Hazumi News

Hi HN, we're Aakash and Shuangling of InspectMind (https://www.inspectmind.ai/), an AI “plan checker” that finds issues in construction drawings, details, and specs.

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.

Here’s a video with some examples: https://www.youtube.com/watch?v=Mvn1FyHRlLQ.

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.

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.

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!

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.

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.

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!

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.

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.

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!

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!

I'm the founder of AhaSend. We've built a transactional email service specifically for developers, with a Pay-As-You-Go pricing model ($0.50 per 1,000 emails) and up to 70% volume discount for platform partners.

While there are many ESPs out there, we focused heavily on the Developer Experience to solve the pain of building and testing email flows:

* The CLI: Heavily inspired by the Stripe CLI, we built a tool that lets you develop and debug locally. You can listen to webhooks, trigger events, manage your domains, and send / list / debug messages directly from your terminal.

* True Sandbox: We offer a full-lifecycle sandbox where emails go through the entire pipeline (processing, signing, etc.) without ever leaving our servers. Rhis also triggers all the webhook events (delivered, clicked, etc.) so you can test your app's logic without spamming real inboxes.

* Granular Security: We support fine-grained API scopes (e.g., reporting-only keys, or keys scoped strictly to sending from specific domains, etc), so you don't have to store "root" API keys in your application.

I'd love to hear your thoughts!