Technology Vision

End-to-end pipeline optimizing monocular video sequences for 3D reconstruction, combining Camera Pose Estimation with 3D Gaussian Splatting to recover dense 3D point clouds.

Precision measurement technology aligning metric scale to reconstructed 3D space and resolving scale ambiguity, deriving Euclidean distance, height, and length between objects in real physical units.

3D spatial cognition engine quantifying volume, geometry, and occupied area of objects within reconstructed spatial data, extracting spatial measurements based on a real-world coordinate system.

Data augmentation technology generating synthetic training data with controlled object, viewpoint, lighting, and background distributions via Diffusion-based generative models, resolving domain gap and data scarcity.

Model training pipeline learning object bounding boxes and classes through label refinement and dataset curation, advancing detection precision and recall through validation and tuning cycles.

Video motion analysis technology detecting motion through optical flow and visual changes across consecutive frames, segmenting static, moving, and state-change intervals along a time series.

Construction simulation AI converting historical daily-report and schedule data into time-series features to analyze workflow, resource allocation, task sequencing, and delay factors, generating optimal schedules, work sequences, and placement rationale from BOQ inputs.

Construction-item standardization technology normalizing unstructured BOQs into standard trade, task, and material taxonomies through large-scale natural-language classification and domain-embedding similarity matching, enhancing the searchability, analyzability, and reusability of cost data.

Cost-analysis AI engine reverse-calculating per-task labor, equipment, and material inputs and their derived costs from standard estimates and BOQs, combining formula-based validation with LLM reasoning to assess input adequacy and estimate validity.

Vector retrieval model trained on domain expressions — construction documents, task, material, and work-item names — refining semantic matching through Hard Negative-based retrieval optimization, improving construction-domain search accuracy by over 20% against general-purpose models.

Document-specialized Agent recognizing the schema and metadata of each document type — daily reports, project schedules, BIM drawings — and delivering answers and references more precise than general-purpose chatbots through structure-aware retrieval, filtering, and multi-step reasoning.

Construction-specialized knowledge chatbot structuring domain documents — specifications, standard estimates, regulations, and guidelines — and delivering precise, fully-referenced answers through an Agentic RAG pipeline built on Query Decomposition, Hybrid Retrieval, Reranking, and evidence tracing.