Launch HN: InspectMind (YC W24) – 审查施工图纸的 AI 智能体

8作者: aakashprasad9121 天前
大家好,我们是 InspectMind(<a href="https://www.inspectmind.ai/">https://www.inspectmind.ai/</a>)的 Aakash 和 Shuangling,InspectMind 是一款 AI “图纸检查器”,用于查找施工图纸、细节和规范中的问题。 施工图纸常常会悄无声息地出现很多错误:尺寸冲突、协调间隙、材料不匹配、缺少细节等等。这些错误会导致施工期间的延误和数十万美元的返工。InspectMind 可以在几分钟内审查建筑项目的全套图纸。它交叉检查建筑、工程和规范,以发现导致返工的问题,从而在施工开始前解决问题。 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,该公司在美国完成了大约 10,000 栋建筑。我们一直感到沮丧的一件事是:很多设计协调问题直到施工开始后才会显现出来。到那时,错误的成本可能会高出 10-100 倍,每个人都在争先恐后地解决本可以更早发现的问题。 我们尝试了一切,包括清单、叠加审查、同行检查,但浏览 500-2000 张 PDF 文件并记住每个细节如何与其他每个文件相关联是一个脆弱的过程。城市审查员和总承包商(GC)的预施工团队也试图发现问题,但它们仍然会溜过去。 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推理纸上的建筑环境。所以我们构建了我们希望拥有的东西! 您上传图纸和规范(PDF)。系统将它们分解为不同的专业和细节层次结构,解析几何图形和文本,并查找不一致之处: * 跨图纸不一致的尺寸; * 被机械/建筑元素阻挡的间隙; * 缺失或不匹配的防火/安全细节; * 从未纳入图纸的规范要求; * 引用不存在的细节的标注。 输出结果是潜在问题列表,其中包含供人工审查的图纸参考和位置。我们不希望自动化取代设计判断,只是帮助 ACE 专业人士不会遗漏明显的东西。当前的 AI 擅长处理明显的事情,并且可以处理的数据量远远超过人类可以准确处理的,所以这是一个很好的应用。 施工图纸没有标准化,每个公司对事物的命名方式都不同。早期的“自动化检查”工具严重依赖于为每个客户手动编写的规则,并且在命名约定更改时会失效。相反,我们正在使用多模态模型进行 OCR + 矢量几何、跨整个集合的标注图、基于约束的空间检查和检索增强的代码解释。不再需要硬编码规则! 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。无需入职培训,只需上传 PDF 文件即可。仍然存在很多边缘情况(PDF 提取的怪异之处、不一致的图层、行业术语),因此我们从失败中学习了很多,可能比从成功中学习的更多。但这项技术已经交付了以前的工具无法实现的结果。 定价是按使用量付费:您上传项目图纸后,我们会立即在线提供每个项目的报价。由于一个项目可能是房屋改造,而另一个项目可能是高层建筑,因此很难进行常规的 SaaS 定价。我们也欢迎对此提供反馈,我们仍在努力解决这个问题。 如果您作为建筑师、工程师、MEP、GC 预施工、房地产开发商、图纸审查员处理图纸,我们很乐意有机会运行一个样本集,并听取哪些问题会发生、哪些有用以及缺少什么! 我们今天一整天都会在这里,深入探讨几何解析、聚类失败、代码推理尝试或关于事情如何出错的真实建筑故事的技术细节。感谢您的阅读!我们很乐意回答任何问题,并期待您的评论!
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Hi HN, we&#x27;re Aakash and Shuangling of InspectMind (<a href="https:&#x2F;&#x2F;www.inspectmind.ai&#x2F;">https:&#x2F;&#x2F;www.inspectmind.ai&#x2F;</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:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=Mvn1FyHRlLQ" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;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&#x2F;architectural elements; - Fire&#x2F;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&#x27;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!