Digs transforms floor plans into AI-readable construction intelligence
Discover how Digs scaled high-quality blueprint annotation for AI-powered construction workflows.
September 1, 2026 | United States
요약 보고
As Digs expanded its AI-powered collaboration platform for home builders and homeowners, it needed a reliable way to turn complex floor plans and blueprint images into structured data its AI systems could use.
By partnering with Uber AI Solutions (UAIS), Digs established a flexible annotation operation that adapted as blueprint-labeling requirements evolved. The engagement maintained high annotation quality, expanded support across more detailed construction elements, improved operational visibility, and created a repeatable foundation for continued AI product development.
핵심 결과
99.5%+
주석 품질 유지
1,000+
room-name 식별을 위한 추가 작업이 지정됨
Building AI-powered construction workflows depends on sophisticated data annotation
Digs is building an AI-powered platform that helps home builders and homeowners organize construction documents, collaborate on plans, automate handoffs, and maintain a digital record of a home. For those workflows to work reliably, the platform first needed to understand residential floor plans and blueprints, which vary significantly in layout, symbols, measurements, orientation, and architectural detail.
As Digs introduced new AI capabilities, its data annotation needs became more sophisticated. Work that began with room segmentation expanded into room naming, measurements, wall interpretation, and other blueprint elements. The challenge was not simply labeling more images. Digs needed a data annotation operation capable of adapting as the product evolved while also preserving downstream consistency builders and homeowners depended on.
Finding an annotation partner that could adapt as the product evolved
Digs partnered with Uber AI Solutions (UAIS). The UAIS team quickly aligned with Digs’ existing technical environment and evolving workflows, rather than forcing them into a rigid operational structure. UAIS assembled a dedicated annotation team and worked within Digs’ established review process. As requirements changed, workflows were adjusted incrementally. Adjusted workflows supported rotated layouts, updated segmentation rules, room naming, measurements, and increasingly specialized blueprint elements without rebuilding the operation from scratch.
The collaboration also addressed an operational visibility gap. Because review and ingestion processes existed across separate systems, blocked work and emerging issues could be difficult to identify quickly. UAIS introduced dashboard reporting and automated Slack updates that surfaced queue status, active operations, and blocked items, giving both teams greater day-to-day visibility while reducing manual coordination.
Sustaining 99.5%+ blueprint annotation accuracy as AI requirements expanded
The engagement established a stable annotation operation capable of supporting changing blueprint requirements while sustaining greater than 99.5% quality across core workflows.
Reporting visibility made it easier to identify workflow issues and coordinate production, while the flexible operating model allowed Digs to introduce new annotation tasks as its AI product evolved. For example, the new workflow was able to easily absorb an add-on project of over 1,000 tasks for room name identification.
With operational experience managing complex annotation programs and adapting to changing requirements, UAIS allowed Digs to expand its use of structured construction data without creating a new internal process every time annotation requirements shifted.
A scalable foundation for AI-powered construction workflows
AI systems for residential construction depend on accurate interpretation of highly variable visual documents. Building those capabilities requires annotation operations that can evolve alongside the product itself.
By partnering with Uber AI Solutions, Digs established a reliable blueprint annotation workflow capable of supporting changing requirements, maintaining quality, and scaling operationally without introducing unnecessary complexity. Now, Digs is building AI-powered workflows for the modern era of home construction and ownership.
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