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Building the AI Data Platform for Animal Health

Seismi's IoT devices are the first layer of a larger vision: collecting the world's most comprehensive proprietary dataset of animal health, from which we will train AI models that predict disease, forecast outcomes, and ultimately produce digital twins of individual animals tracked across their entire lives.

How It Works

The Data Flywheel

Each sensor we deploy adds to a growing, proprietary health dataset — which trains better AI models — which delivers more value to operators — which drives broader sensor deployment.

1

Collect

IoT sensors continuously capture heart rate, respiratory rate, temperature, and movement from animals in the field

2

Aggregate

Data is organized into longitudinal, individual-animal health records — linked across life stages and operations

3

Train

AI models are developed on the growing proprietary dataset, validated against gold-standard clinical instruments in field trials

4

Predict

Models generate health insights, early disease alerts, and outcome forecasts — informing treatment and management decisions in real time

5

Validate

Predictions are measured against real outcomes, closing the loop and continuously improving model accuracy over time

The Vision

A Digital Twin Across the Animal's Entire Life

We are building toward a continuous digital record that follows an individual animal from birth through processing — tracking health, predicting risk, and informing decisions at every stage.

Stage 1

Cow / Calf

Early health monitoring of calves and breeding cows establishes the individual animal's baseline health profile — the foundation of the digital twin.

  • Calf health at birth
  • Maternal bonding patterns
  • Early disease indicators
  • Growth rate correlation

Stage 2

Stocker

Transition period health tracking. Prior health history informs risk stratification as cattle move to new environments and commingled populations.

  • Stress response at turnout
  • BRD risk prediction
  • Pasture performance
  • Commingling health impact

Stage 3

Feedyard

Intensive health monitoring at scale. AI models flag at-risk animals before visible symptoms appear, reducing mortality, treatment costs, and days on feed.

  • Continuous vital signs
  • Early BRD detection
  • Feed conversion correlation
  • Pen-level health trends

Stage 4

Processing

Harvest data closes the loop — correlating in-life health signals with carcass grade and yield to validate predictive models and refine future accuracy.

  • Carcass yield correlation
  • Quality grade prediction
  • Lung pathology matching
  • Model accuracy validation

Research Methodology

Designed for Scientific Rigor

Building trustworthy AI models requires more than data volume — it demands data quality and ground truth validation. Seismi's farm trial methodology is designed to produce rigorously annotated datasets across multiple simultaneous data streams, captured under controlled field conditions.

Our trials pair Seismi sensors with clinical reference instruments in real-world settings, generating the labeled data necessary to validate sensor accuracy and train AI models with confidence before broad deployment.

Correlating in-life health signals with real-world outcomes — including harvest data — closes the validation loop and creates a continuous improvement cycle for model accuracy over time.

Multi-Modal Data Capture

Trials capture multiple simultaneous data streams in a controlled field setting, enabling precise correlation between Seismi sensor readings and clinical reference measurements.

Clinical Reference Validation

Seismi sensors are validated against established clinical standards, ensuring measurement accuracy before AI model training begins.

Annotated Ground Truth

Structured annotation of health events during trials provides the labeled datasets that allow AI models to learn the physiological signatures that precede visible disease.

Closed-Loop Outcome Validation

In-life health signals are correlated with downstream outcomes to measure predictive accuracy and drive continuous model improvement.

Research Partners

Validated by Leading Institutions

Kansas State University

College of Veterinary Medicine — STTR Research Partner

KSU's College of Veterinary Medicine is conducting clinical trials for the Pet Mat and Cattle Sensor, providing the scientific expertise and institutional rigor to validate Seismi's sensor accuracy and inform our AI model development roadmap.

Research & Publications

Scientific Evidence

As our field trials progress, we will publish findings through industry case studies, technical white papers, and peer-reviewed scientific journals.

Case Studies

Real-world deployment results from commercial feedyards and veterinary clinics

Coming soon — check back as our field trials progress

White Papers

Technical documentation on methodology, sensor validation, and AI model architecture

Coming soon — check back as our field trials progress

Peer-Reviewed Publications

Scientific findings submitted to veterinary and animal science journals

Coming soon — check back as our field trials progress