Design review is where mechanical engineering teams lose the most time. It is also where the most expensive mistakes slip through. A significant portion of the design cycle is spent on manual, repetitive checks, yet even after in-depth reviews, errors still reach production and require rework. And the knowledge needed to catch the hard problems is siloed among a few senior engineers, which means everything depends on them.
AI should be the obvious answer here, and most engineering teams have already tried it. Upload a drawing to a general-purpose LLM and ask it to verify GD&T correctness or confirm that a revision matches the released model, and the results are underwhelming. There is a structural reason why.
The Missing Context
Think about what a thorough design review actually requires. It is not one body of knowledge but three.
The first is industry-wide standards, the published rules every drawing must meet, like ISO, ASME, and GD&T conventions. These are external, universal, and not optional. The second is company-specific SOPs, your drafting standards, design checkers, and internal rules that live nowhere a general model can reach. The third is tribal knowledge, the design intent and history buried in your PDM, in Slack threads, and in email, siloed among a handful of senior engineers.
A general LLM has partial access to the first, zero access to the second, and zero access to the third. Asked to confirm that a drawing revision matches the approved ECN in your PDM, it will tell you directly that it cannot, because it has no access to your PDM. Asked to catch every meaningful change between two revisions, it falls back on pixel comparison, which detects visual change but cannot classify whether a change affects form, fit, or function.
This is why trust in AI for engineering runs on a spectrum. Most engineers are comfortable letting a general model summarize an SOP or flag an annotation typo, because the stakes are low. But the tasks that carry real business value, tolerance stack-ups, GD&T verification, redline reports, are exactly the ones where the risk of the AI being wrong is highest. Without the full context, deep AI review of critical work is not trustworthy.
bananaz was built to close that gap. It extracts version history, metadata, and communication from your native CAD and PDM, connecting all three pillars of context so the AI works with the same information a senior engineer would.
The Four Pillars of a Supercharged Design Review
With that context in place, bananaz transforms the design review itself across four capabilities.
Change Analysis
bananaz automatically compares revisions and highlights every 2D and 3D change side by side, so you can review impacts faster and approve ECOs with confidence. Because it reads the native geometry rather than pixels, it understands what actually changed, not just where ink moved on the sheet.
Automated Rule Enforcement
Your checklists and standards become automated checks that scan every drawing for violations before it reaches production. Industry standards, company SOPs, and even individual engineer preferences all become active rules instead of documents in a folder.
Artifact Generation
Change reports, redlines, FAI exports, and ballooned PDFs are generated automatically from the validated design, in minutes instead of days. The documentation burden that used to consume entire afternoons becomes a byproduct of the review itself.
Collaboration
One source of truth for the whole workflow. Comments and markups stay tied to the right revision, so feedback never loses context and no one approves against a stale version.
The Design Agent: Context You Can Talk To
The four pillars run as structured workflows, but the bananaz Design Agent brings the full context to open-ended questions.
Select your CAD files and ask in plain language, "What DFM issues should I address before manufacturing?" or "Perform a tolerance analysis and identify unnecessarily tight tolerances." Because the agent is built on mechanical engineering logic, it parses and understands native metadata across your 2D drawings, 3D models, and BOMs rather than working from a text extract. Every version and every change is synced to your PDM, and the agent ingests your SOPs, standards, and tribal knowledge, then applies them automatically, getting smarter with every design your team runs through it.
This is the difference between AI that advises from outside your workflow and an agentic layer that lives inside it.
Why It Matters
Design review has always forced a trade-off between speed and rigor. Move fast and things slip through, or review deeply and become the bottleneck. With the routine checks running automatically and the full engineering context available for the judgment calls that matter, that trade-off goes away.
That is what it means to supercharge design review.
Ready to see how bananaz supercharges your design review? Book a demo with one of our AI consultants.
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