Introduction
Raster is a modern, AI-first DAM built for teams that want to streamline how they ship photography across the web. It includes features that help Organize, Edit, and Ship their professional photo & image assets. Currently, Raster leverages AI in organization by smartly tagging photos. AI-assisted Editing is coming soon. That left us thinking — how do we improve the shipping experience with AI?
How can we streamline the way marketing teams deliver photos to users? One of the ways that we see Raster users shipping photos is via social media posts. So we created a hypothesis that we wanted to explore: Could we, using Gemini and it’s new 1,000,000 token context window, build an AI tool for Raster that helped teams choose the best photos out of a set, and write a narrative/marketing campaign around them?
Introducing Gemini
Gemini 1.5 Pro from Google is equipped with advanced image recognition and analysis capabilities, utilizing cutting-edge technology to accurately identify objects, scenes, emotions, and other relevant features within images. This allows for a deeper understanding of the meaning behind visuals.
The tool could enhance photo management by providing detailed information and context about image content. It can assess the resonance of a photo with a target audience based on demographics and interests, predicting engagement levels based on content and composition. Additionally, Gemini can curate a selection of photos most likely to succeed in a campaign, with further refinement possible through filters and criteria.
Gemini 1.5 Pro features a 1 million token context window, enabling the processing of more information within a given prompt. This results in more consistent, relevant, and useful output when analyzing large image collections.
Unleashing Gemini's Potential: AI-Powered Features for Raster
Integrating AI at a larger scale is crucial to address an ever-growing volume and complexity of digital assets. AI can automate time-consuming tasks like tagging, categorization, and metadata generation, improving searchability and organization. Additionally, AI-powered image recognition can identify and flag inappropriate content, ensuring brand consistency and compliance. Furthermore, AI can analyze asset usage patterns and suggest relevant content, optimizing workflows and content strategies.
While object recognition has been a cornerstone of machine learning applications in image analysis, it represents only the tip of the iceberg in terms of what these technologies can achieve. To truly unlock the potential of AI, we need to move beyond simply identifying objects and delve deeper into understanding the context, emotions, and scenes depicted in images.
Raster could incorporate Gemini as an AI-powered digital assistant that understands natural language queries, allowing users to search for specific assets, perform batch edits, and even generate creative content using simple voice commands or text prompts.
AI can transform a DAM system into a platform that provides rich experiences for diverse user types. Content creators and editors benefit from AI-powered tools that accelerate and enhance their workflows, such as automated tagging, content generation suggestions, and intelligent editing features. Collaboration is streamlined through AI-driven recommendations for relevant assets and team members, facilitating seamless project management. Automated workflows powered by AI can handle repetitive tasks like file conversions and metadata updates, freeing up time for more strategic work. Additionally, AI can identify duplicate or similar assets, eliminating redundancy and ensuring efficient asset management. By catering to the specific needs of each user type, AI empowers DAM systems to become hubs of creativity, collaboration, and productivity.
Collaboratively crafting AI prompts opens exciting possibilities for teams. By leveraging the diverse perspectives and expertise of team members, prompts can be refined and optimized to elicit more accurate, creative, and relevant responses from AI systems.
Gemini 1.5 Pro Explorations via Google AI Studio
We successfully submitted over 40 images to Gemini 1.5 Pro, and there were still plenty of tokens left out of the available 1 million. The processing time was also impressive.
In-Raster Concept
Select Multiple Images as Input
AI Assistant Output
Conclusion
By leveraging Gemini's advanced image recognition and expansive context window, Raster could empower marketing teams to streamline photo selection and campaign creation, ushering in a new era of AI-driven efficiency and creativity in digital asset management.