Overview

The client is a U.S.-based academic research institute specializing in human-AI collaboration. Renowned for its involvement in federally funded projects, the organization explores advanced technologies such as natural language processing, machine learning, and cognitive interaction. With a strong focus on applied research, they are committed to bridging the gap between theoretical AI concepts and practical, real-world use cases, supporting innovation in both academic and applied research environments.

Technical Stack

  • Python
  • PyTorch
  • Hugging Face Transformers
  • OpenAI APIs
  • Stable Diffusion
  • LLaMA2
  • LangChain

    • Industry

    Education

  • region

    • Region

    USA

  • project-size

    • Project Size

    Non- Disclosable

Highlights

Implemented multi-modal GenAI support for text, image, video, and animation generation

Fine-tuned LLMs for custom content generation tasks

Created a scalable data pipeline and embedding system for efficient retrieval

Conducted benchmarks across open and closed-source models for feasibility analysis

Challenges & Solutions

The researcher required a platform that could evaluate different GenAI models and determine which ones performed best for specific content generation tasks.

  • Solution: We created a benchmarking framework that tested various LLMs and GenAI tools (OpenAI, Stable Diffusion, LLaMA2, etc.) on a curated dataset. Metrics such as response accuracy, latency, and creative variance were tracked to support informed decision-making.

The project involved generating and retrieving context-aware content across multiple formats.

  • Solution: Our LLM engineers designed a unified content embedding pipeline using Faiss and LangChain to generate, index, and retrieve contextual content quickly and accurately. This allowed seamless interaction between users and the system, especially in iterative research scenarios.

The researcher needed support with technical documentation, feasibility studies, and biosketch materials for the grant process.

  • Solution: Our team provided clean, grant-ready technical documents, including system architecture diagrams, experimental results, and model comparison charts. We also helped format the biosketch and prepared a formal letter of participation to fulfill grant eligibility.

Collaboration between research and technical teams had to be closely aligned despite differing working styles.

  • Solution: We maintained consistent communication with the lead researcher through bi-weekly meetings and shared task boards. This ensured that implementation aligned with research goals and allowed agile adjustment of model configurations and experiments.

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Core Features

  • Fine-tuned LLMs for specific interactive content tasks
  • Multi-format GenAI integration (text, image, video, animation)
  • Real-time content embedding and retrieval
  • Grant-ready technical documentation and feasibility analysis
  • no.-of-resources

    • No. of Resources

    02

  • time-frame

    • Time Frame

    February 2025 - June 2025

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