Module 01: Generative Architecture
The Fabric
Physics Engine.
We trained a neural network on 140,000 hours of textile draping physics. It doesn't generate images. It generates executable DXF patterns.
TERMINAL_ACCESS // NODE_1
Silhouette Generation
Outputs grade-ruled tech packs, not mood boards. Geometry that survives the factory floor.
Material Simulation
Predictive modeling for shrinkage, drape, and tensile strength before the first physical sample is cut.
Real-time Costing API
As the AI alters the pattern, the BOM updates live against global cotton and synthetic indices.
No More Moodboards.
The fashion industry runs on PDFs, fragmented communication, and hope. A designer makes a sketch. A technical designer spends a week translating it into a tech pack. A factory in Vietnam spends three weeks guessing the drape, translating subjective descriptors like "relaxed fit" into actual millimeter measurements. It fails. The cycle repeats. This analog latency is the root cause of the industry's catastrophic overproduction and margin compression. We realized that solving this didn't require better project management software; it required a complete paradigm shift in how physical geometry is communicated.
The Fashneva AI Designer bypasses the human translation layer entirely. When you request a "wide-leg pleated trouser in 400gsm cotton," the engine doesn't search for an image on Pinterest. It executes a mathematical script. It generates the exact 2D pattern blocks required to construct a 3D garment that behaves precisely like 400gsm cotton under planetary gravity. By replacing subjective design language with explicit parametric formulas, we ensure that what you see on the screen is exactly what the factory laser cutter will output. The rendering is not an approximation; it is a direct visualization of the underlying DXF vector data.
Our proprietary diffusion models have been trained on over 20 million proprietary tech packs, grading rule sets, and historical yield optimizations. This allows the system to instinctively understand the structural relationships between seams, armholes, and dart placements. If you adjust the shoulder drop by 2 centimeters, the engine automatically calculates the required reciprocal adjustments to the sleeve cap and armscye curve to maintain the structural integrity of the garment, doing in 400 milliseconds what would take a senior patternmaker three hours.
The Architecture.
1. Semantic Translation Layer
When a designer types "oversized drop-shoulder hoodie in 450gsm french terry," our proprietary NLP layer (trained specifically on 50 years of pattern-making terminology and factory floor dialect) translates semantic language into strict geometric parameters. It understands that "drop-shoulder" isn't just an aesthetic—it requires a mathematical extension of the shoulder seam by a calculated ratio relative to the chest width, necessitating a shallower sleeve cap to prevent fabric bunching at the underarm. This layer converts English into a JSON schema that the physics engine can interpret.
2. The Vector Generation Engine
Unlike standard text-to-image models (Midjourney, DALL-E) which output meaningless, rasterized pixels that factories cannot use, Fashneva's generation engine outputs parametric vector graphics. The AI draws the precise 2D pattern pieces (front, back, sleeves, hood, ribbing, pocket bags) required to construct the garment. These pieces are fully scalable, mathematically flawless, and instantly exportable as DXF-AAMA files—the universal language of factory laser cutters. Every curve is a Bezier path, every notch is a marked coordinate, and every seam allowance is automatically added based on the selected machine operation.
3. Predictive Physics Pipeline
A pattern is entirely useless if it doesn't drape correctly in reality. Once the vector paths are drawn, our physics simulator applies the specific material properties to the digital cloth. For a 450gsm french terry cotton, the engine calculates the bend resistance, shear strength, friction coefficients, and shrinkage tolerances before simulating gravity drape on a rigged avatar. If the hood is too heavy and pulls the neckline backward (a common flaw in heavy hoodies), the AI autonomously detects the collision error and adjusts the front neck drop and shoulder angle to mathematically compensate, solving the fit issue before a physical sample is ever sewn.
4. Autonomous Algorithmic Grading
A human pattern maker takes days to manually grade a base pattern from size XS to XXL, carefully calculating the X and Y coordinate shifts for every single notch and corner. Fashneva executes this in 400 milliseconds. Using our anthropometric database of over 100,000 3D human body scans across diverse global demographics, the engine automatically calculates complex, non-linear grading rules that account for real human volume changes, rather than simple mathematical scaling. The system then generates the complete, nested size run, ready for immediate robotic cutting.
EXECUTING GRADING SCRIPT // AUTO-SCALE
The Prompt Engineering Guide.
Fashneva OS uses a massive, proprietary LLM trained specifically on billions of tech pack parameters, historical grading curves, and textile physics. To extract the best possible DXF vectors, you need to speak its language.
A good prompt doesn't just describe a "cool shirt." It describes the physical construction, the material weight, and the precise fit topology.
Bad Prompt
"Make a really cool oversized black hoodie for men."
- No pocket construction (Kangaroo? Hidden seam?)
- "Oversized" is subjective and mathematically meaningless.
Good Prompt
"Mens pullover hoodie, dropped shoulder by 2 inches, 450GSM loopback cotton, kangaroo pocket with bar-tack reinforcement, double-layered hood without drawstrings, ribbed cuffs 3 inches wide. Pantone 19-4052."
- Exact shoulder drop allows precise DXF plotting.
- Specific construction details (bar-tacks) inform the costing engine.
Supported Base Silhouettes.
* The neural network is continuously training. New base topologies are added bi-weekly.