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ANAI lets you automate every component of a ML development lifecycle from data engineering to AI model deployment, essentially democratizing AI for everyone.
An Integrated Platform Product ecosystem with a purpose of ‘ Democratizing AI”. From Data Ingestion through 100 plus Data Connectors, 300 plus unique AI & ML Models to Responsible and Explainable AI, we got all. We leverage AI to Democratize AI, and ML Models to simplify Machine Learning implementation, making it simpler and faster, while ensuring expert levels of accuracy, feature, and transparency.
Platform Product that accelerates your AI & ML journey, enabling you to build ML Models for diverse industries in Days/Weeks, letting all users, across operations and industries benefit from AI.
Manage A to Z of a data science workflow, from Data Engineering and Analysis to building a model and getting explanations on its output under a single platform.
Easily handle data and build ML models without the expertise needed by using a no code framework built into the UI and getting things done within a few clicks.
Integrate data from over a hundred data connectors, generate insights on it after pre-processing and select the ML model that fits best with your use case, allowing you the flexibility that you need while building such projects.
Get the best accuracy possible on your ML models, as the models iterate through hundreds of hyperparameters to find the best ones which are then used during the testing phase resulting in you getting the most accurate models.
Accelerate your ML workflow by constantly reiterating and getting the best model in the fastest way possible using ANAI's simpler but efficient framework.
Get explanations on the predictions given by a model and find any biases within the data set to eliminate them right away before deploying the model in the real world, increasing trust and reliability of your product.
Proportionately scale your ML models and train them as required by the new data having different parameters or feature values.
Create the necessary impact and generate value through your ML projects by reducing the time spent on experimentation and focusing more on the actual usefulness of your projects.
Manage A to Z of a data science workflow, from Data Engineering and Analysis to building a model and getting explanations on its output under a single platform.
Easily handle data and build ML models without the expertise needed by using a no code framework built into the UI and getting things done within a few clicks.
Integrate data from over a hundred data connectors, generate insights on it after pre-processing and select the ML model that fits best with your use case, allowing you the flexibility that you need while building such projects.
Get the best accuracy possible on your ML models, as the models iterate through hundreds of hyperparameters to find the best ones which are then used during the testing phase resulting in you getting the most accurate models.
Accelerate your ML workflow by constantly reiterating and getting the best model in the fastest way possible using ANAI's simpler but efficient framework.
Get explanations on the predictions given by a model and find any biases within the data set to eliminate them right away before deploying the model in the real world, increasing trust and reliability of your product.
Proportionately scale your ML models and train them as required by the new data having different parameters or feature values.
Create the necessary impact and generate value through your ML projects by reducing the time spent on experimentation and focusing more on the actual usefulness of your projects.
ANAI offers an end-to-end automated data science pipeline, from Data Engineering to MLOps and EDA to Explainable AI that enables you to build AI projects with ease but without compromising on quality.
Provides an end-to-end journey for your data to get cleaned, transformed, and standardized into usable formats for the ML pipeline.
Generate insights on your data, build/train ML models based on your use case, and get the most accurate models through hyper-tuning.
Continuously build and test your ML models even after deployment to keep the model accurate and functional towards its objective.
Generate trust in your AI systems by understanding the outcomes of your ML model and ensuring that the model is responsible.
Automated page speed optimizations for fast site performance