HOLIXORAWhen an architect uploads a DWG file to Griya Studio, they wait less than a minute for a photorealistic render.
That turnaround sounds simple. The pipeline that produces it is not. This is a technical walkthrough of how it works.
The Problem It Solves
Traditional architectural rendering requires specialized 3D modeling software, hours of setup, and either a skilled operator or an outsourced render farm. For a small architecture firm handling multiple projects simultaneously, this is both expensive and slow.
The industry standard before Griya Studio: two to five business days for a professional render. Cost: Rp 500,000 to Rp 2,000,000 per image depending on complexity.
Griya Studio's benchmark: under sixty seconds. Cost: a fraction of that.
The Pipeline, Step by Step
Step 1: File ingestion and parsing.
The user uploads a DWG file through the web interface. The system accepts DWG (AutoCAD), DXF, and PDF floor plan formats.
A parsing layer extracts the structural geometry from the file: walls, openings, room boundaries, and scale information. This is not a simple image conversion. The system reads the vector data and constructs a spatial model of the floor plan.
Edge cases here are significant. Architects use different CAD conventions, layer naming structures, and scale notations. The parser has been trained on thousands of Indonesian architectural drawings and handles the most common variations.
Step 2: Scene construction.
The extracted geometry is passed to a scene construction module that builds a 3D environment. The system places furniture, lighting fixtures, and surface materials based on the room type and any style parameters the user specified at upload.
This is where AI does the heavy lifting. The system does not just populate a room randomly. It understands that a 12-square-meter bedroom needs different staging than a 40-square-meter living room. It reads Indonesian residential conventions. The output looks like a professionally staged space because the model was trained on professionally staged spaces.
Step 3: Rendering.
The constructed scene is passed to the render engine. This is a diffusion-model-based image generation pipeline fine-tuned on architectural photography. The model has been calibrated to produce photorealistic output with accurate lighting simulation, material textures, and spatial proportions.
The render takes the longest of any step. On current infrastructure, a standard bedroom render at 1920x1080 takes approximately 35-45 seconds on GPU.
Step 4: Post-processing and delivery.
Output images pass through a quality check layer that validates resolution, detects obvious artifacts, and flags renders that may have failed to represent the input geometry correctly. Failed renders trigger an automatic retry. Passing renders are delivered to the user's dashboard and emailed immediately.
The Infrastructure
The pipeline runs on a Kubernetes cluster with auto-scaling GPU workers. During peak hours, the cluster scales horizontally to maintain sub-60-second SLAs across concurrent requests.
The backend is FastAPI with an async task queue (ARQ with Redis). Each render job is queued, picked up by a worker, executed, and the result is pushed back to the client over a WebSocket connection so the user sees the result appear in real time.
The database is PostgreSQL with a separate object storage bucket (S3-compatible) for all render inputs and outputs.
What We Are Still Improving
Transparency about limitations matters. Current areas where the pipeline produces inconsistent results:
- Highly complex multi-level sections. Floor plan drawings with split-level rooms or mezzanines sometimes produce incorrect scene geometry.
- Unusual Indonesian architectural styles. The model is strongest on contemporary residential and commercial interiors. Traditional Joglo or Minangkabau layouts occasionally require manual correction.
- Exterior renders. The current system focuses on interior spaces. Exterior renders from site plans are on the roadmap but not yet in production.
We document each limitation class, track user-reported issues, and use them to drive the next fine-tuning cycle.
Why This Architecture Scales
The reason we built the pipeline this way, rather than integrating a third-party rendering API, is control. We can fine-tune the model on new data. We can add Indonesian-specific architectural knowledge. We can optimize the pipeline components independently.
A SaaS that rents intelligence from a black-box API has no edge. Griya Studio's edge is in how the pipeline is built and trained, not just that it exists.
Interested in what Griya Studio can do for your architecture firm? Try it free.