Communication,
Re-engineered.
The first zero-latency neural engine for African Sign Languages.
Offline-first. Privacy-centric. Built for the edge.
System Architecture
Optimized for constrained environments. Designed for scale.
Edge Inference
Models execute locally via quantized TensorFlow Lite. Zero dependency on cloud connectivity for core translation tasks.
Dialect-Aware Models
Proprietary datasets covering SASL, KSL, and NSL variants. Continuously retrained on diverse regional inputs.
Local-Only Processing
Video frames are processed in volatile memory. No biometric data is persisted or transmitted to servers.
Real-time Synthesis
Sub-50ms text-to-speech generation. Optimized for natural conversation cadence and low CPU overhead.
Adaptive Compute
Dynamic model scaling based on device thermal state and battery level. Maintains performance on low-end hardware.
Mobile SDK
Integration support for telemedicine and ed-tech platforms. Modular architecture for rapid deployment.
Input Normalization
Raw video feed is captured. Preprocessing pipeline handles noise reduction, lighting correction, and frame stabilization.
Spatial Analysis
Transformer models map keypoints for hands, face, and body pose. Temporal sequences are analyzed for gesture context.
Semantic Mapping
Gestures are converted to semantic tokens, then synthesized into natural language text and audio output.
Technical Benchmarks
High-fidelity sign language recognition utilizing advanced feature engineering.
eKitabuFeature Extraction Analysis
Ready to build?
We are currently rolling out SDK access to select partners. Apply for the private beta to get your API keys.