发布 HN:InspectMind(YC W24)– 用于审查建筑图纸的人工智能助手
嗨,HN,我们是 InspectMind 的 Aakash 和 Shuangling(<a href="https://www.inspectmind.ai">https://www.inspectmind.ai</a>),我们是一款 AI “计划检查器”,可以发现建筑图纸、细节和规范中的问题。
建筑图纸常常存在许多错误:尺寸冲突、协调缺口、材料不匹配、缺失细节等等。这些错误在施工过程中会导致延误和数十万美元的返工。InspectMind 可以在几分钟内审核一个建筑项目的完整图纸集。它交叉检查建筑、工程和规范,以在施工开始前捕捉可能导致返工的问题。
这里有一个包含一些示例的视频:<a href="https://www.youtube.com/watch?v=Mvn1FyHRlLQ" rel="nofollow">https://www.youtube.com/watch?v=Mvn1FyHRlLQ</a>。
在此之前,我(Aakash)创建了一家工程公司,参与了大约 10,000 幢建筑的项目。我们一直感到沮丧的一件事是:许多设计协调问题在施工开始之前并不会显现出来。到那时,错误的成本可能高达 10 到 100 倍,所有人都在忙着修复本可以更早发现的问题。
我们尝试了各种方法,包括检查清单、叠加审查、同行检查,但在 500 到 2000 页 PDF 文档中滚动并记住每个细节与其他页面的连接是一项脆弱的过程。城市审查员和总承包商的预施工团队也在努力捕捉问题,但仍然会有问题漏网。
我们想:如果模型可以解析代码并生成可运行的软件,也许它们也可以帮助我们在纸面上推理建筑环境。因此,我们构建了我们希望拥有的工具!
您只需上传图纸和规范(PDF 格式)。系统会将它们拆分为不同学科和细节层次,解析几何和文本,并寻找不一致之处:- 各页之间不一致的尺寸;- 被机械/建筑元素阻挡的间隙;- 缺失或不匹配的消防/安全细节;- 从未出现在图纸中的规范要求;- 引用不存在细节的标注。
输出结果是一个潜在问题的列表,包含页面引用和位置,供人工审核。我们并不期望自动化取代设计判断,只是希望帮助建筑、土木工程和电气专业人士不遗漏明显的问题。当前的 AI 在处理明显问题方面表现良好,并且可以处理超出人类准确处理能力的数据量,因此这是一个很好的应用场景。
建筑图纸并没有标准化,每个公司对事物的命名方式也不同。早期的“自动检查”工具在很大程度上依赖于客户手动编写的规则,当命名约定发生变化时就会失效。相反,我们使用多模态模型进行 OCR + 向量几何、跨整个图纸集的标注图、基于约束的空间检查和增强检索的代码解释。再也不需要硬编码规则了!
我们目前正在处理住宅、商业和工业项目。延迟时间从几分钟到几个小时不等,具体取决于图纸数量。无需任何入门培训,只需上传 PDF 文件。仍然存在许多边缘案例(PDF 提取异常、不一致的图层、行业术语),因此我们从失败中学到了很多,或许比成功学到的还要多。但这项技术已经能够提供以前工具无法实现的结果。
定价采用按需付费的方式:在您上传项目图纸后,我们会立即提供每个项目的在线报价。由于一个项目可能是家庭改造,而另一个可能是高层建筑,因此很难采用常规的 SaaS 定价。我们也欢迎对此的反馈,我们仍在摸索中。
如果您作为建筑师、工程师、机电工程师、总承包商预施工人员、房地产开发商或图纸审查员与图纸打交道,我们非常希望有机会运行一组样本,并听取您的反馈,了解哪些地方出现问题,哪些功能有用,以及哪些功能缺失!
我们将全天候在这里讨论几何解析、聚类失败、代码推理尝试或关于施工过程中如何出错的真实故事。感谢您的阅读!我们乐意回答任何问题,并期待您的评论!
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Hi HN, we're Aakash and Shuangling of InspectMind (<a href="https://www.inspectmind.ai/">https://www.inspectmind.ai/</a>), an AI “plan checker” that finds issues in construction drawings, details, and specs.<p>Construction drawings quietly go out with lots of errors: dimension conflicts, co-ordination gaps, material mismatches, missing details and more. These errors turn into delays and hundreds of thousands of dollars of rework during construction. InspectMind reviews the full drawing set of a construction project in minutes. It cross-checks architecture, engineering, and specifications to catch issues that cause rework before building begins.<p>Here’s a video with some examples: <a href="https://www.youtube.com/watch?v=Mvn1FyHRlLQ" rel="nofollow">https://www.youtube.com/watch?v=Mvn1FyHRlLQ</a>.<p>Before this, I (Aakash) built an engineering firm that worked on ~10,000 buildings across the US. One thing that always frustrated us: a lot of design coordination issues don’t show up until construction starts. By then, the cost of a mistake can be 10–100x higher, and everyone is scrambling to fix problems that could have been caught earlier.<p>We tried everything including checklists, overlay reviews, peer checks but scrolling through 500–2000 PDF sheets and remembering how every detail connects to every other sheet is a brittle process. City reviewers and GC pre-con teams try to catch issues too, yet they still sneak through.<p>We thought: if models can parse code and generate working software, maybe they can also help reason about the built environment on paper. So we built something we wished we had!<p>You upload drawings and specs (PDFs). The system breaks them into disciplines and detail hierarchies, parses geometry and text, and looks for inconsistencies: - Dimensions that don’t reconcile across sheets; - Clearances blocked by mechanical/architectural elements; - Fire/safety details missing or mismatched; - Spec requirements that never made it into drawings; - Callouts referencing details that don’t exist.<p>The output is a list of potential issues with sheet refs and locations for a human to review. We don’t expect automation to replace design judgment, just to help ACE professionals not miss the obvious stuff. Current AIs are good at obvious stuff, plus can process data at quantities way beyond what humans can accurately do, so this is a good application for them.<p>Construction drawings aren't standardized and every firm names things differently. Earlier “automated checking” tools relied heavily on manually-written rules per customer, and break when naming conventions change. Instead, we’re using multimodal models for OCR + vector geometry, callout graphs across the entire set, constraint-based spatial checks, and retrieval-augmented code interpretation. No more hard-coded rules!<p>We’re processing residential, commercial, and industrial projects today. Latency ranges from minutes to a few hours depending on sheet count. There’s no onboarding required, simply upload PDFs. There are still lots of edge cases (PDF extraction weirdness, inconsistent layering, industry jargon), so we’re learning a lot from failures, maybe more than successes. But the tech is already delivering results that couldn’t be done with previous tools.<p>Pricing is pay-as-you-go: we give an instant online quote per project after you upload the project drawings. It’s hard to do regular SaaS pricing since one project may be a home remodel and another may be a highrise. We’re open to feedback on that too, we’re still figuring it out.<p>If you work with drawings as an architect, engineer, MEP, GC preconstruction, real estate developer, plan reviewer we’d love a chance to run a sample set and hear what breaks, what’s useful, and what’s missing!<p>We’ll be here all day to go into technical details about geometry parsing, clustering failures, code reasoning attempts or real-world construction stories about how things go wrong. Thanks for reading! We’re happy to answer anything and look forward to your comments!