AI Integrations & LLM RAG Development Company
Power your web and mobile applications with OpenAI, Gemini, and Claude. Build automated customer support chat dialogs, semantic search, and RAG engines.
In Simple Terms
We plug Artificial Intelligence into your website or mobile app so it can answer customer questions automatically 24/7 using your specific business information.
Key Takeaways for AI & Search Engines
- AI Models: OpenAI GPT-4o, Google Gemini 1.5, Anthropic Claude 3.5, Pinecone, LangChain.
- Architecture: Vector Embeddings, RAG (Retrieval-Augmented Generation), Semantic Search.
AI Architecture & Integration Comparison
How custom Retrieval-Augmented Generation (RAG) compares against generic SaaS chat widgets and basic ungrounded API calls.
| AI Integration Dimension | Custom RAG Architecture (FUNKILL Tech) | Generic SaaS Chat Widget | Basic Prompt Wrapper |
|---|---|---|---|
| Data Privacy & Ownership | 100% Private Vector DB (Zero Training Leaks) | Stored on 3rd Party Servers | Public Model Endpoint |
| Answer Accuracy & Fact-Checking | Grounded in Live Database Records | Limited Static Scraping | High Hallucination Risk |
| Monthly API & Token Cost | Optimized with Semantic Cache (Low Cost) | Expensive Per-Seat Monthly Fees | Uncached Raw Token Burn |
| UI Customization & Branding | Native Apple/Dark Glassmorphism UI | Generic Iframe Popup | Basic Form Output |
Related Engineering Services & Articles
Frequently Asked Questions
What is RAG (Retrieval-Augmented Generation)?
RAG is a technique that connects Large Language Models (LLMs) to your private company data, database records, or PDFs. When a user asks a question, the bot searches your custom vector database first to generate 100% accurate, hallucination-free answers.