What technologies were used in this project?

The project utilized vLLM for efficient model serving, Open WebUI for the user interface, Mixtral 8x7B and Mistral 7B open-source language models, LoRA fine-tuning for domain adaptation, model quantization for resource optimization, and enhanced RAG techniques for improved query handling.

What was the main challenge in developing this secure LLM solution?

The main challenges included high computational resource demands for running language models locally, identifying optimal open-source models that match GPT quality, and ensuring robust corporate data security with encryption and access controls throughout the information exchange process.

What were the key results achieved?

The solution successfully replaced ChatGPT with a secure, locally hosted alternative that enhanced organizational communication, ensured strict data confidentiality, reduced reliance on external cloud solutions, and provided superior performance with a scalable framework tailored to corporate needs.

How does this solution ensure data security?

The solution ensures data security through independent local deployment on internal servers, advanced encryption protocols, robust access controls, secure integration with organizational databases, and complete control over data processing without external cloud dependencies.

A Secure LLM for Enhanced Information Sharing

Azati built a secure, locally hosted language model (LLM) that serves as a corporate-ready alternative to ChatGPT. Designed for enterprise AI solutions, this platform boosts employee productivity, enhances corporate communication, and ensures maximum data security.

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100%

data privacy compliance

60%

faster information retrieval

75%

search-time reduction

All Technologies Used

vLLM

Open WebUI

Motivation

Modern businesses face a common pain. Employees spend too much time searching for information or waiting for answers, sensitive corporate data is exposed when using cloud-based AI tools like GPT, and off-the-shelf solutions often do not fit real workflows. We built a secure, locally hosted LLM, an AI-powered corporate assistant, to solve these problems, boost employee productivity, streamline corporate communication, and keep all data fully protected.

Main Challenges

Running advanced AI models like GPT locally requires significant computing power, which can be a real hurdle for companies with limited resources, so we optimized the models with quantization and efficient deployment strategies.

Selecting an open-source LLM that matches GPT-level quality while understanding corporate context and workflows requires extensive testing, and we addressed this by evaluating multiple models and fine-tuning the best candidates.

Ensuring corporate communication remains fully secure demands robust encryption and strict access controls, which we implemented alongside seamless integration with internal systems to protect sensitive information.

Our Approach

Selection of Open-Source LLMs

We began by reviewing multiple open-source LLMs, assessing their performance, accuracy, and suitability for corporate workflows. Models like BERT, GPT-2, and others were evaluated to find the ones that best aligned with our objectives for a secure, reliable, and high-performing corporate assistant.

Fine-Tuning with LoRA

Once the base models were selected, we fine-tuned them using LoRA to adapt to our company’s specific terminology, business processes, and communication patterns. This ensured the models could provide relevant, accurate, and context-aware responses for day-to-day corporate use.

Model Quantization

To optimize memory usage and make the models run efficiently on local infrastructure, we applied quantization. This step was crucial for maintaining fast response times and operational efficiency even in resource-constrained environments.

Enhanced RAG Techniques

We improved the RAG approach to make the AI smarter and more responsive by enabling it to handle multiple queries at once, efficiently retrieve related context, and generate hypothetical questions to enrich responses. The system also extracts keywords and identifies topics to structure information, while techniques like HyDE enhance the quality of both retrieved and generated data, making the AI more accurate, context-aware, and reliable for corporate use.

Data Security Protocols

Implemented advanced encryption and access controls, ensuring secure processing and storage of corporate data.

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Solution

Independent Local Deployment

To ensure complete control over data, we installed and configured Mixtral 8x7B and Mistral 7B on the company’s internal servers. This setup allows the organization to process information securely without relying on external cloud services while maintaining superior performance and response times.

Key capabilities:

Local, secure LLM deployment
Full control over corporate data flows
Reduced risk of data leakage

Tailored Corporate Integration

We integrated the AI system seamlessly with internal databases and workflows, adapting it to the company’s specific data structures and business processes. This ensures that employees can access accurate information quickly and that knowledge is shared efficiently across teams.

Key capabilities:

Integration with internal databases and workflows
Automated, secure information sharing
Adaptation to organizational data structures

Enhanced Query Handling

The system was designed to deliver smarter, faster, and more context-aware responses. By refining query processing and improving content generation, employees receive accurate and relevant answers for daily corporate tasks.

Key capabilities:

Multi-query handling
Context-aware parent query retrieval
Hypothetical question generation for richer answers
Keyword and topic extraction for structured insights

Employee Training & Onboarding

To maximize adoption and effectiveness, we conducted comprehensive training for employees. This ensures that the team is comfortable using the system and can leverage its full potential safely and efficiently.

Key capabilities:

Interactive training sessions
Step-by-step interface walkthroughs
Best practices for safe, productive usage

Business Value

Local control over data: Deploying Mixtral 8x7B and Mistral 7B on internal servers ensures full control over corporate data, reducing reliance on external cloud services.

Enhanced performance: Local deployment delivers faster processing and more reliable performance compared to cloud-based alternatives.

Improved data security: By keeping data within the company infrastructure, risks associated with transmitting sensitive information are significantly reduced.

Operational flexibility: The system can be tailored to specific organizational workflows, allowing seamless integration and better alignment with business needs.

Increased efficiency: Employees can access accurate, context-aware responses quickly, improving productivity and decision-making in day-to-day corporate operations.

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