{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_decorai","slug":"decorai","name":"DecorAI","type":"product","url":"https://decorai.io","page_url":"https://unfragile.ai/decorai","categories":["image-generation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_decorai__cap_0","uri":"capability://image.visual.room.context.aware.design.generation","name":"room-context-aware design generation","description":"Analyzes uploaded room photographs using computer vision to extract spatial context (dimensions, lighting, existing furniture, architectural features), then conditions a generative image model on these constraints to produce design variations that respect the actual room layout rather than generating abstract designs. The system likely uses object detection and semantic segmentation to identify walls, windows, doors, and existing furnishings, then passes this structured spatial data as conditioning inputs to a diffusion or transformer-based image generation model.","intents":["I want to see how a specific design aesthetic would look in my actual room before buying anything","I need to generate multiple design options that account for my room's unique layout and lighting","I want AI suggestions that respect my existing furniture and architectural constraints"],"best_for":["Homeowners and renters exploring design options for specific rooms","Budget-conscious users wanting to visualize purchases before commitment","Interior design enthusiasts seeking rapid iteration on mood boards"],"limitations":["Output quality degrades significantly with poor lighting, extreme angles, or cluttered room photos — requires clear, well-lit images from consistent viewpoints","Cannot reliably extract 3D spatial dimensions from 2D photos, limiting accuracy of furniture scale and placement suggestions","Struggles with non-standard room shapes, angled ceilings, or complex architectural features that fall outside training data distribution","No persistent understanding of user's existing furniture — each generation treats the room as a blank canvas unless explicitly referenced in prompts"],"requires":["Digital photograph of room (minimum 1080p resolution recommended)","Adequate lighting in photo (natural or artificial) for computer vision feature extraction","Internet connection for cloud-based image processing and generation","Modern web browser or mobile app with image upload capability"],"input_types":["image (JPEG, PNG, WebP)","text (design style preferences, color palette descriptions, mood descriptors)"],"output_types":["image (generated room renderings with design applied)","structured metadata (detected room features, furniture items, spatial properties)"],"categories":["image-visual","design-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_decorai__cap_1","uri":"capability://image.visual.multi.variation.design.exploration","name":"multi-variation design exploration","description":"Generates multiple distinct design interpretations of a single room in rapid succession, allowing users to explore different aesthetic directions (minimalist, maximalist, bohemian, industrial, etc.) without re-uploading photos or re-specifying constraints. Likely implements a sampling-based approach where the same room context is passed to the generative model with different style embeddings or prompt variations, enabling parallel generation of diverse outputs.","intents":["I want to see 5-10 different design directions for my room to compare aesthetics","I need to explore how different color palettes would work in my space without manual iteration","I want to quickly narrow down my preferred style by seeing multiple interpretations side-by-side"],"best_for":["Users in the exploratory phase of design decisions who haven't committed to a specific aesthetic","Indecisive homeowners who benefit from visual comparison rather than abstract descriptions","Design students or professionals using the tool as a rapid ideation accelerator"],"limitations":["Variations often cluster around trending aesthetics rather than exploring truly niche or personalized styles — tends toward safe, algorithmically-favored designs","No memory of user preferences across sessions — each new room upload requires re-specification of desired styles","Generation latency scales with number of variations requested; requesting 10+ variations may take 30-60 seconds depending on infrastructure","Variations may be superficial (color swaps) rather than structural (furniture arrangement changes) if the model lacks fine-grained control over spatial composition"],"requires":["Single room photograph uploaded to the system","Sufficient API quota or generation credits (if metered)","Browser session or app instance capable of displaying multiple high-resolution images simultaneously"],"input_types":["image (room photo)","text (optional style preferences, mood keywords, aesthetic directions to explore)"],"output_types":["image array (5-15 design variations of the same room)","metadata (style tags or aesthetic classifications for each variation)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_decorai__cap_10","uri":"capability://image.visual.augmented.reality.room.preview","name":"augmented-reality room preview","description":"Allows users to view generated designs overlaid on their actual room using AR technology (smartphone camera), enabling real-time visualization of how the design would look in their space. Likely uses ARKit/ARCore to track the room and overlay the generated design as a virtual layer, with perspective correction to match the user's viewing angle.","intents":["I want to see how a design looks in my actual room from my perspective","I want to walk around my room and see the design from different angles in AR","I want to show family or friends the design in AR to get their feedback"],"best_for":["Mobile users with AR-capable devices (iPhone 11+, Android 8+)","Users who want high-confidence visualization before