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One Platform.

Limitless AI capabilities.

The ANAI platform enables the accelerated delivery of your ML models to production in weeks versus years. Our solutions break down barriers for AI adoption, solve complex business problems, and accelerate the ideation to implementation cycle.

 

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Add Data. Train Models. Deploy to Production.

ANAI is built to significantly decrease the time it takes for building, testing and deploying ML models allowing businesses to focus more on business and customers, than complexities of model building.

150+

Data Connectors

ANAI engine’s data ingestion pipeline has 150+ data connectors, and if you can’t find one, let us know and we will work with you.

 

A To Z

of AI & ML

We help you focus on taking care of your business while we meet all your AI and ML needs, from data engineering and autoML to RAI and XAI.

Weeks & Months

vs. Quarters and Years

From Data Engineering to model training & tuning, to deployment and monitoring in approx. 16 weeks*, helping us in Democratizing AI for everyone.

 

450+

ML Models

ANAI comes with 450+ unique AI and ML models, and more are constantly being added to offer speed, focus, and business value, as well as nimbleness and scalability.

Employ an All-In-One AI Platform

Our Process

Our six Step Approach

01

Problem Definition & Hypothesis Generation

Defining the problem and
generating a hypothesis before
diving deeper with the machine
learning is an important
requirement for success of all types
of ML project.

02

Feature Transformation

After knowing what you want, the
next step is sourcing the data that
will be required for solving your
predefined problem.

03

Model Generation

Selecting the right data and correct
the features will help building ML
models that outperforms everything
else. Building the right ML model can
make a difference between success
and failure of the ML project as each
model has its own uniqueness.

04

Model Validation & Tuning

After the model has been built and
trained, it needs to show prominence
on the data that it hasn’t seen before.

 

05

Deployment & Monitoring

After selecting the best of the best
machine learning model, it needs to
be deployed and managed. ANAI’s
MLOps helps you in monitoring.

 

06

Explanations

Along with building the best ML
models which performs accurately in
any situation, it should also be a
priority or an obligation of the ML
engineers to make the model
explainable and responsible to its
users.

 

Be a part of our community

To contribute for Democratizing AI!

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our platform

Platform Core Features

Data Engineering Pipeline

Provides an end-to-end journey for your data to get cleaned, transformed, and standardized into usable formats for the ML pipeline.

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Machine Learning Pipeline

Generate insights on your data, build/train ML models based on your use case, and get the most accurate models through hyper-tuning.

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MLOps

Continuously build and test your ML models even after deployment to keep the model accurate and functional towards its objective.

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Responsible And Explainable AI

Generate trust in your AI systems by understanding the outcomes of your ML model and ensuring that the model is responsible, fair, and accountable.

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Interested in deploying computer vision in your workplace?

Take a look at Apture, our CV solution for multiple CV use cases across industries.

Visit Apture.ai >>

Data Ingestion and Pipelines

Feature Engineering

Data Wrangling

Responsible & Explainable AI

Build your own Models

Monitor and Manage

Want to get started?

Connect with us to get a free demo

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