Question 1

What is LLM integration and why does my business need it?

Accepted Answer

LLM integration connects large language models (like GPT-4o or Claude) directly to your proprietary company data, databases, and software. Instead of relying on generic knowledge, the AI can now answer questions, summarize documents, and automate tasks based specifically on your internal business context.

Question 2

What is RAG and how does it prevent AI hallucinations?

Accepted Answer

RAG stands for Retrieval-Augmented Generation. When you ask a question, the system first retrieves the exact documents from your database, and then forces the AI to construct an answer based ONLY on those retrieved documents. It prevents hallucinations by providing strict, factual constraints.

Question 3

Is my company data safe when you integrate an LLM?

Accepted Answer

Yes. By default, we use Enterprise API tiers (like OpenAI Enterprise or AWS Bedrock) which guarantee that your data is never used to train their public models. For maximum security, we can also deploy open-source models (like Llama 3) entirely on your own private servers.

Question 4

Which LLM is best for enterprise — GPT-4o or Claude?

Accepted Answer

It depends on the use case. GPT-4o is excellent for complex logic, coding, and speed. Claude 3.5 Sonnet excels at analyzing massive documents and provides highly nuanced, safe responses. We are model-agnostic and will recommend the best fit for your specific requirements.

Question 5

What is the difference between RAG and fine-tuning?

Accepted Answer

Fine-tuning permanently bakes knowledge into the model's 'brain', which is very expensive ($50k+) and cannot access real-time data. RAG is like giving the AI a search engine to read your live documents before it answers. RAG is cheaper, faster, and allows for real-time updates.

Question 6

How much does LLM integration cost globally?

Accepted Answer

A foundational RAG proof-of-concept typically costs between $5,000 to $10,000. Full enterprise deployments with CI/CD pipelines, vector database hosting, and high-availability APIs generally range from $20,000 to $50,000 depending on the scale and complexity.

Question 7

Does DevAura provide LLM services in our global offices?

Accepted Answer

Yes, DevAura Technologies is serving the US, Saudi Arabia, and UAE markets. We provide enterprise-grade LLM integration, AI consulting, and RAG engineering to top local companies, as well as acting as a dedicated AI partner for businesses globally in the US, UK, and UAE.

Question 8

Can LLM be connected to our existing CRM and databases?

Accepted Answer

Absolutely. We can ingest data from almost any source: SQL/NoSQL databases, Salesforce, HubSpot, Jira, Confluence, internal APIs, or massive folders of raw PDFs and Word documents.

Question 9

Can we run the LLM privately without sending data to OpenAI?

Accepted Answer

Yes. We specialize in private, air-gapped AI deployments. We can containerize open-source models like Llama 3 or Mistral and host them directly on your secure VPC or on-premise hardware, ensuring zero data leaves your network.

Question 10

How long does LLM integration take?

Accepted Answer

We can usually deliver a working proof-of-concept (PoC) against a subset of your data in just 2 to 3 weeks. Scaling this PoC into a fully secure, production-ready enterprise application typically takes 2 to 3 months.

YourCompanyData+AI=UnbeatableCompetitiveAdvantage.

Why Pasting Your Data Into ChatGPT Is Not Enough

Generic ChatGPT.com

DevAura RAG System

Fine-Tuning Alone

How We Make AI Know Your Business

INGEST

EMBED

ANSWER

Your Data Privacy Is Our Non-Negotiable

What happens to your data on ChatGPT.com?

What about Azure OpenAI or AWS Bedrock?

What about fully private deployment?

The DevAura Default:

Choose Your Model — We Handle the Integration

GPT-4o

Claude 3.5 Sonnet

Gemini 1.5 Pro

Llama 3 70B

Mistral 7B

Azure OpenAI

The Modern AI Stack

Frameworks

Vector Databases

LLM APIs

Data Processing

Infrastructure

Our Technical Integration Process

Data Audit & Schema Mapping

Chunking Strategy & Embedding Selection

Vector Database Setup & Indexing

LLM Integration & Prompt Engineering

Accuracy Testing with RAGAS

"The AI integration saved our analysts thousands of manual hours. Every answer is cited and accurate — our team trusts it completely."

Frequently Asked Questions