purchasing","Users sharing designs with others for collaborative decision-making"],"limitations":["AR visualization requires ARKit/ARCore support — not available on older devices or non-smartphone platforms","Lighting in AR preview may not match actual room lighting, leading to inaccurate color/brightness perception","AR tracking can be unstable in low-light conditions or rooms with few visual features, causing jitter or drift","AR overlay may not account for occlusion (e.g., existing furniture blocking the view of virtual furniture), leading to unrealistic previews","Performance overhead of AR rendering may cause frame rate drops on lower-end devices","AR preview is limited to the user's current viewpoint — cannot show the full room design at once"],"requires":["Smartphone with ARKit (iOS 11.3+) or ARCore (Android 7.0+) support","Generated design image with depth/3D information","AR rendering engine (ARKit/ARCore SDK integration)","Mobile app with camera access and AR capabilities"],"input_types":["image (generated design with depth information)","camera feed (real-time smartphone camera input)"],"output_types":["AR visualization (real-time overlay of design on camera feed)","screenshot/video (captured AR preview for sharing)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_decorai__cap_11","uri":"capability://planning.reasoning.furniture.arrangement.optimization","name":"furniture-arrangement optimization","description":"Suggests optimal furniture placement and room layout based on spatial constraints, traffic flow, and design principles (e.g., focal points, balance, ergonomics). Likely uses constraint satisfaction or optimization algorithms to find furniture arrangements that maximize usability and aesthetic appeal while respecting room dimensions and existing fixtures.","intents":["I want suggestions for how to arrange furniture in my room for better flow and functionality","I want to optimize my room layout for traffic patterns and accessibility","I want design suggestions that consider both aesthetics and practical usability"],"best_for":["Users rearranging furniture or planning new layouts","Users with small spaces who need to optimize every square foot","Users who prioritize functionality and flow alongside aesthetics"],"limitations":["Furniture arrangement optimization requires accurate room dimensions and furniture sizes — estimates from photos may be inaccurate","Optimization algorithms may not account for user-specific needs (e.g., accessibility requirements, pet-friendly layouts, work-from-home ergonomics)","Suggested arrangements may not be practical to implement (e.g., moving heavy furniture, dealing with fixed fixtures like radiators)","No understanding of user's daily routines or activity patterns — optimization may prioritize aesthetics over functional needs","Optimization results may be deterministic and repetitive — users may see the same arrangement suggestions across different designs"],"requires":["Room dimensions and layout (extracted from photo or user-specified)","Furniture dimensions and properties (detected from photo or user-specified)","Constraint satisfaction or optimization algorithm (e.g., genetic algorithm, simulated annealing)","Design principles and heuristics for good layouts (focal points, traffic flow, ergonomics)"],"input_types":["image (room photo with furniture)","text (optional: functional requirements, accessibility needs, activity patterns)"],"output_types":["image (design with optimized furniture arrangement)","structured data (furniture positions, traffic flow diagram, design rationale)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_decorai__cap_2","uri":"capability://memory.knowledge.style.preference.learning.and.personalization","name":"style-preference learning and personalization","description":"Tracks user interactions (which designs users save, like, or request modifications to) and builds a preference profile to bias future generations toward their aesthetic tastes. Likely implements a collaborative filtering or embedding-based preference model that learns style affinities from user feedback, then uses these learned preferences to weight the style conditioning in subsequent generation requests.","intents":["I want the AI to learn my design taste over time so suggestions become more personalized","I want to save favorite designs and have the system recommend similar aesthetics for other rooms","I want to refine the AI's understanding of my style through feedback without writing detailed prompts each time"],"best_for":["Users designing multiple rooms who want consistent personalization across projects","Users willing to provide feedback to improve recommendation quality over time","Homeowners planning long-term renovations who benefit from style consistency"],"limitations":["Preference learning requires sufficient interaction history (likely 10+ saved designs minimum) before personalization becomes meaningful","User preferences may be inferred from limited feedback, leading to over-fitting to early choices or misinterpretation of style signals","No explicit control over preference weights — users cannot directly edit their style profile or override learned preferences","Privacy concern: preference data is stored server-side and may be used for model improvement or analytics","Preference model may not capture context-dependent preferences (e.g., user likes minimalism for bedrooms but maximalism for living rooms)"],"requires":["User account with persistent session/login","Interaction history (saved designs, likes, feedback)","Backend infrastructure for preference model training and inference"],"input_types":["user interaction signals (saves, likes, ratings, modification requests)","design metadata (style tags, color palettes, aesthetic classifications)"],"output_types":["personalized style embeddings or preference vectors","weighted style recommendations for new