Install required dependencies
• Create basic Express server
• Comment out authentication for development
• Test basic frontend-backend communication

Week 2: API Migration

• Create conversation management endpoints
• Implement assistant selection API
• Set up OpenAI API integration
• Replace Make.com webhook calls
• Test message sending/receiving
• Implement basic error handling

Week 3: Data Persistence

• Choose and set up database solution
• Create data models and schemas
• Implement conversation storage
• Add message history functionality
• Test data persistence across sessions

Week 4: Feature Enhancement

• Implement configurable assistants
• Add tone-of-voice customization
• Create conversation export features
• Add streaming message responses
• Implement usage tracking

Week 5: Security & Polish

• Add comprehensive content filtering
• Implement rate limiting
• Add request logging
• Create admin panel for assistant management
• Performance optimization
• Documentation updates

Questions for Clarification

1. OpenAI Model Preferences: Which OpenAI models would you prefer? (GPT-4o, GPT-4o-mini, etc.)
2. Database Choice: SQLite for simplicity or PostgreSQL for features?
3. Assistant Configuration: How many pre-configured assistants do you want?
4. Streaming: Should we implement real-time streaming responses?
5. Authentication Timeline: When do you want to re-enable authentication?
6. Deployment: Local development only or eventual cloud deployment?
7. Legacy Support: Should we maintain backward compatibility with existing conversations?

Benefits of the New Architecture

1. Direct Control: Full control over AI behavior and responses
2. Better Performance: Eliminate proxy layers and reduce latency
3. Cost Optimization: Direct API usage for better cost management
4. Enhanced Features: Access to latest OpenAI features and models
5. Easier Debugging: Local development and debugging capabilities
6. Customization: Easy assistant personality and behavior modification
7. Data Ownership: Complete control over conversation data

This transition plan provides a clear path from the current Make.com-based architecture to a modern, direct OpenAI integration while maintaining the existing user experience and security standards.