Everfur

Everfur

AI transparency disclosure

Last updated: May 7, 2026

1. Overview

Strand Health Inc., doing business as Everfur ("Company," "we," "us," or "our"), is committed to transparency regarding our use of artificial intelligence ("AI") and machine learning ("ML") technologies. This AI Transparency Disclosure ("Disclosure") describes how AI and ML are used within the Everfur Health Intelligence application and the Everfur Clinical Intelligence platform (collectively, the "Services"), including the types of data processed, the nature and limitations of AI-generated outputs, and your rights and choices.

2. AI Technologies Used

Our Services employ multiple AI and ML technologies, including but not limited to:

  • Natural Language Processing (NLP): Used to interpret free-text symptom descriptions, clinical notes, and user queries, and to generate human-readable health assessments and clinical recommendations.
  • Computer Vision: Used to analyze photographs of pet skin conditions, eye conditions, and other visual inputs for screening-level health assessments.
  • Audio Analysis: Used to analyze recordings of respiratory sounds (such as coughs) to provide preliminary assessments of potential respiratory conditions.
  • Knowledge Graph Reasoning: Used to traverse our proprietary veterinary knowledge graph, which encodes relationships between diseases, symptoms, medications, breeds, and other clinical entities derived from peer-reviewed veterinary literature.
  • Drug Interaction and Dosing Models: Used to calculate species-specific and weight-adjusted medication dosing and to identify potential multi-drug interactions and contraindications.
  • Differential Diagnosis Ranking: Used to generate ranked lists of potential diagnoses based on presented symptoms, patient characteristics, and clinical context.

3. Training Data Sources

Our AI models are trained on the following categories of data:

  • Peer-Reviewed Veterinary Literature: Over 50,000 publications from 47 veterinary journals, accessed under license or through text and data mining agreements with publishers.
  • Structured Clinical Reference Data: Including but not limited to drug formularies, pharmacokinetic databases, breed-specific disease prevalence data, and clinical practice guidelines from recognized veterinary specialty organizations.
  • Proprietary Knowledge Graph: A structured database encoding clinical relationships, disease ontologies, drug properties, and evidence hierarchies curated by our scientific team.

We do not use user-submitted content (including pet photographs, clinical notes, symptom descriptions, or chat interactions) to train our AI models unless the user has provided explicit, informed, opt-in consent through a clearly presented mechanism within the Services. If such consent is provided, it may be withdrawn at any time through account settings, and previously submitted data will be excluded from future training cycles.

4. How AI Outputs Are Generated

4.1 Input Processing

When you use an AI-powered feature, your input (text, image, or audio) is transmitted to our secure servers via encrypted connection (TLS 1.2 or higher). The input is processed by one or more of our AI models to generate the relevant output.

4.2 Output Generation

AI outputs are generated through computational analysis of your input against our training data and knowledge graph. Outputs may include health assessments, differential diagnoses with confidence scores, drug dosing recommendations, interaction alerts, and clinical decision support suggestions.

4.3 Confidence Scores

Where applicable, AI outputs include confidence scores or probability rankings. These scores represent the statistical likelihood of a particular result based on the model's training data and the specific inputs provided. Confidence scores are NOT diagnostic certainty ratings. A high confidence score does not guarantee that a particular condition is present, and a low confidence score does not guarantee that a condition is absent.

5. Limitations and Risks

You should be aware of the following limitations of our AI systems:

  • Accuracy: AI models are not infallible. Outputs may contain errors, inaccuracies, or omissions. The accuracy of any output depends on the quality, completeness, and representativeness of the input data provided and the training data used.
  • Bias: AI models may reflect biases present in the training data, including but not limited to over-representation of certain breeds, species, or clinical presentations, and under-representation of rare conditions or atypical presentations.
  • Currency: AI models reflect the state of veterinary knowledge at the time of training. New research, drug approvals, safety alerts, or clinical guidelines published after the model's last update may not be reflected in AI outputs.
  • Context Limitations: AI models process only the information explicitly provided by the user. They cannot perform physical examinations, order laboratory tests, or access historical medical records unless such records are provided.
  • Edge Cases: AI models may perform less reliably on unusual, atypical, or complex cases that are poorly represented in the training data.

6. Human Oversight

For consumer users (Everfur Health Intelligence): AI outputs are informational and should be shared with your licensed veterinarian for professional interpretation and clinical decision-making.

For professional users (Everfur Clinical Intelligence): AI outputs are clinical decision support tools. All outputs should be reviewed and validated by a licensed veterinary professional before being applied to patient care. The treating veterinarian retains full responsibility for all clinical decisions.

7. Automated Decision-Making

Our Services do not make autonomous decisions that produce legal effects or similarly significant effects concerning any individual. All AI outputs are advisory in nature and require human review before any action is taken. We do not use AI to make decisions about insurance eligibility, employment, credit, housing, or any other domain that could produce legal effects concerning individuals.

To the extent that any applicable law grants you rights with respect to automated decision-making technology (including the California Consumer Privacy Act, as amended), you may exercise those rights by contacting us at privacy@everfur.com.

8. Data Processing and Privacy

For detailed information about how we collect, use, and protect your data, including data processed by our AI systems, please refer to our Privacy Policy at everfur.com/privacy.

9. Your Rights and Choices

You have the following rights and choices with respect to AI processing of your data:

  • Opt-Out of Model Training: You may opt out of having your submitted content used for AI model training at any time through your account settings.
  • Access and Deletion: You may request access to or deletion of data processed by our AI systems by contacting us at privacy@everfur.com.
  • Explanation: You may request a general explanation of how our AI systems generate outputs relevant to you by contacting us at privacy@everfur.com.

10. Updates to This Disclosure

We may update this Disclosure from time to time to reflect changes in our AI technologies or practices. Material changes will be communicated through the Services or via email. The effective date at the top of this Disclosure indicates when it was last updated.

11. Contact Information

Strand Health Inc.
d/b/a Everfur
1002 Dean Street, Suite 101, Brooklyn, NY 11238
Email: privacy@everfur.com