generations"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_decorai__cap_3","uri":"capability://data.processing.analysis.design.to.shopping.list.conversion","name":"design-to-shopping-list conversion","description":"Extracts furniture, decor items, and materials visible in generated designs and maps them to shoppable products with estimated costs, creating a structured shopping list that users can purchase from integrated e-commerce partners. Likely uses object detection to identify items in the generated image, then queries a product database or API (Amazon, Wayfair, etc.) to find matching items with pricing and availability.","intents":["I want to know what furniture and decor items are in the design and where to buy them","I need an estimated budget for implementing a specific design before committing","I want to quickly purchase the items from a generated design without manually searching for each piece"],"best_for":["Users ready to purchase and implement designs immediately","Budget-conscious shoppers who want transparent cost estimates before design selection","Users who lack time to manually source individual items"],"limitations":["Object detection may misidentify items or hallucinate products not actually present in the design, leading to incorrect shopping lists","Product matching is imperfect — detected items may not map to real products, or matches may be poor quality or overpriced alternatives","Pricing data is stale or incomplete — e-commerce APIs may not have real-time inventory or pricing for all suggested items","No budget constraints in generation — designs may suggest items far outside user's stated budget without awareness","Shopping list includes only visible items; hidden structural changes, paint colors, or lighting modifications may not be captured","E-commerce integrations may be limited to specific partners (e.g., only Amazon, not local furniture stores or specialty retailers)"],"requires":["Generated design image with identifiable furniture and decor items","Integration with e-commerce APIs (Amazon Product Advertising API, Wayfair API, etc.)","Product database with pricing and availability data","Optional: user's budget preferences or spending limits"],"input_types":["image (generated design)","text (optional budget constraints, brand preferences, retailer preferences)"],"output_types":["structured data (shopping list with item names, estimated prices, product links)","text (budget summary, cost breakdown by category)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_decorai__cap_4","uri":"capability://text.generation.language.iterative.design.refinement.via.text.feedback","name":"iterative design refinement via text feedback","description":"Allows users to request modifications to generated designs through natural language feedback (e.g., 'make it brighter', 'add more plants', 'use warmer colors') without re-uploading photos or starting over. Likely implements a prompt-engineering layer that translates user feedback into conditioning adjustments for the generative model, or uses a fine-tuning approach to adapt the model to user-specific modifications.","intents":["I want to tweak a design I like without generating a completely new one","I want to iteratively refine a design through conversational feedback","I want to explore variations of a design by requesting specific changes (more color, different furniture, etc.)"],"best_for":["Users who have found a design direction they like and want to refine it","Users who prefer iterative exploration over batch generation","Users who struggle to articulate their preferences upfront but can provide feedback on generated outputs"],"limitations":["Feedback interpretation is lossy — vague requests like 'make it nicer' may not translate to meaningful design changes","Iterative refinement can lead to style drift or incoherence if feedback is contradictory or cumulative","No explicit constraint satisfaction — feedback like 'add more storage' may be ignored if the model doesn't understand spatial implications","Latency per refinement iteration (likely 10-30 seconds) makes rapid back-and-forth refinement slower than batch generation","No undo/version history — users cannot easily revert to previous iterations if a refinement goes wrong"],"requires":["Previously generated design image to refine","Natural language processing pipeline to interpret user feedback","Generative model capable of conditional generation based on feedback embeddings"],"input_types":["text (natural language feedback: 'brighter', 'more plants', 'warmer colors', 'remove clutter', etc.)","image (previous design to refine)"],"output_types":["image (refined design incorporating feedback)","text (optional explanation of changes made)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_decorai__cap_5","uri":"capability://image.visual.design.to.3d.model.export","name":"design-to-3d-model export","description":"Converts 2D generated designs into 3D room models that users can explore interactively, walk through, or import into design software (SketchUp, Blender, etc.). Likely uses depth estimation from the original room photo combined with detected furniture dimensions to reconstruct 3D geometry, then maps the generated design onto this 3D model.","intents":["I want to explore a design in 3D before committing to purchases","I want to import the design into my own design software for further refinement","I want to see how the design looks from different angles and viewpoints"],"best_for":["Users who want high-fidelity visualization before implementation","Professional designers or architects using DecorAI as a rapid ideation tool","Users with access to 3D design software who want to extend the designs"],"limitations":["3D reconstruction from single 2D photos is inherently ambiguous — depth estimation errors compound into inaccurate 3D models","Furniture dimensions are estimated from detected objects, not measured — 3D models may have incorrect scale or proportions","Export