Specify the task
Agree on the task, intended use, relevant building types, available evidence, and what success means.
Data & model partnerships
Build the data.
Train the model.
Partner with RAKE ML on synthetic datasets, specialized computer vision, and real-world building data. Bring the visual task you need to solve or the evidence your team collects.
Tell us about your project
Tell us about the data you need, the data you have, or the model you want to build.
- Access synthetic training data.Scope a dataset around specific materials, defects, environments, or visual tasks.
- Develop a model together.Collaborate on specialized feature recognition, training, and real-world validation.
- Bring real-world evidence.Explore partnerships involving imagery, inspections, condition labels, or building outcomes.
Have a technical brief? Email our team
How we collaborate
From a defined task
to a working pilot.
A useful dataset starts with the model’s job. Together, we define the target features, data specification, and real-world evaluation before building the first batch.
Design the dataset
Choose the scene variables, annotation formats, and imagery requirements. Agree on data access, licensing, and delivery.
Train, evaluate, and expand
Use a focused first dataset to evaluate the model on your task. Refine the scene generation and training data around the results.
The data partnership
Connect simulation
with field experience.
Imagery shows appearance. Inspections describe condition. Repair and outcome records show what happened next. Bringing these sources together helps us build better training tasks and evaluate models on real buildings.
Inside our synthetic environments- Imagery providers
- Aerial, drone, and ground-level views across buildings, locations, and dates.
- Manufacturers & field teams
- Material expertise, inspection observations, and documented exterior defects.
- Insurance & property teams
- Building records, maintenance histories, and condition outcomes.
- Researchers & model teams
- Specialized training tasks, evaluation datasets, and technical collaboration.