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Graft is an artificial intelligence (AI) platform company that enables companies lacking machine learning (ML) engineers and AI infrastructure to build AI-enabled tools through a no-code interface. The company first bootstraps the AI infrastructure that large organizations have developed by using a large foundation model to train smaller, more specific models. Then, they package and provide that infrastructure as a service, bridging the resource and expertise gaps characterizing many smaller companies under enormous pressure to incorporate AI tools into their products and workflows.
ML engineers and hardware are in limited supply and command a premium. While foundation models are becoming commoditized, building and implementing the applications that use those models still requires technical expertise.
After earning his PhD from Stanford, Adam Oliner built the ML organization at Splunk, later becoming head of machine learning at Slack. During his tenure at Slack, Oliner observed that all large companies were converging around the same approach to AI—using large, pre-trained models to train downstream models. This experience motivated him to found Graft.
As foundation models become commoditized, those who create an effective platform and user interface for transforming those models into working products should capture more value. Equivalent products from cloud service providers (CSPs) like Amazon still require engineering knowledge to implement and stitch together many disparate services. Graft believe that the technical expertise of their founding team will enable the company to move faster than potential competitors.
While cloud service providers like AWS (Bedrock) and GCP (Vertex AI) offer a variety of services for training and deploying AI models, they place the onus on enterprises to stitch together disparate solutions to create an end-to-end solution on their clouds, which results in increased complexity and the need for technical expertise. Graft enables its customers to build and deploy AI-powered apps with no engineering team, no infrastructure, and no vast quantity of labeled data. Currently able to support data queries, semantic search apps, and summarization apps, the company plans to add predictive and visualization apps to its platform.
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