formats may be limited (e.g., only GLTF/GLB, not native SketchUp or Blender formats), requiring conversion","3D models lack fine details (textures, materials, lighting) that make 2D designs appealing — 3D exports may look less polished than original renders","Interactive 3D exploration requires WebGL-capable browser or dedicated app; not all users have compatible devices"],"requires":["Generated design image with depth information","3D reconstruction pipeline (monocular depth estimation model)","3D model export infrastructure (GLTF/GLB generation, optional format converters)","Optional: 3D viewer or integration with design software APIs"],"input_types":["image (generated design with depth metadata)","text (optional export format preference: GLTF, FBX, SKP, BLEND)"],"output_types":["3D model file (GLTF, FBX, or native design software format)","interactive 3D viewer (WebGL-based in-browser exploration)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_decorai__cap_6","uri":"capability://planning.reasoning.budget.constrained.design.generation","name":"budget-constrained design generation","description":"Generates design suggestions that respect user-specified budget constraints, prioritizing cost-effective items and avoiding expensive pieces that exceed the stated budget. Likely integrates pricing data from the shopping-list conversion pipeline into the generation conditioning, using cost as a hard constraint or soft penalty in the model's objective function.","intents":["I want design suggestions that fit my budget without expensive items I can't afford","I want to explore design options at different price points (budget, mid-range, luxury)","I want to know upfront if a design is affordable before I fall in love with it"],"best_for":["Budget-conscious homeowners and renters","Users with fixed renovation budgets who need cost-aware suggestions","Renters who cannot make expensive permanent changes"],"limitations":["Budget constraints are difficult to enforce in generative models — the model may suggest expensive items even with budget conditioning","Pricing data is approximate and may not reflect actual costs (sales, regional variation, shipping, installation labor)","Budget constraints may force the model toward lower-quality or less aesthetically appealing designs, limiting creative exploration","No understanding of cost-benefit tradeoffs — model may suggest cheap items that look cheap rather than finding good value","Budget constraints are global (total room budget) rather than per-item, making it hard to allocate budget across furniture, decor, and labor"],"requires":["User-specified budget amount (total or per-category)","Pricing data for suggested items (from e-commerce APIs or internal database)","Generative model capable of cost-aware conditioning"],"input_types":["text (budget amount, currency, optional budget breakdown by category)","image (room photo)"],"output_types":["image (design respecting budget constraints)","structured data (itemized cost breakdown, total estimated cost)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_decorai__cap_7","uri":"capability://planning.reasoning.room.type.specific.design.templates","name":"room-type-specific design templates","description":"Provides pre-configured design templates optimized for specific room types (bedroom, living room, kitchen, bathroom, home office) with room-type-specific constraints and aesthetic guidelines. Likely uses room classification (detected from the input photo or user-specified) to select appropriate design templates, style palettes, and furniture recommendations that are optimized for that room's function.","intents":["I want design suggestions tailored to my room's function (bedroom vs. living room vs. office)","I want to use proven design templates for my room type rather than starting from scratch","I want design suggestions that respect room-type-specific constraints (e.g., bedroom lighting, office ergonomics)"],"best_for":["Users designing specific room types who want expert-guided suggestions","Users unfamiliar with design principles for their room type","Users who want to quickly apply proven design patterns to their space"],"limitations":["Templates may be too rigid or generic, limiting creative exploration for users with non-standard room types or uses","Room classification may be incorrect (e.g., misidentifying a home office as a bedroom), leading to inappropriate suggestions","Templates may not account for multi-functional rooms (e.g., bedroom-office hybrid) or non-standard uses","Template-based generation may produce less diverse or creative designs compared to unconstrained generation","Limited customization of templates — users cannot easily modify template parameters or create custom templates"],"requires":["Room type classification (auto-detected from photo or user-specified)","Pre-configured design templates for each room type","Room-type-specific design guidelines and constraints"],"input_types":["image (room photo)","text (optional room type specification, functional requirements)"],"output_types":["image (design based on room-type-specific template)","structured data (template name, design guidelines applied, room-type-specific recommendations)"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_decorai__cap_8","uri":"capability://image.visual.before.and.after.comparison.visualization","name":"before-and-after comparison visualization","description":"Displays the original room photo and generated design side-by-side or in an interactive slider format, allowing users to visually compare the before and after states. Likely uses image alignment and blending techniques to ensure the before/after images are spatially registered for accurate comparison.","intents":["I want to see the transformation from my current room to the designed room","I want to evaluate how dramatic the design change is","I want to share before-and-after comparisons with family or friends for