A canvas can draw a jacket. It can't cost one.
Refabric is an all-in-one AI fashion tool that helps brands, retailers, and manufacturers design garments, generate automated tech packs, and produce AI product photography. FLORA is a horizontal multi-model canvas with real breadth. What it has no reason to have is fashion's vocabulary: placement-itemised BOM lines, seam relationships, pre-graded size charts, CMT costing, runway ingestion filtered by designer. Refabric is apparel-only on purpose, because that vocabulary is the difference between an asset and a product.
Free trial. No credit card required.
Side by side
Refabric vs FLORA
| Capability | Refabric | FLORA |
|---|---|---|
| Scope | Trend analysis, collection development, design and variation, fabric and print, technical specs with pattern blocks and 3D, costing, manufacturability checks, AI models, photoshoots and campaign imagery | Horizontal multi-model AI canvas; fashion is one studio among many |
| Agentic workflow | Agentic workflows coordinate tasks with human review, carrying the collection brief, material choices and commercial targets between stages, so a fabric change propagates to cost and technical requirements | The FAUNA agent, a public API and MCP; agents operate canvas nodes rather than fashion stages |
| Design Canvas | Moodboards, Brand DNA, brand assets, fabrics and trims attached as references and called into one prompt with “@”, so a single generation draws on all of them | Node graph across 50+ models, saved workflows, batch permutation |
| Brand DNA | A locked narrative, colour palette and silhouette, fabric and construction tags, so a whole capsule generated from it shares one design language automatically | Style DNA and brand kits trained on aesthetic references |
| Fabric & print | Masked Edit & Variate against an approved fabric reference; Print Tools places repeats at true scale and rotation; Smart Recolor takes an exact hex to a brushed region | Fabric as a prompt term |
| Trend data | Explore Trends surfaces runway and social looks filtered by city, season, year, gender and designer, with AI analysis per look and Style Crops isolating construction | Not a capability |
| Tech Pack & cost | One approved image to BOM, Placement Guide callouts, pre-graded size chart, pattern blocks carrying panels, seam relationships and measurements, 3D assembly and a Cost Sheet | Not a capability |
| Production Intelligence | Flags manufacturability issues on the spec, suggests fabric alternatives on performance grounds, and benchmarks the garment against category standards | Not a capability |
| AI models & PDP | Bulk AI photoshoots at catalogue scale, face and texture held across every pose, output at platform-exact dimensions | Strong imagery and video; no guaranteed identity across a set |
| Access | Public pricing, free trial, enterprise tier | Free to start |
| Best for | Freelance designers through to enterprise groups: studios, agencies, creative and production offices, brands, retailers and manufacturers | Teams whose work spans far beyond apparel |
Highlighted rows are the eight capabilities that decide whether a design becomes a product that sells.
Why the output is better
Design intent carried into construction
One approved design image becomes a complete technical package in minutes. The Bill of Materials auto-populates with fabrics, trims, closures and hardware itemised by type and placement, editable in place for supplier-specific sourcing. Placement Guide pins construction onto front, back and side artwork as callouts. The size chart arrives pre-graded. Pattern blocks represent garment panels, seam relationships and measurements as structured construction information, so designers and patternmakers can review construction, ease and likely fabric consumption against material requirements before anything is cut. A Cost Sheet gives CMT plus material from real marketplace fabric prices adjusted for manufacturing geography. Digital checks support the process; physical fit and production readiness still require expert assessment and sample validation.
No general canvas represents seam relationships or itemises a BOM by placement, because no general canvas needs to.
The brief travels with the work
Agentic workflows coordinate tasks across the platform with human review at each stage, carrying the collection brief, the material choices and the commercial targets forward instead of leaving them in somebody's inbox. Because product context is shared, a team can see how a fabric or construction change moves cost and technical requirements at the same time, in the same place. That is what removes repeated handoffs and avoidable revisions. Designers, merchandisers, buyers, patternmakers, production teams and marketers work in one workspace rather than six, and the agents coordinate between them rather than replacing anyone's judgement.
FAUNA directs a canvas and chains models. That is a different job from carrying a collection brief, a material choice and a cost target between a designer, a patternmaker and a buyer.
Live signals, not a training set
Explore Trends surfaces relevant looks from runway shows and social media, filterable by city, season, year, gender and designer, and breaks each look down into an AI-generated attribute analysis. Style Crops isolate a specific construction out of a look so it can be carried into a design as a reference. The data is ingested continuously, which means a design can be generated against what is happening in fashion now rather than against whatever a model absorbed eighteen months ago. No general image model can do this, because it is a data operation rather than a model feature.
