Capabilities
Numerade combines the largest STEM video dataset ever assembled with deep expertise in learning science to advance three interconnected capability areas, each designed to push frontier AI closer to genuine, human-quality STEM instruction.
Frontier STEM Q&A
Expert-level question answering across 50+ STEM disciplines, from introductory coursework through graduate-level research. Our data captures the multi-step reasoning chains that domain experts use to solve complex problems.
PhD-verified accuracy
Every question-answer pair is created and verified by subject-matter experts, including professors, graduate researchers, and experienced educators.
Multi-step reasoning
Solutions don't just provide answers; they model the full chain of reasoning, including setup, intermediate steps, and conceptual justification.
Broad & deep coverage
From calculus and organic chemistry to quantum field theory and stochastic processes, our data spans the full spectrum of STEM difficulty.
Structured for training
Datasets are formatted with rich metadata (subject tags, difficulty levels, prerequisite mappings, and solution step boundaries) ready for fine-tuning and RLHF.
Multimodal Understanding & Generation
STEM learning is inherently visual. We provide the data and methodology to train models that can both interpret and create the visual language of STEM: diagrams, graphs, tables, equations, and step-by-step visual walkthroughs.
Visual aid generation
Training data derived from 5M+ educator-created videos showing how experts construct diagrams, graphs, and illustrations to explain concepts.
Graph & table comprehension
Paired examples of complex visual inputs (charts, data tables, circuit diagrams) with structured, expert-written interpretations and solutions.
Cross-modal reasoning
Rich text-to-visual and visual-to-text examples that teach models to fluidly translate between mathematical notation, written explanation, and visual representation.
STEM-native visual vocabulary
Unlike generic image datasets, our visual data is grounded in the specific notation and diagrammatic conventions of each STEM field.
Learning Science & Pedagogy
A model that can solve a problem is not the same as a model that can teach it. We apply insights from cognitive science and learning theory to build AI systems that scaffold understanding, adapt to the learner, and promote genuine comprehension.
Pedagogical scaffolding
Our educators structure explanations using techniques like worked examples, fading, and analogical reasoning. These patterns are captured directly in our training data.
Cognitive load awareness
Solutions are designed to manage information flow, breaking complex problems into digestible steps aligned with how students actually learn.
Adaptive depth
Multi-level explanations allow models to be trained on the same concept at different depths, enabling student-adaptive tutoring behavior.
Teaching, not just answering
The distinction between an answer and a lesson is central to our data philosophy. We optimize for student understanding, not just correctness.
Who this is for
Our capabilities serve teams building the next generation of intelligent STEM products and research.
AI Labs
Fine-tune foundation models on expert-verified STEM data to improve reasoning, visual comprehension, and instructional quality.
EdTech Platforms
Integrate pedagogically-aware AI capabilities into learning products, from tutoring and homework help to adaptive courseware.
Research Institutions
Access large-scale, structured STEM datasets for research in multimodal reasoning, cognitive science, and AI-assisted education.
Built to compound
These capabilities are not isolated. They reinforce each other. Multimodal reasoning improves when grounded in pedagogical structure. STEM Q&A accuracy increases with richer visual context. And learning science research continuously feeds back into how we build and curate data. The result is an AI training ecosystem purpose-built for education.