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.
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
Challenge 01
High Computational Resource Demands
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.
#1
Challenge 02
Identifying Optimal Models
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.
#2
Challenge 03
Ensuring Corporate Data Security
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.
#3
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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Inquire for more infoSolution
01
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
02
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
03
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
04
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
Results & business impact
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.
