Revolutionizing Customer Care

Empower your business with Euler AI's workflow tools, designed to help you experiment, deploy, and scale seamlessly. Our AI-driven chatbots autonomously manage customer support, enhance user experience, and streamline operations.


Learn How
hero dashboard with experiment and deployment graphs
1

Experiment

2

Deploy

3

Scale



1



Experiment

Power up your existing support workflow with versioning, setup, and reproducibility.

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Setup up your environments in seconds

Use one of our preconfigured environments, or bring your own. Setup using a single command:
$ euler environment setup

  • Use any number of files to define your environment:
    requirements.txt, Dockerfile, .bashrc, and more
  • Instantly spin up your favorite workspace with
    $ euler notebook

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View your experiments in one place

Compare metrics, model configurations, and task runs directly in your terminal with:
$ euler ls

  • Easily reproduce any of your previous experiments with the
    $ euler rerun command
  • Local dashboard for easy searching, filtering,
    and sorting of experiment logs
2

Deploy

Turn your local models into dynamic cloud services and endpoints. Any cloud, any machine learning framework.

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Easy deployments for new chat algorithms and experiments

Don't waste your data scientists' time writing ops code. Go from local script to deployed service with:
$ euler deploy


  • Flexible and high performance serving system for
    machine learning models in production as APIs
  • Perform A/B testing over multiple algorithms
  • Dashboard to monitor and manage all services
3

Scale

Reliable scaling of infrastructure services for inference.

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Scale out inference code on any framework or cloud platform

Cloud infrastructure prices fluctuate constantly. Euler enables you to seamlessly deploy cross-cloud for the lowest possible rate.


update your model versions

Autoscale based on latency and
throughput needs

Euler ensures you're only ever paying for the resources you need. Set automated triggers and thresholds specific to your business use case.

collaborate on model versions with peers

Setup and monitor automated workflows

Your organization gets new data daily, begin leveraging that with automated retraining and model builds.