Coactive.Science

aBOUT US

Building Verticalized AI
with Satellite
and Video Data using Active Learning

Active Learning

The Human + AI Advantage

Unmatched Precision and Adaptability

Our AI outperforms traditional methods with insights driven by precision and innovation.

Continually Improving

Through active learning and expert feedback, our models adapt seamlessly to evolving challenges and real-world complexities.

Easy AI Integration

Seamlessly integrate your custom LLMs and multimodal AI models using our Model Context Protocols (MCP), ensuring accuracy, adaptability, and effortless domain integration across industries.

Platform Features

How do we enable out partners?

Optimizing Dataset Building

Selective Sampling: The platform identifies the most informative or uncertain data points to collect.
- Example: Instead of annotating thousands of aerial images across an area, the AI requests annotations for those images for which it has highest uncertainty, maximizing learning impact.
Cost Efficiency: Reduces data gathering costs by focusing on critical data points, minimizing the need for exhaustive annotation.

Improving Model Accuracy

Refining Edge Cases: Active learning focuses on challenging or ambiguous inputs that can significantly improve model performance once resolved.

Example: To obtain greater predictive accuracy for mineral exploration, the algorithm guides the collection of ground samples to achieve higher accuracy, faster and cheaper.

Accelerating AI Development

Early Model Training: Active learning allows AI models to achieve higher accuracy with smaller datasets, enabling faster initial deployment.
Iterative Improvement: The platform ensures continuous learning, keeping AI models updated and reducing obsolescence.

Enhanced Collaboration

Expert-In-The-Loop: The platform facilitates collaboration between domain experts and AI systems, ensuring high-quality annotations and real-world relevance.

Example: Validating resource exploration data with geologists.

Scaling Across Applications

Cross-Industry Use: The models can be deployed using transfer learning across domains such as healthcare, retail, finance, and more to tackle specific challenges.

Adapting to Dynamic Environments

Continuous Feedback: Active learning platforms integrate real-time feedback from users or experts, enabling the model to stay relevant in rapidly changing domains.

Example: In e-commerce, an AI recommending products can adapt to shifting trends by querying data on emerging preferences.

Our Startups

Driving Innovation Across Boundaries

We don’t just invest in AI—we build it from the ground up, partnering with visionary founders, researchers, and engineers to bring cutting-edge solutions to market. Our startups are reshaping industries such as agriculture, environmental monitoring, mining, urban planning, and logistics by transforming raw data into actionable intelligence.

Join us as we push the boundaries of AI innovation.