Act A - The Market Structure
Building academic AI models requires 'ground truth'—datasets rigorously labeled by humans. When the data is highly complex (cell morphology, structural engineering faults), crowdsourcing platforms fail entirely because the crowd doesn't understand the nuance. Researchers are forced to either do it themselves (wasting hundreds of PI hours) or hire generic labor and suffer ruined datasets. The ideal labor pool exists in the university ecosystem itself, but there is no mechanism to deploy them.
Act B - The Story
Dr. Zhao has a grant to train a neural net to detect micro-tumors. He needs 10,000 slides annotated manually. He tried outsourcing to an international click-farm, and the resulting data was mostly noise.
Ahmed, a high-performing pre-med student across the country, needs flexible income that doesn't conflict with his intense lab schedule. He already knows how to spot tissue irregularities.
Dr. Zhao uploads his dataset to the secure academic platform. The matching engine filters for students who have completed 300-level anatomy courses across Canadian universities. Ahmed is matched, completes his TCPS 2 privacy module, and begins picking up micro-tasks between classes. Dr. Zhao gets perfectly annotated academic-grade data; Ahmed earns a vital stipend utilizing his actual educational skills.
Act C - Why This Market Stays Broken Without Infrastructure
The university environment is naturally fragmented. An engineering PI has zero visibility into the biology department's student pool at another school. DeeperPoint organizes the national student base into a dynamic, highly specialized task-force, accelerating AI development while directly financially supporting the student base.
Characters are fictional. The AI annotation bottleneck is real. DeeperPoint is building the infrastructure this story describes.