Architecture is data. I build the systems that make it speak.

WORKFLOW AREAS

BIM Automation

Dynamo pyRevit Python

Automating repetitive BIM tasks through scripting and parametric workflows, reducing manual effort and standardizing outputs across disciplines.

Problem: Repetitive manual tasks consume valuable project time, while manual data handling increases the risk of inconsistencies between BIM models, project documentation, schedules, and deliverables.

Result: By leveraging Dynamo, pyRevit, Python, and other automation tools, repetitive workflows can be automated, data synchronization improved, and consistency maintained across BIM models and project documentation, reducing errors while increasing efficiency.

AI-Assisted Design

Claude Krea ChatGPT

Leveraging AI tools to accelerate design exploration, generate photorealistic visualizations, and streamline documentation workflows.

Problem: Traditional design exploration, visualization, and documentation workflows are often time-consuming, requiring multiple iterations across different software while repetitive production tasks slow project delivery.

Result: Leveraging AI tools accelerates concept development, generates high-quality photorealistic visualizations, and streamlines documentation workflows, enabling faster design iterations, improved communication, and more efficient project delivery.

Data & Coordination

IFC IDS Power BI Solibri

Structuring BIM data for interoperability, rule-based model checking and live reporting dashboards for project stakeholders.

Problem: Poorly structured BIM data and inconsistent information standards reduce interoperability, hinder automated quality control, and make project reporting time-consuming and unreliable.

Result: Structuring BIM data using standardized information requirements enables seamless interoperability, rule-based model checking, and live reporting dashboards, improving data quality, project transparency, and informed decision-making.

Visualization Pipeline

Lumion Photoshop InDesign

Building end-to-end visualization workflows from BIM model to photorealistic render, integrating AI post-processing for final outputs.

Problem: Architectural visualization workflows often require multiple disconnected software platforms, repetitive file transfers, and extensive manual post-processing, resulting in longer production times and inconsistent outputs.

Result: An end-to-end visualization workflow seamlessly transforms BIM models into photorealistic renders, integrating AI-assisted post-processing to accelerate production, enhance visual quality, and ensure consistent deliverables.

IDEOLOGY

UNHINGED CURIOSITY, SKIN IN THE GAME, NOT JUST SOFTWARE, BUT WORKFLOWS.

PROJECT SHOWCASES

01 | Room Data Sheet

Converting room data to a single A4 sheet for each room through Dynamo.

Dynamo Python HTML

Challenge: Room data was scattered across multiple Revit schedules with no unified, printable output per room — coordinators had to manually compile information for each space.

Solution: A fully automated pipeline that generates one formatted A4 PDF for each room, using live data directly from the Revit model. Dynamo extracts the room information, while a Python node structures the data into an HTML file and saves it to a designated directory. The HTML files are then converted into individually formatted PDF sheets, creating a complete and automated room documentation workflow.

02 | AI-Assisted Visualization Workflow

Professional visualization setup from Revit to ChatGPT.

Revit Revit Render Photoshop Claude AI ChatGPT

Challenge: Creating highly realistic architectural renders in less time.

Solution: The workflow begins with a selected Revit scene, which is rendered and then annotated in Photoshop to indicate the objects or elements that need to be added or modified. Claude AI is then used to generate a detailed prompt based on these annotations. The annotated image and the AI-generated prompt are subsequently provided to ChatGPT to produce the final visualization.

03 | Revit family library synchronization via RBP

Revit Batch Processor.

Revit Revit Batch Processor Dynamo Claude AI

Challenge: How to keep synchronized Revit families along all the firm's standard.

Solution: RBP opens a model in the background, runs a Dynamo graph against it, saves, and closes — no UI, no manual intervention.

04 | pyRevit extention

Custom pyRevit extension.

Python pyRevit Claude AI

Challenge: For users who are not familiar with Dynamo, converting selected scripts into a custom pyRevit extension provides a simpler and more intuitive interface, allowing them to access the same functionality without having to navigate or understand the underlying Dynamo graphs.

Solution: Using the same tools through a simpler and more intuitive interface.

Project 01

05 | Room based dimention

Inserting vertical and horizontal dimension using room boundaries.

Dynamo Python

Challenge: On large-scale projects, manually dimensioning thousands of rooms is a time-consuming and repetitive task.

Solution: The Dynamo script extracts room boundaries and the internal faces of surrounding walls, then automatically places horizontal and vertical dimensions between two opposite walls.

06 | Sheet creation from document list

Creating sheets, numbering and naming through document list.

Dynamo Python

Challenge: Creating sheets manually is time-consuming and can lead to inconsistencies with the project document register.

Solution: Sheets are generated automatically from the document register, ensuring consistency, reducing manual effort, and minimizing human error.