发布 HN:Opusense(YC X25)– 现场建筑检查员的 AI 助手
嗨,HN,我们是Roya和Michael,Opusense AI的联合创始人(<a href="https://www.opusense.com">https://www.opusense.com</a>),我们的工具旨在帮助工程师和顾问自动生成建筑工地报告,支持通过文字或语音笔记以及照片生成报告。
这里有一个视频:<a href="https://www.youtube.com/watch?v=u3Pi1iih1_Y" rel="nofollow">https://www.youtube.com/watch?v=u3Pi1iih1_Y</a>。
在此之前,我(Roya)曾在华为从事人机交互工作,之前还在土木工程公司担任建筑工地检查员。我拥有土木工程博士学位,基于我的经验,报告撰写无疑是工作中最繁琐和令人厌倦的部分。
你会整天在工地上走来走去,做一些简短的笔记(有时,你可能会依赖记忆),并拍摄照片,然后再去三个工地,最后才回到办公室,努力回忆你想写的所有内容。有时你会凭记忆填补空白,或者故意保持模糊。报告必须保持一致、符合品牌要求,并由高级工程师审核。这在团队中耗费了大量时间。
撰写报告是工作中最糟糕的部分,因此我们创建了Opusense来解决这个问题。在现场,用户可以输入或口述简短的笔记(例如:“混凝土板东端钢筋暴露”),该工具会将其转换为完整的句子、段落、表格或符合公司格式的报告模板中的照片说明。你可以离线工作,在线时会自动同步。
大多数检查和报告工具都是为清单式工作流程设计的(这对于家庭检查或缺陷清单很有用),但土木、结构、环境或岩土工程师通常需要自由形式的笔记,而不是单选按钮。
这特别适合大型语言模型(LLMs),因为工程现场报告存在于一个受限且传统的领域:相似的语言、重复的结构以及跨公司和项目高度标准化的内容。这里有很多冗余和繁琐的工作,比如总结相同的现场条件、格式化重复的数据、将现场笔记转换为精炼的段落,所有这些都可以通过适当的提示和保护措施来很好地处理。我们并不是在生成任意的散文,而是在将结构化的输入(笔记、图像、表单)转换为结构化的输出,使用公司定义的模板和必填字段,从而最大限度地降低幻觉的风险。当事实至关重要时(例如测试结果或测量值),我们会将其与用户的输入保持一致,模型不会凭空捏造数据,因为没有可供其创造的内容。这使得LLMs在这种情况下不仅仅是新奇的工具,而是真正适合这项工作的最佳工具。
在技术层面上,我们结合了经过提示工程优化的LLMs和公司特定的格式规则,以获得不仅听起来不错,而且看起来也正确的输出。我们最近增加了翻译功能,并根据现场反馈快速迭代。我们的收费是按席位计算,目前已在中型公司部署,并与一些每周需要提交数千份报告的跨国工程公司进行试点。我们也开始看到来自进行内部质量保证报告的施工经理和开发商的兴趣。
我们目前还没有自助试用产品的方式,因为我们的业务模式要求模板根据公司进行定制。但可以在这里查看演示:<a href="https://www.youtube.com/watch?v=u3Pi1iih1_Y" rel="nofollow">https://www.youtube.com/watch?v=u3Pi1iih1_Y</a>,如果你想自己探索用户界面,这里有一个样本账户供你登录:
<pre><code> 登录: hndemo@opusense.com
密码: OpusenseHacker2025
</code></pre>
该应用程序可在Apple和Google Play商店下载。当生成样本报告时,你可以使用相同的登录凭据登录网页界面,通过我们的网站(www.opusense.com)在线查看报告。
我们很想听听其他人对现场工作、报告或类似工作流程(工程、建筑等)工具的看法。如果你在这个领域有过开发经验,或者对如何改进它有想法,我们非常欢迎你的分享!
查看原文
Hi HN, we're Roya and Michael, co-founders of Opusense AI (<a href="https://www.opusense.com/">https://www.opusense.com/</a>), a tool to help engineers and consultants automatically generate construction site reports from typed or voice notes, plus photos.<p>Here’s a video: <a href="https://www.youtube.com/watch?v=u3Pi1iih1_Y" rel="nofollow">https://www.youtube.com/watch?v=u3Pi1iih1_Y</a>.<p>Before this, I (Roya) worked in human-machine interaction at Huawei, and before that as a construction site inspector for civil engineering firms. I have a PhD in Civil Engineering, and in my experience reporting was by far the most tedious and mind-numbing part of the job.<p>You’d walk around a site all day taking short notes (maybe, often you'd rely on memory) and snapping photos, then go to three more sites before finally making it back to the office and try to remember everything you wanted to write. Sometimes you’d fill in gaps from memory or you’d keep it purposefully vague. Reports had to be consistent, branded, and checked by senior engineers. It was a huge time sink across the team.<p>Writing reports was the worst part of the job, so we built Opusense to get rid of it. On-site, users type or dictate short notes (e.g. “rebar exposed east end of slab”), and the tool turns them into full sentences, paragraphs, tables, or photo captions in a report template that matches the firm’s format. You can work offline, and it syncs automatically when back online.<p>Most inspection and reporting tools are built for checklist-style workflows (which is great for home inspections or punch lists), but civil, structural, environmental, or geotechnical engineers usually need freeform notes, not radio buttons.<p>This is a particularly good fit for LLMs because engineering field reports live in a constrained, conventional domain: similar language, repeated structures, and highly standardized content across firms and projects. There’s a lot of redundancy and grunt work, summarizing the same site conditions, formatting repetitive data, translating field notes into polished paragraphs, all of which LLMs handle well with the right prompting and guardrails. We’re not generating arbitrary prose; we’re transforming structured inputs (notes, images, forms) into structured outputs, with firm-defined templates and required fields that minimize the risk of hallucination. When facts matter (e.g. test results or measurements), we keep them grounded in the user’s input, the model doesn’t invent data because there’s nothing for it to invent. This makes it one of those cases where LLMs aren’t just a novelty, they're genuinely the best tool for the job.<p>Under the hood, we use a combination of prompt-engineered LLMs and firm-specific formatting rules to get outputs that don’t just sound good, but also look right. We’ve recently added translation features, and we’re iterating quickly based on field feedback. We charge per seat and are deployed at mid size firms, and trialing with some multinational engineering firms who have thousands of reports to file each week. We're also starting to see interest from construction managers and developers who do their own internal QA reporting.<p>We don't have a self-serve way to try out the product yet, because the way our business works requires templates to be customized by company. But there’s a demo at <a href="https://www.youtube.com/watch?v=u3Pi1iih1_Y" rel="nofollow">https://www.youtube.com/watch?v=u3Pi1iih1_Y</a>, and if you want to poke around the UI yourself, here’s a sample account to log in with:<p><pre><code> login: hndemo@opusense.com
password: OpusenseHacker2025
</code></pre>
The app is available for download on the Apple and Google Play stores. When sample reports are generated, you can log into the web interface to also view them online through our website (www.opusense.com) with the same login credentials.<p>We’d love to hear how others are thinking about tools for field work, reporting, or similar workflows (engineering, architectural, etc.). If you’ve built in this space, or have thoughts on how to improve it, we’re all ears!