Paqly — Intelligent 3D Packing

Paqly

Pack brilliantly.

Turn a packing list into a validated 3D suitcase plan. ChatGPT can build and edit the list through WebMCP; you keep control of every size assumption, tick items off as you pack, then replay the exact order.

Validated fit — floor used Fold + squeeze soft physics Checking… ChatGPT site tools Global search

3D packing planTight physical packing · gravity settled · zero deliberate spacing.

Drag to rotate · wheel/pinch to zoom · tap an item

Physics ready · 0 overlaps

ItemsAdd one item at a time and edit its exact dimensions inline. Bulk lists are added through ChatGPT/WebMCP.

0 items

Add manuallyOne physical item at a time for accurate sizing. To add a whole list, use ChatGPT through this page’s WebMCP tools.

Nothing packed yet—

Item assumptionspacked size

Width cm Height cm Depth cm

Access Material

Softness Max compression % Mass kg

ReplayPack in the exact solved order.

12.0 s

Open caseFinal pack

Packing orderLower layers are completed before anything above them.

8 items

LuggageChoose a real packing strategy, then tune its usable interior.

Medium suitcase

Internal width Internal length Internal depth

Reference exterior47.0 × 66.1 × 28.0 cm

Usable interior45.2 × 59.8 × 26.0 cm

Corner radius—

Trolley mechanismUnder liner

Hard-shell luggage uses a strict Primary-first 50/50 clamshell. Soft luggage uses one deformable cavity with its own placement priorities.

Precision solver. Primary is packed and compacted first; only items that remain after the best Primary solution are sent to Secondary, then evaluates rotations, garment fold states, compression, support and contact gaps. Every submitted item stays visible in the 3D scene. If the validated packing solution is over capacity, overflow items remain visibly staged with the open suitcase and the closure attempt must fail rather than silently omitting them.

Model + material intelligencePremade organic GLB meshes for common items; physics envelopes remain authoritative.

Color-locked GLB

Every core item loads from a separately generated GLB library with runtime-locked PBR albedo colors so materials cannot wash out to white. Garment meshes switch with the solved fold state; labels remain visible and move with their assigned layer.

Embedded generation policy

Real-world scale · premade organic meshes · ≤40% soft-goods squeeze · adaptive fold/gap-fill states · AI layer strategy · deterministic geometry validation · closure physics audit · never claim fit on failure.

Research basis: Khronos glTF guidance · Bullet/Ammo soft bodies · deformable 3D bin packing · real luggage handle geometry.

ChatGPT · WebMCPChatGPT can batch-add full lists and operate this live packing plan; no model API key is embedded.

Detecting

Registered tools0

Last agent actionNone

Checking this browser…

When opened in a WebMCP-capable ChatGPT browser, ChatGPT reads the physics guide, calculates per-item fold/compression/layer strategy, submits that strategy to the page, then the deterministic solver validates and renders it.

Performance mode: render-on-change · colored GLBs cached · physics only during replay/closure

Fit verificationNo floating, no overlaps, no out-of-case geometry.

READYAdd an item to begin.

Bottom layer coverage—

Raw envelope volume—

Highest stack—

Rendered mesh bounds—

Inside internal cavity—

AABB collisions—

Static support—

Insertion paths—

Outer-view / cavity audit—

Physics modelHybrid

External fit auditChecks rounded-cavity containment, handle channels, lid clearance, mesh envelopes and all four inspection views.

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Fit Check exposes the authoritative collision/deformation envelopes. Outer Audit inspects the final pack from isometric, top, front and side viewpoints and refuses a fit if the realistic cavity is violated.