Live runway and social ingestion versus a model's training set. The sharpest line between a fashion platform and a general one.
Change one component. Leave everything else alone.
Fabrics are trained on composition and performance data, so the prototype shows the texture, drape, fit and movement that blend actually produces, and a library of thousands ships with every package. Edit & Variate masks a single component with a brush, a magic wand or a text description, attaches the approved fabric, and leaves every other approved detail untouched. Print Tools places repeats, engineered prints, logos and placement graphics at true scale and position, with Make Seamless turning any pattern into a tileable repeat. Smart Recolor takes an exact hex or palette colour to a brushed region without touching the design, so a colourway stays a colourway rather than becoming a new design.
On a horizontal canvas, silk is a word. In Refabric it is a trained reference masked onto one component.
The platform reads the spec back
Once the pack exists, a Production Intelligence layer reads it. It flags manufacturability issues before the spec leaves the building, suggests fabric alternatives on performance grounds rather than on price alone, and benchmarks the garment against category standards so an outlier is visible while it is still cheap to change. This is the difference between software that records a decision and software that has an opinion about it. A construction that will cause trouble on the line gets caught at the desk, not at the first proto, and the designer finds out while the design is still open.
A general canvas has no notion of a category standard, a manufacturability risk or a fabric alternative, because it has no notion of a garment being made.
Catalogue imagery at scale, with one face
Bulk AI photoshoots generate catalogue and campaign imagery at the volume a collection launch actually needs. Ghost and flat lay shooting produces a matched view set from one garment reference; Product to On-Model applies a chosen model, avatar or mannequin with front, back, side and three-quarter templates, carrying the garment's fit, proportions and details over from the source. Lighting holds while only pose and camera angle change, face consistency maintains identity across the whole product page, stitch patterns and print quality survive every angle, and output lands at the exact dimensions the platform requires. The virtual models are yours to create and reuse.
Face consistency across a catalogue launch is a fashion requirement, not something a general model is built to guarantee.
Every reference you have, in one prompt
Attach a runway look from Moodboards, your Brand DNA, a fabric reference and an approved trim to the canvas, then call each one into the prompt with “@”. The generation merges only the pieces you named, so trend direction, brand identity and real materials arrive in the same instruction instead of across three separate rounds. Sketch to Design accepts a flat technical drawing as the structural base and keeps the original construction while the prompt supplies what the sketch cannot show. Nothing is re-typed, and the thread that produced a concept stays attached to it.
Model breadth is not reference depth. Brand DNA, fabrics, trims and runway looks are callable objects here, not prompt adjectives.
One profile, and the capsule holds together
Brand DNA runs a deep analysis of your own visual assets: fabric type, weight, drape and texture; colour palettes, tones and the relationships between them; silhouette, construction and proportions; and detail-level specifics down to trims, stitching patterns, closures and embellishments. Every design generated against a selected Brand DNA draws from the same locked narrative, palette and construction tags, so a whole product family shares one design language automatically rather than by review. Component recommendations stay scoped to libraries you have already approved.
Brand kits hold a look. A profile built from garment assets holds silhouette, proportion, trims and stitching.
Sustainability & traceability
Generic image generation does nothing about sample rounds, because nothing it produces reaches a factory.
Count the journeys a sample takes before it appears on a website: factory to brand, brand to fit session, back for correction, then to a studio with a model, a photographer and a day. Much of that exists to produce images rather than to produce a garment. Bulk AI photoshoots remove the logistics stacked around it, and catching a manufacturability problem at the desk means fewer styles reach the sample stage twice. Because the imagery exists before the garment does, a collection can also be presented and sold earlier, which is where production quantities stop being a forecast. On traceability, everything a digital product passport is expected to ask for already lives inside a tech pack: composition, components and their placement, suppliers, measurements. Refabric holds it as editable structured fields exportable to Excel rather than trapped in a PDF designed for printing. The textiles delegated act is indicatively scheduled for 2027 with a transition period following. If your BOM is structured and versioned, a product passport is an export with a mapping layer; if it is a PDF, it is a project with a budget and a consultant.
FAQ
Frequently asked questions
What is the best FLORA alternative for fashion?
Refabric, if the output is garments that get manufactured. FLORA is the better fit if apparel is one brief among many.
Can a general AI canvas replace Refabric?
Not downstream of the render. There is no BOM, no seam information, no cost sheet and no guarantee the model's face holds across a set.
Does Refabric have an enterprise story?
Structured PDF, Excel and SVG export and agentic workflows with human review today, with PLM connectors for Centric and FlexPLM on the roadmap inside twelve months.
From trend signal to production ready, in one platform.
Design Smarter · Market Faster