TwinKnowledge moves beyond general-purpose AI with a proprietary multimodal pipeline built for the messy reality of construction documentation interpreting the relationship between textual and graphical project data across the full asset lifecycle.
A multi-layered technical approach purpose-built for the graphical and textual data inherent in AEC workflows, not a general model bent to fit.
Our computer-vision models parse drawing sets in their native format, recognizing title blocks, plan views, and the symbolic dependencies between details and schedules that general models cannot read.
A custom retrieval-augmented generation system combined with state-of-the-art LLMs specialized in the languages of construction, CSI MasterFormat, complex building codes, and spec terminology so natural-language queries return context-aware answers.
Base models are fine-tuned for every client and project, accounting for the unique visual orientation and representation styles that vary firm to firm reaching near-human levels of comprehension.
Fragmented data like drawings, BIM models, specifications, and O&M manuals are unified into an intelligent knowledge graph, enabling complex cross-referencing like validating a drawing detail against a specific line in a thousand-page specification.
A single stack that turns private and public data into cited, validated answers, integrated with the systems your teams already use.
TwinKnowledge unifies scattered project documentation into a connected data environment, validating requirements across the plan, design, build, and operate phases.
How TwinKnowledge compares to incumbents and off-the-shelf AI point solutions.
Each agent automates a manual, high-risk task — with measurable impact on rework, RFIs, and review coverage.
Bring a spec, an RFP, or a set of drawings. We'll show the platform validating 100% of your requirements in a 30-minute walkthrough.