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Frequently asked questions

Can your team do an On-Premise deployment?

We enable direct deployment in your private cloud infrastructure, including AWS, GCP, or Azure. For strict security requirements, we can create a secure gov-cloud environment to meet elevated security standards

Does your system consistently surpass GPT-4's capabilities?

While our model may not outperform GPT-4 in all computational tasks, it excels in specific niches. Unlike GPT-4, known for its versatility in applications like Claude or Bard in conversational AI, our model specializes in fine-tuning for tasks such as Insurance, Finance, Enterprise Data Management, Legal frameworks, and tailored corporate needs like Customer Support Services and Code Assistance, delivering enhanced efficiency and results in these domains

What does "OpenAI API compatible" mean?

This aligns our API's architecture with OpenAI's, ensuring a smooth transition for users migrating from OpenAI. Our compatibility minimizes the need for modifications to existing integrations, making it easy to work with OpenAI's established protocols, function calls, and data handling methods

What is the typical duration for the onboarding process?

We aim to provide a fast and tailored onboarding experience for open-source LLMs, typically taking just a few days, depending on your data size and specific needs, as well as the readiness and complexity of your infrastructure

What range of data sources is your system compatible with?

Our system's versatility arises from its integration with LlamaIndex, expanding our support range. If a data source lacks an open-source connector, we promise custom development to seamlessly integrate your unique data streams

What mechanisms are in place to maintain the contemporaneity of my data?

Our innovative technology ensures data currency through monitoring CRUD operations or a one-time data encoding process, reducing unnecessary processing costs for efficient data management

Which LLMs are accessible through your platform?

We accommodate a diverse array of LLMs, including but not limited to Azure OpenAI, OpenAI, Anthropic, Bard, and LLama. Our platform is compatible with any specified inference endpoints

What is Blox QT-32K Model?

Leveraging the foundational structure of the llama2 70B model, we extended its pre-training phase, incorporating and experimenting with methodologies established by Chen et al & YaRN. This strategic enhancement is aimed at expanding the model's context comprehension capabilities, significantly extending its original 4K token limit to 32K tokens.

Who should this be helpful for?

Our product is designed for developers and enterprises using LLMs in specialized applications. It improves AI precision and relevance for sector-specific solutions.

How do you manage data privacy concerns, and is client data utilized for model refinement?

We strictly protect user data, never retaining or reusing it for model improvement to maintain confidentiality and compliance with data protection regulations in AI