Generative AI - Technology and Tools

We work with our clients to develop a comprehensive Generative AI strategy that aligns with business goals and objectives. Our technology stack is designed to provide robust and scalable AI solutions, leveraging the latest advancements in the field

We help our clients leverage the power of Generative AI and cloud computing to improve their operational efficiency, reduce costs, and massively enhance their ability to innovate.

Open-Source Models: We utilize cutting-edge open-source LLMs to remain at the forefront of AI technology. This approach allows us to integrate the most effective methods and models into our solutions, ensuring that our clients benefit from the latest developments in AI research.

Inferencing optimization is done with the help of tools such as vLLM.

NVIDIA Inference Microservices (NIM): Efficient inferencing and hosting at scale using NVIDIA NIM ensures that our models deliver high performance with minimal latency, which is essential for real-time applications.

TensorRT LLM: Enhancing the performance of real-time applications with optimized inference capabilities, TensorRT LLM ensures that our solutions are both powerful and efficient.

Retrieval-Augmented Generation (RAG): This framework enhances the capabilities of LLMs by integrating them with external knowledge sources, ensuring precise and contextually relevant responses.

Fine-tuning models to align with specific corporate goals and operational contexts. This ensures that each AI solution is uniquely adapted to the client’s needs, enhancing the relevance and effectiveness of the generated outputs. The company will own its own custom LLM within the safety of their corporate firewalls, and at much faster speeds.

Advanced Training Techniques: Utilizing techniques such as QLoRA (Quantized Low-Rank Approximation) and GGUF (4-bit integer quantization), we optimize opensource LLM model performance in terms of accuracy and speed, while significantly reducing operational costs. These advanced methodologies allow for efficient model hosting and execution, making real-time applications more feasible and cost-effective.