Our process
Our process
Our process
Our process
On
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On
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Basic
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On
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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
