Launch HN: InspectMind (YC W24) – AI agent for reviewing construction drawings

Symbol variation is a huge challenge across firms.

Our approach mixes OCR, vector geometry, and learned embeddings so the model can recognize a symbol plus its surrounding annotations (e.g., “6-15R,” “DIM,” “GFCI”).

When symbols differ by drafter, the system leans heavily on the textual/graph context so it still resolves meaning accurately. We’re actively expanding our electrical symbol library and would love sample sets from your workflow.

We parse symbols using a mix of vector geometry, OCR, and learned detection for common architectural/MEP symbols. Cross-discipline checks are a big focus as we already flag mismatches between architectural, structural, and MEP sheets, and we’re expanding into deeper electrical/mechanical spec alignment next. Would love to hear which symbols matter most in your workflow so we can improve coverage.

Totally! Division 10 and specialty trades are often the first to see coordination issues show up in the field. We’re trying to bring that same early-warning benefit across the entire drawing set so errors never make it to construction. Would love to run a real project from your family’s world if they’re open to it!

We see that a lot — specs that are clearly boilerplate or outdated relative to the drawings. Our goal isn’t to force a change, but to surface where the specs and drawings diverge so the designer can quickly decide what’s intentional vs what’s baggage. “Flag + context for fast human judgment” is the philosophy.

Great point! Owner’s reps and commissioning teams are becoming one of the fastest-growing user groups for us. At SD/DD we can surface coordination risks early, highlight spec–drawing mismatches, and give owners a clearer picture of design completeness before things get locked in. If you’re open to it, we’d love to run a sample SD/DD set from your world and see what’s most useful.

BIM just shuffles the problem around. There are firms that do "one source of truth" BIM models but the real issue is conflicts and workflow buy in.

How do you get architect to agree with engineer with lighting designer with lighting contractor when they all have different non overlapping deadlines, work periods, knowledge and scope?

edit: if you don't work in the industry, BIM helps for "these two things are in the same spot", but not much for code unless it's about clearance or some spatial based calculation

BIM definitely helps, but most projects still rely heavily on 2D PDFs for coordination and permitting especially in the US. Even when BIM exists, drawings often lag behind the model and changes don’t stay perfectly synced. We see AI plan checking as a bridge that helps teams catch what falls through the cracks in today’s workflows. And BIM only catches certain issues not building codes etc.

We infer the applicable codes from the project metadata + the drawings themselves.

The location + occupancy/use type tells us the governing code families (e.g., IBC/IRC, ADA, NFPA, local amendments), and then we parse the sheets for callouts, annotations, assemblies, and spec sections to map them to the relevant provisions.

So the system knows when to check (e.g., plumbing fixture clearances) because of the objects it detects in the drawings, and it knows what code to check based on jurisdiction + building type + what’s being shown in that detail.

The model still flags with human-review intent so designer judgment stays in the loop.

We price per-project based on size/complexity not % of construction cost, so there’s no conflict of interest around bigger budgets. Today our main users are architects/engineers and GC pre-con teams, but subs who catch coordination issues early also get a ton of value.

Hallucinations still happen occasionally, but we bias heavily toward high-confidence findings so noise stays low. On typical projects we surface a few hundred coordination issues that are real, observable conflicts across sheets rather than speculative checks. We’re actively improving precision by learning from every false positive customers flag. We show you the drawings, specs, etc. so you can verify it yourself not just trust the AI.

We do extensive preprocessing to ensure AI receives accurate context, data, and documents for review, and we’re continuously refining this, so accuracy keeps improving every day. Right now the accuracy isn't super stable yet across projects, but we've had findings with > 90% accuracy results

Great question. We currently focus primarily on coordination, dimension conflicts, missing details, and clear code-triggered checks that don’t require sealed structural judgment. For structural code references (e.g., ASCE-7), we infer applicable sections and surface potential issues for a licensed engineer to review. We don’t replace engineering judgment or sealed design accountability.

Great questions. We’re working on a more formal public benchmark and will share results as our dataset grows. Today, we typically catch coordination issues like conflicting dimensions, missing callouts, building code and clearance violations that humans often miss in large sheet sets. Behind the scenes it’s a multimodal workflow: OCR + geometry parsing + cross-sheet callout graph + constraint checks vs. code/spec requirements.

Totally fair callout and appreciate the feedback. We’re already testing alternative hero layouts focused purely on real customer results and example issues caught. Our goal is to win trust by demonstrating usefulness/results, not who invested in us.