CoreFrame Labs | AI Agricultural Robotics

About CoreFrame Labs

Robotics & AI innovation — building practical automation for real-world industries.

CoreFrame Labs is a robotics and artificial intelligence company focused on developing compact, reliable automation systems. We design intelligent machines powered by computer vision, edge AI, and real-time robotics engineering.

Our first flagship project is an agricultural robot capable of identifying ripe strawberries in real time. Using a dataset of ~5000 labeled images (expanding dataset), our vision model currently achieves ~88% classification accuracy distinguishing ripe vs. unripe fruit. This prototype serves as a foundation for larger multi-industry automation systems.

Our goal is to validate this concept, demonstrate real-world capability, and expand CoreFrame’s robotics platform into additional industries that benefit from intelligent automation.

✅ Edge-optimized AI vision using YOLO architecture
✅ Early robotic arm concept for gentle, low-damage fruit picking
✅ Developing an integrated AI-robotics pipeline for future field deployment
✅ Roadmap toward autonomous robotics across multiple industries

Technology Stack

Built on modern AI, robotics, and edge compute.

Computer Vision

Ripe vs. unripe strawberry detection using YOLO-based computer vision models trained on custom agricultural datasets. Optimized for real-time inference in field environments.

YOLO-based object detection for fruit ripeness classification

Robotics

Modular robotic grabber concept designed for gentle, low-damage fruit handling. Built to adapt to varying field layouts, plant spacing, and mounting configurations.

Soft-contact grabber tool for delicate fruit handling

Edge Hardware

Targeted deployment on edge AI hardware platforms with integrated cameras and sensors, enabling low-latency autonomous operation without cloud dependency.

Jetson-class edge AI deployment target

Real-Time AI Crop Detection System

Field-tested AI vision system validated under real outdoor agricultural conditions, including natural lighting variation, dense canopy occlusion, and wind-driven plant movement.

Current Results

• ~5000 training images
• ~88% classification accuracy
• Real-time field detection pipeline operational
• ~230 ms average inference latency on consumer-grade hardware

Tested using real farm-collected data (Texas-based field conditions)

This system enables automated harvesting decisions by identifying which strawberries are ready to pick in real time. Our next phase focuses on integrating this model with robotic harvesting hardware for full end-to-end automation.

Perception-to-Targeting Validation

Early-stage perception-to-targeting MVP demonstrating conversion of AI-generated strawberry detections into simulated robotic targeting coordinates under real outdoor agricultural conditions.

Current MVP Capabilities

Simulated robotic targeting path generation
Real-time coordinate extraction from AI detections
Multi-target detection under outdoor field conditions
Early perception-to-action feasibility validation
~190–235 ms observed inference latency during testing

This prototype demonstrates an early-stage perception-to-targeting pipeline designed to bridge AI-generated crop detections with future robotic harvesting systems. The current MVP focuses on validating targeting logic, coordinate estimation, and simulated robotic movement planning under real agricultural conditions.

2D targeting simulation only not physical robotic actuation

Robotic Harvesting System Concepts

Early-stage robotic harvesting concepts connecting perception, motion, and gentle fruit handling into a focused Phase I development architecture.

CoreFrame Labs is developing a Phase I robotic harvesting architecture designed to connect AI-powered strawberry detection with lightweight robotic movement and gentle fruit handling.

The SCAAR arm concepts shown below represent early-stage engineering direction for feasibility validation, prototype planning, and perception-to-action integration. These visuals are conceptual design studies, not final production hardware.

Key Focus Areas

Compact 4-DOF harvesting arm architecture
Short-range fruit targeting and transfer
Soft-contact end-effector design
Lightweight modular construction
Field-focused prototype iteration

Conceptual blueprint rendering of the SCAAR Phase I robotic harvesting arm from the side.

Conceptual rear isometric blueprint rendering of the SCAAR Phase I robotic harvesting arm.

Conceptual close-up blueprint detail of the SCAAR Phase I robotic arm wrist and end-effector.

SCAAR Phase I Arm — Side Architecture View

Research & Industry Analysis

Industry research supporting CoreFrame Labs' work in agricultural automation and AI-powered harvesting systems.

Market Research

Market Research on Strawberry Harvesting and Automation

Strawberry farming is one of the most labor-intensive sectors in agriculture, with harvesting requiring up to 3,000 labor hours per acre. The crop must be picked repeatedly by hand throughout the season and relies on large seasonal harvest crews. This research examines production scale, labor demands, and the growing opportunity for AI-assisted automation in specialty crop agriculture.

Sources: USDA NASS, USDA ERS, UC Davis, University of Florida IFAS, Texas A&M AgriLife

Economic Analysis of Strawberry Harvesting and AI-Assisted Automation

Harvest and postharvest operations can represent up to 70% of total strawberry production costs in major U.S. growing regions, with per-acre harvest costs exceeding $79,000 in some California production systems. This research evaluates the economic structure of harvesting, cost drivers for growers, and how AI-assisted systems could improve efficiency and reduce labor costs.

Sources: USDA NASS, USDA ERS, UC Davis, UF IFAS, Texas A&M AgriLife, U.S. Department of Labor

Meet CoreFrame Labs

Leadership driving CoreFrame Labs' AI and robotics mission from strategy to deployment.

Dakotah Singh

Co-Founder & Chief Technology Officer

Dakotah leads the design and deployment of end-to-end AI and robotics systems at CoreFrame Labs. He specializes in applied machine learning, computer vision, and real-time inference, focusing on building production-oriented systems designed for real-world deployment.

He architected and developed CoreFrame Labs’ computer vision models from the ground up, achieving ~88% accuracy in agricultural environments. His work emphasizes continuous model iteration, dataset engineering, and performance optimization to improve reliability in real-world conditions.

Dakotah designs scalable ML pipelines and data processing workflows, overseeing the full AI lifecycle from data collection and labeling to training, optimization, and deployment. He also leads the integration of AI systems with robotic hardware, enabling real-time perception for field-ready automation.

Jessica Singh

Co-Founder & Chief Executive Officer

Jessica brings over 25 years of experience in business leadership, including 15 years in the technology sector, with a strong background in operations, finance, and strategic execution. As CEO of CoreFrame Labs, she leads the company’s strategic direction, partnerships, and financial oversight, ensuring the successful transition of advanced AI and robotics technologies into real-world applications.

She has a proven track record of managing and scaling business and technology-driven operations, with a focus on execution, sustainability, and long-term growth. At CoreFrame Labs, she is responsible for building industry relationships, securing strategic opportunities, and aligning the company’s AI and robotics innovation with real market demand.

Supported By

CoreFrame Labs participates in startup programs supporting the development of advanced AI and robotics systems.

AWS Activate

Amazon Web Services startup program supporting early-stage startups.

Microsoft for Startups - Azure

Microsoft startup program supporting AI and robotics development.

NVIDIA Inception

NVIDIA startup program supporting companies building AI and accelerated computing technologies.

Google for Startups Cloud Program

Google Cloud startup program supporting CoreFrame Labs with infrastructure, tooling, and credits for AI and robotics development.

Contact & Partnerships

Interested in pilots, grants, or joint development?

AI & Robotics for Real-World Automation