Our process

Our process

Our process

Our process

On

Subscription

Basic

Pro

Custom

On

Subscription

Basic

Pro

Custom

On

Subscription

Basic

Pro

Custom

On

Subscription

Basic

Pro

Custom

01. Engage

We explain our services, discuss your business goals and make a flexible agreement, allowing us to define specific assignment orders later.

On

Subscription

Basic

Pro

Custom

01. Engage

We explain our services, discuss your business goals and make a flexible agreement, allowing us to define specific assignment orders later.

02. Request

Start requesting the workflow-automations and AI applications you need, our developers are right there to transform your ideas into reality.

02. Request

Start requesting the workflow-automations and AI applications you need, our developers are right there to transform your ideas into reality.

Ryan H.

import openai
from Autodesk.Revit.DB import Document, ElementId

def suggest_clt( self, element_id ):
"""
Suggests the most appropriate CLT element type based on contextual data & embedded Fabrify data.
"""
element = self.doc.GetElement( element_id )
element_info =
f"Element Name: {element.Name},
Category: {element.Category.Name},
Type: {element.GetType( ).Name}"

prompt = f"Given this element, suggest an appropriate CLT type: {element_info}"
response = openai.Completion.create(
engine = "fabrify-clt-v3",
prompt = prompt,
max_tokens = 1000 )

print( "Suggested CLT Wall Type:", response.choices[ 0 ].text.strip( ) )
self.doc.FabrifyCLTWallType = response.choices[ 0 ].text.strip( )

Ryan H.

import openai
from Autodesk.Revit.DB import Document, ElementId

def suggest_clt( self, element_id ):
"""
Suggests the most appropriate CLT element type based on contextual data & embedded Fabrify data.
"""
element = self.doc.GetElement( element_id )
element_info =
f"Element Name: {element.Name},
Category: {element.Category.Name},
Type: {element.GetType( ).Name}"

prompt = f"Given this element, suggest an appropriate CLT type: {element_info}"
response = openai.Completion.create(
engine = "fabrify-clt-v3",
prompt = prompt,
max_tokens = 1000 )

print( "Suggested CLT Wall Type:", response.choices[ 0 ].text.strip( ) )
self.doc.FabrifyCLTWallType = response.choices[ 0 ].text.strip( )

Ryan H.

import openai
from Autodesk.Revit.DB import Document, ElementId

def suggest_clt( self, element_id ):
"""
Suggests the most appropriate CLT element type based on contextual data & embedded Fabrify data.
"""
element = self.doc.GetElement( element_id )
element_info =
f"Element Name: {element.Name},
Category: {element.Category.Name},
Type: {element.GetType( ).Name}"

prompt = f"Given this element, suggest an appropriate CLT type: {element_info}"
response = openai.Completion.create(
engine = "fabrify-clt-v3",
prompt = prompt,
max_tokens = 1000 )

print( "Suggested CLT Wall Type:", response.choices[ 0 ].text.strip( ) )
self.doc.FabrifyCLTWallType = response.choices[ 0 ].text.strip( )

Ryan H.

import openai
from Autodesk.Revit.DB import Document, ElementId

def suggest_clt( self, element_id ):
"""
Suggests the most appropriate CLT element type based on contextual data & embedded Fabrify data.
"""
element = self.doc.GetElement( element_id )
element_info =
f"Element Name: {element.Name},
Category: {element.Category.Name},
Type: {element.GetType( ).Name}"

prompt = f"Given this element, suggest an appropriate CLT type: {element_info}"
response = openai.Completion.create(
engine = "fabrify-clt-v3",
prompt = prompt,
max_tokens = 1000 )

print( "Suggested CLT Wall Type:", response.choices[ 0 ].text.strip( ) )
self.doc.FabrifyCLTWallType = response.choices[ 0 ].text.strip( )

03. Build

Our developers swiftly begin building your custom solutions, prioritising speed without compromising on quality.

Ryan H.

import openai
from Autodesk.Revit.DB import Document, ElementId

def suggest_clt( self, element_id ):
"""
Suggests the most appropriate CLT element type based on contextual data & embedded Fabrify data.
"""
element = self.doc.GetElement( element_id )
element_info =
f"Element Name: {element.Name},
Category: {element.Category.Name},
Type: {element.GetType( ).Name}"

prompt = f"Given this element, suggest an appropriate CLT type: {element_info}"
response = openai.Completion.create(
engine = "fabrify-clt-v3",
prompt = prompt,
max_tokens = 1000 )

print( "Suggested CLT Wall Type:", response.choices[ 0 ].text.strip( ) )
self.doc.FabrifyCLTWallType = response.choices[ 0 ].text.strip( )

03. Build

Our developers swiftly begin building your custom solutions, prioritising speed without compromising on quality.

Speed

Security

Accuracy

Speed

Security

Accuracy

Speed

Security

Accuracy

04. Test & Optimise

We iteratively test and review our milestones together with you, so that we can maximise feedback whilst ensuring we work to a well-structured, realistic plan.

Speed

Security

Accuracy

04. Test & Optimise

We iteratively test and review our milestones together with you, so that we can maximise feedback whilst ensuring we work to a well-structured, realistic plan.

05. Become An Industry Leader

We offer the possibility to our clients to engage in extended parnterships, helping both organisations towards becoming a worldwide industry leader.

05. Become An Industry Leader

We offer the possibility to our clients to engage in extended parnterships, helping both organisations towards becoming a worldwide industry leader.

Get in touch

Get in touch

Get in touch

Get in touch

Office

Bloxhub, Bryghuspladsen 8

Copenhagen 1473

Denmark

Bloxhub, Bryghuspladsen 8

Copenhagen 1473

Denmark