Revolutionizing the use of local Large Language Models

Empower Your Business with TieSet’s Adaptive LLM
– Absolute Security, Absolute Privacy –
Achieve Maximum Performance
with the Power of Latest LLM Technologies.
Cost-Effective, Eco-Conscious, Reliable, Uncompromised!

Secured and Local

Locally trained language models enable secure on-device processing without external data exposure. Local customization improves precision for user-specific vocabulary and tasks.

External AI Integration

We integrated Claude or OpenAI for external searches. These read-only integrations broaden our knowledge by tapping with external AI systems while avoiding sensitive data living secured local networks.

Cost effective

No Token charges, local LLMs eliminate privacy, security, and latency costs of external APIs. On-device deployment provides inexpensive customization and scalable performance gains per user.

Use your own harware or our preloaded units.

Our environmental solution lowers carbon emissions.

By utilizing a local LLM rather than relying on data center operations, AdaptiveLLM is able to greatly reduce its carbon footprint and promote more environmentally-friendly business practices. Since large language models typically require massive computing power from servers and data centers, their operation emits considerable greenhouse gases from fossil fuel-derived electricity. However, by keeping our LLM small and running it locally on more energy-efficient hardware, we bypass the carbon-intensive aspects of remote data servers and infrastructure. Research shows even a moderately complex LLM can produce over 626,000 pounds of CO2 equivalent emissions if relying on typical data center power. But our locally hosted model has decreased estimated emissions by over 90% compared to conventional, data center-dependent LLMs. As businesses and technologies universally transition toward greener solutions, our localized approach serves as a vanguard for energy-efficient natural language processing and environmentally conscious AI implementation overall.

File Management

Our file management embeds files into a semantic vector space. By tokenizing and embedding local files, the model understands conceptual relationships beyond just keywords.

Tag Management

Users tag files with department labels and model suggestions. Administrators manage department tags, tune model tagging, and control inter-department access.

File Security by Company Department

Adaptive-LLM trains a large language model on corporate files labeled by department. We leverages the language model's understanding to intelligently and securely segment files.


Local large language models (LLMs) like Adaptive-LLM can integrate with common workplace tools like Slack and Box to extend their capabilities. For example, Adaptive-LLM can connect to a company’s Slack workspace via an API and participate in conversations, offer suggestions, and answer questions. Users can mention Adaptive-LLM in any channel, and it can respond with relevant information or task automation. Similarly, Adaptive-LLM can connect to Box through its API and index files stored there. This allows Adaptive-LLM to search content in Box and summarize documents when asked. Overall, integrating local LLMs with external programs allows the AI assistant to tap into a broader range of company information and conversations, enabling more natural and seamless interactions.

What We Offer

Artificial intelligence will help everyone succeed

Locally hosted Adaptive-LLM can provide companies with a customized, secure, fast, and lower-cost solution that unlocks unique capabilities giving them an edge over competitors relying on generic third-party services.

Language Understanding

Adaptive-LLM can be applied to enhance natural language understanding tasks such as question answering, sentiment analysis, and named entity recognition.

Machine Translation

Adaptive-LLM framework provides a powerful solution for machine translation tasks by enabling customization, adaptability, and improved performance.

Information Retrieval

Adaptive-LLM can assist in developing more effective information retrieval systems by optimizing the relevance and ranking of search results.

Text Summarization

Adaptive-LLM enhances text summarization by optimizing context and data storage, resulting in improved accuracy and efficiency.

Dialogue Systems

Adaptive-LLM enhances dialog systems by dynamically adapting to diverse conversational contexts, improving understanding, and generating more contextually appropriate responses.

Sentiment Analysis

Adaptive-LLM improves sentiment analysis by adapting to different contexts and effectively incorporating domain-specific data, leading to more accurate sentiment classification.

Hardware Selection:

Hardware Selection for local LLMs

We provide cost effective Kubernetes cluster devices for efficient large language model inferencing. Key considerations include GPU memory capacity to support multiple concurrent users, model queries, as well as CPU core counts, system memory, and storage to build cost-effective cluster servers and edge devices tailored for low latency text generation and comprehension. AdaptiveLLM allows easy deployment of large language models on clusters and resource-constrained edge devices while handling load balancing, autoscaling, and efficient model splitting automatically. We require no datacenter operations for our models to be finetune or operate locally.


Up to 400 TOPS per unit


Managed Remotely


Managed Remotely

Use your own cloud services.

Running the LLM on your own infrastructure gives you full control over access, security, monitoring, etc. You don’t have to rely on or trust an external provider. 

Our Latest Projects

Next-gen innovation is capturing your knowledge securely by department.

Sensitive data remains under your control rather than being sent to a third-party system. This allows you to better manage privacy and compliance. 

Cost Savings

Excellent value

No Data Center fees for cloud APIs. Local LLMs provide excellent value, customization, and scalability per user.



specialized LLMs

Local models rapidly adapt to user-specific vocabulary for precise, tailored experiences. Fits user needs and local language patterns.


Keeping data safe.

Local language models keep data on-device, avoiding external transmission risks. Users maintain complete control over personal data.

Offline Access

Connectivity not required

Local models enable offline usage with no connectivity required. Available anytime, anywhere.

Low Latency

Instant access

With no round-trip to external servers, local LLMs offer instant, responsive inferences for seamless interactions.


Removing vulnerabilities

No external connectivity required for local LLMs, removing vulnerabilities. Users' data stays private and secure.

Why Choose Us

Experience the limitless possibilities – with Large Language Models.

Unlock the full potential of AI with customizable large language models. Achieve unprecedented levels of responsiveness, precision, and capability by training models locally on your data. Experience groundbreaking on-device intelligence tailored precisely to your needs and use cases, opening up limitless possibilities.

Customized Intelligence

Train large language models on your local data to customize them precisely to your needs. By tuning models on your specific vocabulary and use cases, you enable them to understand your world better than anyone else. Unlock tailored intelligence that improves every interaction.

Responsive and Secure

Keep your data fully private while deploying large language models locally. With on-device processing, you get lightning fast responsiveness without any external connectivity. Experience the seamless power of AI that protects your privacy, security, and ownership of data.


What they say about ADAPTIVE LLM

George D. Coffey California

Having access to a local LLM that is customized to my needs has been a game-changer. I can get tailored responses to my prompts instantly without having to rely on a generic cloud-based model

Valorie A. Woods Orange County

The ability to keep my conversations with the LLM private and secure by running it locally has given me peace of mind. I don't have to worry about my data being exposed or used without my consent."

Harold K. Grimm San Diego

Training the local LLM on my own datasets has allowed me to get much more relevant and nuanced results. I'm amazed at how well it understands the specifics of my work now."

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