We combine human creativity and machine learning to invent extraordinary experiences.
We work at the intersection of art and technology, humans and machines.
Deeplocal’s superpower is humanizing technology. We believe that hybrid intelligence—AI and human intelligence working together—can result in remarkable things.
This extends to creativity in advertising. AI is a powerful tool in our tool belt, but human interaction drives everything we do.
We believe that AI is a tool to catalyze creativity and make fun IRL.
For years, our creative engineers have used AI and machine learning to create brand experiences that feel both magical and relatable.
We’ve built or tuned models for object detection, classification tracking, style transfer, and more. But it’s what these tools help us create in the real world that matters.
Powering real-world experiences with AI/ML
We let Flaming Lips fans compose a song with the band live using Google AI.
We created electromechanical flowers that bloom in response to visitors using real-time sensing and ML.
We generated algorithmic chalk portraits using Einstein’s handwriting and equations.
We transported fans into the world of Rick and Morty with body pose and facial expression tracking.
We invited guests to create future worlds with words, using Google Imagen.
We built an amusement park ride to show how Google Assistant makes our lives easier at every turn.
We worked with Virgin Voyages to create custom video invites from JLo.
We let Next attendees create personalized postcards in minutes with generative AI.
We explained how Deutsche Bank is using AI to reshape the future of business.
We showed off the drive thru of the future with Wendy’s FreshAI.
Follow along for our points of view and future thinking around AI.
Before they were buzzwords, AI and ML were integral parts of our creative engineering process.
We use existing ML platforms and also architect and build our own models to:
Recognize
Identify constellations of points to recognize things like faces and poses
Transform
Modify or augment existing media based on inputs like style, emotions, or user preferences
Generate
Create brand new media based on inputs like text, image, or sound
Scale (MLOps)
Create infrastructure to deploy ML models in production (i.e. make sure they work to support high-profile events and campaigns)
