This is exactly the hard part symbols aren’t enough when each drafter overloads them, so we lean on the annotation + schedule context (fixture tags, notes like “DIM,” control zones, panel/ckt callouts, and control intents) to disambiguate.
Yep, exactly, when layer data survives the PDF export, it’s a huge help. We use it as a weak signal for clustering and object grouping, but never rely on it fully since it’s often inconsistent or stripped. When it’s there, accuracy and speed both improve noticeably.
Awesome — thank you! Easiest is email: aakash@inspectmind.ai or they can book a demo call on our website or upload plans directly on website. If they can share (1) a representative drawing/spec set (or a small sample) + (2) what trade/Division they focus on, we can run it and send back findings with sheet refs.
We do best on CAD-originated PDFs where we can use the underlying vector data, but we can run on scanned/hand-drawn sets too. In that case we rely more on image-based detection + OCR (no clean vector layer), so accuracy depends on scan quality, contrast, and how consistent the annotations are. We’ve had success on some older/detail-heavy scans, but it’s definitely a harder mode. If you have a representative “old-school” set, we’d love to run it and show you where it works well vs where it struggles.
Great question. By “vector geometry” we mean we’re using the underlying CAD-style vector data embedded in many PDFs (lines, arcs, polylines, hatches, etc.), not just raster images. We reconstruct objects and regions from that geometry, then fuse it with OCR (for annotations, tags, labels) and a detection model that operates on rendered tiles. The detector + OCR tells us what something is; the vector layer tells us exactly where and how it’s shaped so we can run dimension/clearance and cross-sheet checks reliably.
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.
Happy to swap notes. If you send a representative lighting plan set, we can run it and share how the detector clusters, resolves, and cross-references symbols across sheets. Always excited to compare approaches with teams solving adjacent problems.
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.
Yes today users simply gather the sheets for whatever phase they want reviewed (DD, 80% CDs, 100% CDs, etc.), ZIP them or upload PDFs directly, and the system handles the rest. It auto-detects disciplines, reconstructs callout graphs, and runs checks across the full set. We're also adding integrations with ACC/Procore/Revit so sheet aggregation becomes automatic.
Today the workflow is simple: users just drag-and-drop the full drawing/spec set (ZIP or PDFs) for whatever phase they want reviewed. The system automatically splits sheets by discipline, reconstructs callout relationships, and runs the checks. We’ll be adding integrations with ACC/Procore/Revit exports so this becomes even more automated.
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.
Yes one of the biggest values of our system is reducing “noise.” Instead of surfacing 2,000 micro-clashes, we cluster findings into higher-order issues (e.g., “all conflicts caused by this duct run” or “all lighting mismatches tied to this dimming spec”). We’re not a BIM viewer yet, but we do map issues back to sheet locations, callouts, and detail references so teams can navigate directly to the real source of the problem.
We store files securely on AWS with strict access controls, encryption in transit and at rest, and zero sharing outside the file owner’s account. Only our engineers can access a project for debugging and only if the customer explicitly allows it. We can also offer an enterprise option with private cloud/VPC deployment for firms that require even tighter controls. Users can delete all files permanently at any time.
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.
We’d love that — perfect use case. Send a recent set and we’ll run a discounted comparison so you can see what we catch vs. what surfaced during construction. If helpful, we can hop on a quick call to walk through results and collect feedback. Email me aakash@inspectmind.ai
Most teams run us late DD through CD anywhere the set is stable enough that coordination issues matter. Subs especially like running it pre-bid at ~80–100% CDs so they don’t inherit coordination risk. Earlier checks also help designers tighten the set before hand-offs, so value shows up at multiple stages. Eventually the goal is to be continuous QA tool including during construction by pulling in field data too and comparing to drawings and specs. Like drawings showed X size and field photos show Y size.
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.
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.
Yep, the model identifies objects/conditions on sheets (fixtures, stairs, rated walls, landings, etc.) and triggers the relevant checks automatically. It can run thousands of checks per project, but we only surface high-confidence findings where the combination of geometry + annotations + code context points to a real risk. Humans stay in the loop to confirm what matters.
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.
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.
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.
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!
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.
I'm excited to share with you a new nonprofit initiative I'm working on called Project Karma. The goal of Project Karma is to inspire and enable startup founders and business owners to donate some of their equity to charity.
As a founder, I know firsthand how much of our net worth is tied up in our company's stock. I also know that many of us are looking for ways to make a positive impact on the world and give back. That's why I believe that donating a portion of our equity can be a powerful way to make a difference and create a lasting legacy of giving.
Through Project Karma, I hope to create a movement of founders and business owners who are committed to using their company stock as a tool to make a positive impact on the world. Whether it's donating a portion of their equity to charitable causes through a charitable vehicle like a charitable remainder trust (CRUT), or working with their company to develop a charitable giving program, I believe that together we can multiply the impact of charitable giving and create a lasting legacy of giving back.
If you're a founder or business owner who is interested in learning more about Project Karma and how you can get involved, I'd love to hear from you. Together, I believe we can make a real difference in the world and inspire others to do the same.