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About Us is a cutting-edge service that allows companies to host and train a private Large Language Model (LLM) within their own secure environment, keeping their valuable data and ideas completely confidential. With the power of an LLM at their disposal, companies can enjoy all the productivity benefits that come with natural language processing, without the risk of exposing sensitive information to third parties. Whether you’re looking to streamline your customer service, optimize your internal operations, or gain insights from vast amounts of data, is the perfect solution for your business. Contact us today to learn more about how we can help you achieve your goals while keeping your confidential information safe and secure.

What We Do

Maintenance Assistant

Train an LLM using your own maintenance manuals, and maintenance logs, including data for proprietary machines or processes that you do not want exposed publicly.

Customer Support Assistant

Train an LLM to assist your customer service staff to respond to customer questions, including specific information from your own help desk tickets from the past, you current policies and procedures while keeping your proprietary knowledge away from public LLM’s.

Programming Assistant

Train an LLM on your own source code, your standards and conventions used by your development team so the coding assistant generates standards-compliant code that works well and fits in with the rest of your development library.


Legal Firms

Law firms can use’s language model training services to create a chat assistant that will help them analyze legal documents more efficiently.  Their sensitive data that cannot be shared publicly will be kept private and secure.

Healthcare Industry

Healthcare organizations handle a vast amount of sensitive data that needs to be kept confidential. can be used to create custom language models to analyze patient records, identify patterns and insights, and improve patient outcomes. 


Financial Services can help financial organizations develop custom language models that can analyze financial data, detect fraud and anomalies, and provide insights to improve financial performance and customer service.

Government Agencies

Government agencies handle sensitive data that needs to be kept confidential, and using can help them create custom language models to analyze data from various sources and improve decision-making processes.

Marketing and Advertising

Marketing and advertising agencies often analyze vast amounts of consumer data to develop targeted marketing campaigns. By using’s secure hosting and language model training services, marketing agencies can ensure that their clients’ data remains confidential while still being able to analyze it effectively. can benefit any organization that handles sensitive data and needs to ensure that it remains confidential. By leveraging’s language models and secure hosting services, organizations can improve their data analysis capabilities, make better decisions, and improve overall performance without exposing their valuable and sensitive data to public language models.


A typical project flow for a customer of would include:



During the discovery phase, the customer and team will work together to identify the specific use case for the LLM, as well as any data sources that will be needed. This may involve conducting a thorough analysis of the customer's existing data and workflows.


Data preparation

Once the use case has been identified, the customer will need to provide with the necessary data. This may involve cleaning and structuring the data, as well as identifying any relevant data subsets.


Model training

Once the data has been prepared, the LLM will be trained on the customer's specific use case. This may involve fine-tuning an existing language model, or training a new model from scratch.


Once the LLM has been trained, it will be evaluated to ensure that it meets the customer's requirements. This may involve testing the model against a set of pre-defined metrics, or conducting user acceptance testing.


Once the LLM has been evaluated and approved, it will be deployed to your own private cloud server. This may involve integrating the model with existing systems, as well as providing training and support to end-users.



After deployment, will continue to work with the customer to ensure that the LLM remains up-to-date and continues to meet their evolving needs. This may involve re-training the model on new data, as well as making updates to the underlying architecture or infrastructure as needed.

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