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Harness Large Language Models for Advanced Language Processing

Join the era of large language model-driven innovation with QBurst. Build and integrate LLM-powered applications into existing workflows for more context-aware interactions.

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Our Services

Consultation and
Strategy

Our consultants will study your business and identify areas where LLMs can add value, suggest.. a suitable large language model, the fine-tuning required, and the proper point of integration with your existing processes.
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Custom Solution
Development

We develop NLP solutions where the LLMs are fine-tuned on task-specific data to be customized.. for your business. Our experts will assess your workflow to identify the optimal integration points and seamlessly incorporate the solution into your existing applications, tools, or website.
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Prompt
Engineering

Our prompt engineers are skilled in creating prompts that serve as navigational cues for LLMs.. , ensuring that the responses generated are aligned with the specific context and goals of the intended application.
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LLM-Powered Application
Development

From selecting a foundational model to fine-tuning it and hosting and deploying the solution.. , our end-to-end application development service will ensure you get the best out of your LLM-based app.
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Open-Source and
Cloud LLM Support

We customize open-source LLMs for specific applications and provide support to set up the hosting.. infrastructure, ensuring scalability and performance. Additionally, for cloud-based LLMs we can implement privacy controls to protect sensitive data.
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Consultation and Strategy

Our consultants will study your business and identify areas where LLMs can add value, suggest a suitable large language model, the fine-tuning required, and the proper point of integration with your existing processes.

Custom Solution Development

We develop NLP solutions where the LLMs are fine-tuned on task-specific data to be customized for your business. Our experts will assess your workflow to identify the optimal integration points and seamlessly incorporate the solution into your existing applications, tools, or website.

Prompt Engineering

Our prompt engineers are skilled in creating prompts that serve as navigational cues for LLMs, ensuring that the responses generated are aligned with the specific context and goals of the intended application.

LLM-Powered Application Development

From selecting a foundational model to fine-tuning it and hosting and deploying the solution, our end-to-end application development service will ensure you get the best out of your LLM-based app.

Open-Source and Cloud LLM Support

We customize open-source LLMs for specific applications and provide support to set up the hosting infrastructure, ensuring scalability and performance. Additionally, for cloud-based LLMs we can implement privacy controls to protect sensitive data.

Large Language Model Use-Cases

NLP
Tasks

  • Sentiment Analysis
  • Language Translation
  • Text Summarization
  • Information Extraction

Content
Generation

  • Marketing Materials
  • Product Descriptions
  • Advertisements
  • Newsletters

Customer
Service

  • Virtual Assistants
  • Chatbots
  • Recommendation Engines

Information
Retrieval

  • Search Engines
  • Question-Answering Systems

Large Language Model Operations

Putting a large language model into use involves a series of steps. By meticulously executing them, we can help you effectively leverage LLMs for your use case.

Data Collection and Preprocessing Data Collection and Preprocessing

Identify relevant and quality data. Create repeatable, editable, and shareable data sets to iteratively prepare data for product lifecycle. Steps are taken to protect sensitive data.

Prompt Engineering Prompt Engineering

Provide instructions, examples, or constraints to ensure that the LLM generates responses that are specific to the application.

Fine-Tuning Fine-Tuning

Train using the task-specific dataset. This helps the model adapt its knowledge and learn to generate contextually relevant responses.

Integration Integration

Incorporate the LLM into existing systems and workflows using APIs.

Deployment Deployment

Whether it's on-cloud, on-premises, or a hybrid solution, we design and configure efficient architectures to ensure scalability, reliability, and resource optimization.

Data Collection and Preprocessing

Identify relevant and quality data. Create repeatable, editable, and shareable data sets to iteratively prepare data for product lifecycle. Steps are taken to protect sensitive data.

Prompt Engineering

Provide instructions, examples, or constraints to ensure that the LLM generates responses that are specific to the application.

Fine-Tuning

Train using the task-specific dataset. This helps the model adapt its knowledge and learn to generate contextually relevant responses.

Integration

Incorporate the LLM into existing systems and workflows using APIs.

Deployment

Whether it's on-cloud, on-premises, or a hybrid solution, we design and configure efficient architectures to ensure scalability, reliability, and resource optimization.

Popular LLMs

lama
dolly
palm
t5
gpt4
stability
gpt3
whisper

LLMs are pre-trained on a large corpus of data that can be used for a wide range of downstream tasks. Depending on your requirements, resources, and bandwidth, we can help you find the one that fits your needs. A quick comparison between open-source and closed-sourced models is provided below:

Open-Source Models Closed-Source Models
Open-source models, such as StableLM, Llama 2, and XLNet, are freely available for anyone to access and use. For closed-source models like GPTs, users may be subject to licensing fees or usage restrictions.
Open-source models can be hosted locally to ensure that confidential information is not sent to any third parties. Closed-source models require all data to be sent to third-party servers, which potentially raises concerns about data privacy and security.
Open-source models give developers more freedom to diagnose and fix any errors or biases. Closed-source models are less transparent, which can make it difficult for developers to diagnose and correct biases in them.
Open-source models are often more customizable, allowing companies to tailor the models to their specific needs and use cases. Companies may have less control over the development of closed-source models and may need to invest more resources into customizing them for their specific needs.

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