Sources Sought Commercial Space Domain Awareness Sda Tender

• Country: USA
• Summary: Sources Sought Commercial Space Domain Awareness Sda, Training Large Language Model Llm Module
• UST Ref No: 139230337
• Deadline: 30 Apr 2026
• Financier: Self Financed
• Purchaser Ownership: Government
• Tender Value: Refer Document
• Notice Type: Tender
• Document Ref. No.: FA882026PKLBAS2
• Purchaser's Detail:
  Login to see full purchaser details. Login to see full purchaser details. Login to see full purchaser details. Login to see full purchaser details. Login to see full purchaser details. Login to see full purchaser details. Login to see full purchaser details. Login to see full purchaser details. Login to see full purchaser details.

  Login to see details
• Description:
• PURPOSE/DESCRIPTION
  1. THIS IS A SOURCES SOUGHT ANNOUNCEMENT ONLY. This notice does not constitute a commitment by the Government. All information submitted in response to this announcement is voluntary, and the Government will not pay for information requested nor will it compensate any respondent for any cost incurred in developing information provided to the Government. The Government is under no obligation to acknowledge receipt of submissions or of the information received, or to provide feedback to respondents with respect to any information submitted under this announcement.
  2. This Sources Sought seeks information on commercially available large language model (LLM) trainers capable of ingesting user inputs and technical documentation to create and update training as the needs of the operational system changes. The goal is to identify commercial vendors offering LLM based trainers, today or in the future. This Sources Sought Announcement is issued solely for informational and planning purposes only and is not a solicitation.
  In your response, please answer the following questions:
  Company Information and Capabilities:
  Can you provide relevant experience and past performance with LLM training and development, or extensive experience deploying artificial intelligence/machine learning (AI/ML) solutions within a secure enclave, within at least IL5 FedRAMP environment?
  Describe your experience with fine-tuning and deploying pre-trained foundation models (e.g., Llama, GPT, Claude) for specific enterprise tasks.
  If you develop proprietary LLMs, describe the model architecture and the resources required to train it from scratch.
  
  Provide specific, quantifiable examples of how your deployed solutions have reduced training time or improved proficiency, preferably with metrics (e.g., 'reduced new-hire training from 4 weeks to 5 days'). Please provide specific examples of how your training frameworks have been deployed in other Departmen...
• Documents:

 Tender Notice