> QUERY: "Staff training for Revit-to-AI workflow; implementing LoRA for strict style/site consistency; setup and maintenance contract."
1. OPERATIONAL SCAN (THE PROBLEM)
Agent analyzed standard AI output vs. Firm Requirements.
Constraint Identified: Generic models (Stable Diffusion XL, Flux) default to a "Pinterest/ArtStation" aesthetic that lacks the firm's specific branding or regional context (e.g., "Pacific Northwest Modernism").
The Conflict: Without custom training, staff wastes hours "prompt engineering" trying to remove hallucinations. Inconsistent output across different project phases confuses clients.
The Agentic Fix: Train two distinct LoRA (Low-Rank Adaptation) models:
1. [FIRM_STYLE_V1]: Trained on 30+ past high-quality renders to lock in lighting, color palette, and material vibes.
2. [SITE_CONTEXT_PNW]: Trained on local flora/weather data to ensure "Grey Sky" realism rather than generic "California Sun."
2. TECHNICAL IMPLEMENTATION & CURRICULUM
> DATASET PREP: Requires curation of 20-50 high-res images from the firm's portfolio. Images must be captioned (tagged) manually to teach the AI what represents the "Firm Style." > WORKFLOW: Revit Massing > ControlNet (Geometry Lock) > LoRA Injection > Upscale.