LexiaCore is the AI that powers every Steel Taxas product — and now it's available as a platform for developers and enterprises who want to build at the frontier.
A unified AI stack running from the silicon to the cloud — built for real-time, real-world intelligence.
LexiaCore ingests data from every available sensor — cameras, microphones, lidar, IMUs, environmental sensors. Raw signal becomes structured understanding in milliseconds.
A multimodal transformer architecture fuses all sensor streams and applies contextual reasoning. It knows not just what it sees, but what it means in context.
Decisions are made on-device in under 20ms. LexiaCore drives actuators, triggers automations, and continuously refines its model based on outcomes.
With privacy-preserving federated updates, LexiaCore grows smarter with every interaction — without your data ever leaving your device.
Object detection, semantic segmentation, depth estimation, and scene understanding — all running locally at 60fps on our neural silicon.
Far-field voice recognition with speaker identification, ambient noise suppression, and natural language understanding in 47 languages.
Simultaneous processing of heterogeneous sensor streams — lidar, IMU, thermal, chemical — into a coherent world model.
LexiaCore maintains a persistent, privacy-first memory of your preferences, routines, and environment to continuously personalize behavior.
Our custom neural silicon runs full LexiaCore inference at under 5 watts. Zero cloud dependency. Zero latency. Zero compromise on privacy.
Access the same AI powering millions of Steel Taxas devices through our enterprise API. Integrate multimodal intelligence into your products with a few lines of code.
from lexiacore import LexiaClient client = LexiaClient( api_key="lx_your_key_here" ) # Analyze a scene from your robot vacuum response = client.vision.analyze( image="./kitchen_frame.jpg", tasks=[ "obstacle_detection", "floor_type_classification", "dirt_heatmap" ] ) print(response.obstacles) # → [Chair(conf=0.98), Dog(conf=0.96)] print(response.floor_type) # → "hardwood"