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Core technology
Engineering first: measurable, regression-tested, and rollback-ready.
Large model stack
The training side uses mainstream distributed frameworks and parameter-efficient fine-tuning (LoRA / adapters). Inference is split by workload: batch documents use offline queues; online chat uses rate limits and caching. Outputs are validated with a unified JSON Schema, with automatic retries and fallback templates on failure.
Multimodal fusion
Text, vector primitives, raster images, and tables are weakly aligned in one project space; cross-page references use frame IDs and layout trees to avoid plausible but wrong cross-document bleed.
Edge computing architecture
Vision models export to ONNX / TensorRT. Edge and center sync metrics and sample fingerprints only; raw images stay on premise by default.
Data security and compliance
Private knowledge bases can run fully offline. When public LLM calls are required, we support field-level desensitization and a configurable "zero egress" mode with a local small model as fallback.