feedback"],"best_for":["Users evaluating the impact of design changes","Users sharing design ideas with others for feedback","Users documenting renovation projects"],"limitations":["Before-and-after comparison is only meaningful if the original photo and generated design are spatially aligned — misalignment makes comparison confusing","Lighting differences between original photo and generated design may make comparison misleading (e.g., generated design appears brighter due to different lighting assumptions)","Users may over-estimate the realism of generated designs when viewing before-and-after comparisons, leading to disappointment during actual implementation","Comparison format (slider vs. side-by-side) may not work well on mobile devices with limited screen space"],"requires":["Original room photo","Generated design image with spatial alignment to original photo","Image comparison UI (slider, side-by-side, or animated transition)"],"input_types":["image (original room photo)","image (generated design)"],"output_types":["interactive UI (before-and-after comparison viewer)","image (static before-and-after composite for sharing)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_decorai__cap_9","uri":"capability://search.retrieval.design.inspiration.library.and.curation","name":"design-inspiration library and curation","description":"Maintains a searchable library of user-generated and AI-generated designs organized by style, room type, color palette, and other metadata. Users can browse, save, and use designs from the library as inspiration or starting points for their own projects. Likely uses semantic search and tagging to enable discovery of similar designs.","intents":["I want to browse design inspiration from other users' projects","I want to find designs similar to one I like","I want to save designs I love and reference them later"],"best_for":["Users seeking design inspiration before starting their own project","Users who benefit from seeing real examples of designs they like","Community-oriented users who want to share and discover designs"],"limitations":["Library quality depends on user contributions — sparse or low-quality libraries limit usefulness","Search and discovery may be limited by metadata quality — poorly tagged designs are hard to find","Privacy concerns: user-generated designs may contain personal information or be used without permission","Library may be biased toward trending aesthetics, limiting discovery of niche or personalized styles","No collaborative filtering or recommendation system mentioned — discovery may rely on manual search rather than personalized recommendations"],"requires":["Database of designs (user-generated and AI-generated)","Metadata tagging system (style, room type, color palette, etc.)","Search and discovery interface (semantic search, filtering, sorting)","Optional: recommendation engine for personalized suggestions"],"input_types":["text (search queries, style keywords, room type filters)","image (optional: image-based search for similar designs)"],"output_types":["design gallery (curated list of matching designs)","structured metadata (style tags, room type, color palette, user ratings)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Digital photograph of room (minimum 1080p resolution recommended)","Adequate lighting in photo (natural or artificial) for computer vision feature extraction","Internet connection for cloud-based image processing and generation","Modern web browser or mobile app with image upload capability","Single room photograph uploaded to the system","Sufficient API quota or generation credits (if metered)","Browser session or app instance capable of displaying multiple high-resolution images simultaneously","Smartphone with ARKit (iOS 11.3+) or ARCore (Android 7.0+) support","Generated design image with depth/3D information","AR rendering engine (ARKit/ARCore SDK integration)"],"failure_modes":["Output quality degrades significantly with poor lighting, extreme angles, or cluttered room photos — requires clear, well-lit images from consistent viewpoints","Cannot reliably extract 3D spatial dimensions from 2D photos, limiting accuracy of furniture scale and placement suggestions","Struggles with non-standard room shapes, angled ceilings, or complex architectural features that fall outside training data distribution","No persistent understanding of user's existing furniture — each generation treats the room as a blank canvas unless explicitly referenced in prompts","Variations often cluster around trending aesthetics rather than exploring truly niche or personalized styles — tends toward safe, algorithmically-favored designs","No memory of user preferences across sessions — each new room upload requires re-specification of desired styles","Generation latency scales with number of variations requested; requesting 10+ variations may take 30-60 seconds depending on infrastructure","Variations may be superficial (color swaps) rather than structural (furniture arrangement changes) if the model lacks fine-grained control over spatial composition","AR visualization requires ARKit/ARCore support — not available on older devices or non-smartphone platforms","Lighting in AR preview may not match actual room lighting, leading to inaccurate color/brightness perception","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.2,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.283Z","last_scraped_at":"2026-04-05T13:23:42.561Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=decorai","compare_url":"https://unfragile.ai/compare?artifact=decorai"}},"signature":"yWbuo2nxBZUnOfVTCdpbDogDrYtD219gZ5UehiiANR0MPXjivA4X+m+TB2yQ75yxxk1dVtnmb5XhfLshGSpFCQ==","signedAt":"2026-06-22T04:05:58.440Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/decorai","artifact":"https://unfragile.ai/decorai","verify":"https://unfragile.ai/api/v1/verify?slug=decorai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}