Event Decor Spec-hardening Agent
CAM sends reference images with little or no written context. Simone interprets the images against her memory of Jeff's preferences, writes a spec from scratch, flags gaps, and sends to Jeff. If Jeff's feedback reveals wrong material choices or missing details, Simone rewrites and resubmits. The first draft is the bottleneck.
CAM sends reference images. Simone uploads them to the agent. The agent reads the images, references the 'How Jeff Thinks' knowledge base, and drafts a structured spec in the format Simone would write to Jeff. Simone reviews the draft in the ticket, makes edits if needed, and sends to Jeff. The spec is ready to send without rewriting.
Reference images arrive with no context. Simone rewrites them into a spec from scratch.
When a custom event build request lands, the input is almost always images: AI mockups, Pinterest inspo, photos from other events.
Simone spends 2-3 hours per project on the first draft because she's starting from a blank page and inferring material choices, dimensions, and feasibility from images alone.
Jeff's expertise about lightweight materials, freight-elevator constraints, and event-build finishes is not documented. It lives in his correction emails and Simone's memory of past projects.
General Claude suggests permanent-construction materials because its training data skews toward building, not event fabrication. FED needs lightweight, modular, transportable builds that fit hotel freight elevators.
Simone gets a first-draft spec in minutes, not hours. She reviews and sends.
Agent produces item label, H×W×D, materials, surface finish, LED details, and special notes. Simone reviews and forwards to Jeff without rewriting.
The agent grounds suggestions in Jeff's actual fabrication constraints and past corrections, not generic construction knowledge.
When critical info is missing (dimensions, venue constraints, finish type), the agent surfaces a checklist of specific questions Simone can resolve with the CAM or fill herself.
Agent drafts, Simone reviews and edits, nothing goes to Jeff without her approval. No autonomous sends.
The agent reads reference images, applies FED's lightweight-transportable-modular constraints from the knowledge base, and drafts the structured spec in the exact format Simone would write to Jeff. Simone reviews, edits if needed, and sends. The first draft no longer starts from a blank page.
From images to spec in four steps
The agent accepts whatever input arrives, reference images, brief notes, or both, and produces a first-draft fabrication spec grounded in FED's real material knowledge and fabrication constraints.
Simone uploads the images the CAM sent (AI mockups, inspo photos, Pinterest links) and any written brief. Images alone are enough; text is optional.
The agent analyzes the images, references the 'How Jeff Thinks' knowledge base (FED's fabrication rules and past examples), and infers materials, dimensions, and construction approach.
The agent produces a structured spec in the format Simone writes to Jeff: item label, H×W×D, materials, finish, LEDs, special notes. Missing critical info appears as a checklist of clarifying questions.
Simone reviews the draft in the ticket, makes edits if needed, and sends to Jeff with one click. The ticket holds all images, the spec, and revision history in one place.
Reference images +
AI-generated mockups, Pinterest inspo, photos from other events, or any visual reference the CAM sends.
Written brief or notes +
Optional. CAM notes, client requirements, budget signal, or any text context. Often absent.
FED fabrication constraints +
The 'How Jeff Thinks' knowledge base: lightweight, modular, transportable, freight-elevator-sized, preferred materials and finishes, and past corrections from Jeff's emails.
Image analysis +
Agent reads the reference images and infers approximate dimensions, materials, and construction approach.
Constraint application +
Agent applies FED's lightweight-transportable-modular rules and material preferences from the knowledge base to ground the spec in what FED can actually build.
Gap detection +
Agent identifies missing critical information (dimensions, venue constraints, finish type, LED requirements) and surfaces specific clarifying questions.
Spec formatting +
Agent produces the structured spec in the exact format Simone writes to Jeff: item label, H×W×D, materials, surface finish, LED details, special notes, and flagged gaps.
First-draft fabrication spec +
Structured text spec with item label, H×W×D, materials, surface finish, LED details, and special notes. Ready for Simone to review and forward to Jeff.
Gap checklist +
Specific clarifying questions for critical missing information. Simone resolves these with the CAM or fills them herself before sending to Jeff.
