A specialized generative AI system, using a fine-tuned Stable Diffusion model (LoRA), that creates unique exhibition booth designs based on user prompts (e.g., 'futuristic 10x20 booth for a tech company'). It also analyzes the generated design to provide a bill of materials and an estimated production cost.
Exhibition and event agencies spend significant time producing booth concepts for pitches. Designers create several renders, the client picks one, then estimators work out materials and costs, and the numbers often force a redesign. The design-to-quote cycle can drag on for weeks, and agencies may invest heavily in speculative pitches they do not win, with little reusable output to show for the effort.
AI exhibition booth design uses a Stable Diffusion model fine-tuned with LoRA on the agency's own past booth designs to generate concepts from a brief, such as a futuristic stand for a technology company. A computer vision step identifies key elements, such as walls, screens, counters and lighting, and maps them to a cost database to produce an indicative materials list and estimate. The output is a starting point for designers and estimators, not a final quote.
The agency gathers past booth renders, photos and matching bills of materials with costs, ensuring it owns or has rights to the images used for training.
A Stable Diffusion model is fine-tuned with LoRA on this material so it produces concepts in the agency's style and within buildable norms.
Staff enter a brief with stand size, style, brand colours and key features, and the tool generates several concept images.
Computer vision identifies elements in the chosen concept and matches them to items in a cost database, producing a draft materials list and estimate range.
Designers refine the concept into proper drawings, and estimators validate quantities and prices before any figure is given to the client.
An event marketing agency uses this tool to quickly generate multiple booth concepts for a client. The client selects a design, and the AI provides an instant budget estimate, which an estimator confirms before a formal quote.
Stable Diffusion with LoRA, Computer Vision for analysis, Cost database, Python, Web UI
Training data must be images the agency owns or is licensed to use; using competitors' or venues' photos may infringe the Copyright Act 1968. Client brand assets used in prompts should stay confidential, so host the model privately or in an Australian cloud region. Estimates shown to clients should be clearly labelled as indicative, because presenting AI estimates as firm quotes could mislead under the Australian Consumer Law. Final designs must meet venue rules and relevant building and electrical standards, such as those applied by major Australian convention centres, which the AI cannot assess. Engineering sign-off remains necessary for structures.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
They are indicative and depend heavily on the quality of the cost database and how well the vision model identifies elements. They are useful for early conversations about whether a concept fits the budget, but estimators must validate quantities, labour, freight and venue charges before giving a quote.
General image generators produce attractive but often unbuildable designs and do not reflect the agency's style. Fine-tuning with LoRA on your own past work produces concepts closer to what your team actually builds, and a privately hosted model keeps client briefs confidential. It requires more investment, which is why this is a high-cost project.
Preparing training images and cost data, fine-tuning and testing the model, building the element detection and cost mapping, creating a web interface and validating outputs with designers and estimators all take time. Most agencies pilot it on internal briefs before using it in client meetings.
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