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Machine learning platform to scale pet health monitoring.
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Imagine you take a picture of your pet to diagnose their health, and instantly know what to do. This is the DIG Labs promise. We built a visual AI engine, trained on hundreds of thousands of images - expert reviewed - that can analyze pet issues in real-time across dozens of areas of concern like ears, nose, teeth, and more. But it doesn’t stop there - we use these insights to create personalized plans for pets like diet transition, weight loss programs, and more.
19+
AI models
110k+
Images used in training
1,2k+
Submission every week
About DIG Labs
DIG Labs supercharges pet care, nutrition, lab diagnostics, and public health companies with visual insights that go beyond the surface. Their technology enhances product and customer experiences, setting a new standard in the industry. With over 20,000 analyses and counting, the company is committed to revolutionizing pet health. As the team continues to grow, significant strides are made in helping thousands of dogs achieve better health each month.
DIG Labs is dedicated to creating better solutions for pet care – less guesswork and more answers, resulting in happier, healthier dogs. Whether it's pet nutrition, diagnostics, or collaborating with leading veterinarians, their focus is on pioneering pet health through data excellence.
Problem
The challenge was to design a scalable machine learning platform capable of handling complex, proprietary datasets across multiple pet types, health indicators, and classification systems, while also supporting a state-of-the-art infrastructure that could process tens of thousands of daily requests and seamlessly integrate with a multi-tenant environment where each client might require custom workflows or even its own proprietary model.
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Our process
To meet business needs, we need to connect the Machine Learning pipeline to a multi-tenancy product that could scale to the custom needs of enterprise clients easily. The product is divided in three components:
1. ML Pipeline
Run, monitor, and process images to get instant health reports on your pets.
2. Multi-tenancy platform
Onboard new clients, allow us build custom feature sets, and for clients to interface with.
3. API-first product
For innovators in the Health, Pet, and Vet industry to build on top of the ML stack.
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“Island’s capability to source and retain top talent is unmatched. I have full confidence that the team will continue to deliver and scale to our growing needs.”
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CEO at DIG Labs
Machine Learning pipeline
We see the Machine Learning pipeline as its own application, separated from the multi-tenancy platform. By design, this enables the teams to move faster, isolate problems, and allow for greater f lexibility in development.
The end to end workflow from data capture to pre-processing, model selection, tuning, to results, needed to operate in real-time, ensure transparency in the system - allow us to intervene when we needed to, while incorporating human feedback to improve model performance over time.
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Building a client interface
The platform serves a broad range of users, from pet owners to veterinarians, each with distinct access and data needs. A multi-tenant architecture enables separated data flows and customized interactions per user group. The iOS and web applications are the main interfaces, connecting through an API Gateway that ensures secure, efficient request handling and a smooth user experience.
Data engineering
Our data engineering layer consolidates multiple data sources into a scalable, unified framework. AWS Athena supports efficient querying of large datasets, PostgreSQL manages metadata, and Amazon S3 provides secure image storage. Together, these components form a reliable data foundation for machine learning.
Model registry and expert reviews
All trained models are registered and evaluated through A B testing to validate performance. Domain experts review each model for accuracy and reliability, supported by model cards that document metrics and intended use. Only approved models move forward to deployment.
SageMaker pipeline
SageMaker Pipelines orchestrate the full ML lifecycle and enable continuous training as new data becomes available. Processing Jobs handle data preparation, while Training Jobs manage model training and hyperparameter optimization, ensuring consistent and high quality model performance.
Machine learning workflow
Using SageMaker Studio, we built an end to end ML workflow covering data preprocessing, model training, and hyperparameter tuning through scalable pipelines. This approach accelerates development and supports efficient customization across tenants.
The modular design allows rapid adaptation to new pet health use cases, reducing the time to scale tenant specific models by up to five times and improving overall predictive performance.
Model deployment
Models are deployed through a RESTful API that integrates seamlessly with client applications. This enables real time pet health insights from iOS and web inputs, delivering fast and reliable results to users.
Multi-tenancy platform
This platform consumes the Machine Learning APIs at scale to build real business use cases. We are essentially the first consumers of our own AI product. Tenants also known as Clients, identified with unique identifier would have a custom version of the application to meet their needs - data requirements, model configuration settings, unique consumer workflows, etc.
A multi-tenancy architecture helped us secure each environment for enterprise scale. In addition, we built a turnkey app that any customer can whitelabel, deployed within seconds.
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Automation of tenants
Tenant onboarding was fully automated, reducing setup time from over 12 hours to under 10 minutes. Configuration, branding, and deployment to GitHub and AWS are handled instantly through a simple Retool app.
Data engineering
The platform delivers ML insights across the ecosystem through integrations with third party tools, including practice management and communication platforms such as Shepherd, Segment, Airtable, and Gladly.
API-first product
DIG Labs offers multiple products and solutions to Clients. The element of customizability, personalization, and control for enterprise use cases, required us to build a product that is extremely adapatable to current known use cases but also the future.
Exposing our APIs to clients to build on top fulfilled the promise of an open-architecture system. Enterprises across Pet, Vet, and Heath industries built on top of our RESTful API to create custom experiences inside their own applications. This opens the door for dozens and sometimes hundreds of use cases.
Our open API stack
Swagger and Postman for API documentation. Automated documentation with YAML files for easy maintenance.
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AWS API Gateway to manage credentials, rate limits. It also allows you to offer an SDK in almost any language.
Loom for technical demos and walkthroughs.
Results
Our platform enabled DIG Labs and its clients to achieve highly accurate pet image analysis through a modular ML pipeline and strong feedback loops. Automated multi tenant onboarding and flexible white label or custom configurations allowed rapid client customization. An open, API first architecture supported fast iteration, easy integration, and scalable adoption across diverse use cases.
>96%
AI model accuracy in first 2 months
+100,000
Expert-labelled images used for training
+3,000
Image submissions processed per week
10X
Human speed with image analysis
>19
Machine Learning models deployed
Want to build something?
Let’s talk about what you’re working on next and see how we can help.
No pitches, no hard sell. Just a real conversation.
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You’re all set!
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