Ticket with full context +
All input images, the draft spec, and revision history stored in one place so context is not lost across rounds.
Is this for you?
- + Lead designers handling custom fabrication requests - Simone and teams like her who sit between sales and the fabrication shop and produce first-draft specs from mixed-format client inputs.
- + Event decor and scenic fabrication companies - Shops with unwritten fabrication expertise (material knowledge, transport constraints, assembly rules) that lives in one person's head or in scattered correction emails.
- + Teams where the first draft is the bottleneck - Workflows where the designer spends 2-3 hours per project rewriting client images into a spec because the input is almost always visual with no context.
- + Operations teams documenting fabrication knowledge - Alex and teams building SOPs and knowledge bases from expert feedback emails so that expertise is not lost when the expert is unavailable.
- - Permanent construction or building projects - The agent is tuned for lightweight, modular, event-build materials and constraints. It will not produce specs for permanent structures, heavy steel, or building-code-driven construction.
- - Inventory-only or rental projects - Projects assembled entirely from existing stock do not need a spec agent. Simone is not involved in those workflows.
- - Pricing or estimation - The agent produces text specs only. Jeff prices the spec separately using the Build Out Order Form. Estimation is not in scope.
- - Visual renders or image generation - The agent produces text-based specs. Simone continues using SketchUp and ad-hoc AI tools for visual mockups.
Scoped build plus usage-based runs
The agent is a custom build for Florida Event Decor. Pricing covers the initial build, knowledge base assembly, and integration with FED's Google Workspace and email systems. Ongoing usage is metered by the number of spec-generation runs and knowledge base updates.
- Build scope includes: classical agent for spec generation, image input handling, gap detection, ticket storage, and Simone's review-and-send interface.
- Knowledge base assembly (the 'How Jeff Thinks' doc) is a prerequisite and is scoped separately with FED's operations team.
- Usage-based pricing covers spec-generation runs and knowledge base updates as FED's fabrication constraints evolve.
- Pricing and estimation (the Build Out Order Form) remain Jeff's responsibility and are not included in the agent.
What input format does the agent accept?
The agent accepts whatever arrives: reference images (AI mockups, Pinterest inspo, event photos), written briefs, or both. Images alone are sufficient. Text context is optional.
Does the agent produce pricing or estimates?
No. The agent produces text-based fabrication specs only. Your estimator (Jeff in FED's case) prices the spec separately using your standard pricing form. Estimation is not in scope.
How does the agent know your fabrication constraints and material preferences?
The agent references a knowledge base document (your fabrication rules, preferred materials, transport constraints, and past corrections from your estimator). This document must be assembled before the agent can produce FED-realistic specs. Without it, the agent defaults to generic construction knowledge.
What happens when critical information is missing from the client input?
The agent surfaces a checklist of specific clarifying questions (dimensions, venue constraints, finish type, LED requirements) rather than guessing. You resolve these with the client or fill them yourself before sending the spec to your estimator.
Does the agent send specs to your estimator automatically?
No. The agent drafts the spec. You review it in the ticket, make edits if needed, and send to your estimator with one click. Nothing goes out without your approval.
Can the agent generate visual renders or mockups?
No. The agent produces text-based specs only. You continue using SketchUp and other tools for visual mockups and renders.
How does the agent improve over time?
When your estimator sends back corrections (wrong materials, missing details, structural feedback), those corrections feed into your knowledge base. Each subsequent draft benefits from what your estimator would have caught before, so the agent's suggestions become more accurate as it accumulates real feedback from your team.
Is this agent built for permanent construction or building projects?
No. The agent is tuned for lightweight, modular, event-build materials and constraints. It will not produce specs for permanent structures, heavy steel, or building-code-driven construction. It is built for event decor and scenic fabrication teams.
Parallel workflow for teams that need first-draft spec generation with human review before external handoff.
Similar document-assembly pattern for teams that turn images and notes into structured review-ready outputs.
Ready to turn reference images into first-draft specs?
The agent reads the images your CAM sends, applies your fabrication constraints and material knowledge, and drafts the spec your designer would write. Your designer reviews and sends to your estimator. No more blank-page rewrites.