Maintaining a consistent and engaging personal presence across digital interactions is challenging, especially when real-time communication demands require a high degree of fidelity in appearance and expression. Existing solutions often fail to capture the nuances of human interaction, leading to a disconnect between a user's intended digital persona and its actual representation.

Pickle provides an AI-powered platform for creating and deploying personalized digital clones, enabling users to maintain an authentic and dynamic online presence. The core technology involves a real-time interactive avatar model that accurately replicates a user's voice, lip-sync, and emotional expressions. This avatar can be streamed directly into video conferencing applications via the Pickle Camera, ensuring users always present their best digital self. Additionally, the platform is developing Pickle Glass, an AI desktop assistant designed to offer proactive, context-aware support by observing screen content and audio input. The ultimate goal is to facilitate seamless, human-like digital interactions through advanced AI and extensive conversational data training.

Saram-1 Interactive Avatar Model: Utilizes a real-time, personalized avatar model for high-fidelity lip-syncing, trained on extensive conversational data for nuanced emotional resonance.

Pickle Camera: Enables streaming of the digital clone into video calls, facilitating real-time lip-syncing driven by the user's voice.

Pickle Glass: An open-source, proactive AI desktop assistant that provides real-time assistance based on screen content and audio input, designed for background operation.

End-to-End Training Pipeline: Employs a data-driven approach, training models on millions of hours of conversational data to achieve human-like interaction quality.

Low-Latency Inference: Achieves sub-400ms latency through techniques like FP8 TensorRT quantization, model pruning, and optimized rendering architectures (NeRF and DiT) for seamless real-time performance.

Personalization via LoRA: Leverages Low-Rank Adaptation (LoRA) for fine-tuning foundation models with user-specific video data, capturing individual facial features, gestures, and expressions.

Contextual Awareness: Integrates context projectors and multi-modal LLM layers to extract and utilize conversational context for more natural avatar behavior.

The platform targets individuals and professionals seeking to enhance their digital presence in video calls and online interactions, as well as users interested in AI-powered desktop assistance.