AI Studio

Use Data and AI services to build differentiators for Omnichannel Retail

Streamoid is dedicated to simplifying Omnichannel retail using AI. With a team of experienced data scientists, computer vision and NLP experts, we use unstructured data, normalise it and build AI models to solve Omni-channel retail problems.

Data Annotation

We accept feed from anywhere and automatically add the relevant attribute tags in the Retailers Taxonomy. All the data sets are cleaned, normalized and prepared for classifier training.

Build classifiers

Using retailer’s own data, domain experts use AI studio to train and build classifiers in the fastest way possible with zero lines of code.

Clear calls to actions

At each training session, AI studio analyses the failed images and gives clear insights and specific calls to action for the kind of data needed from you to improve the models.

Monitor with ease

Evaluating models and keeping them up to date is essential to building enterprise ready AI solutions. AI-Studio offers various ways to evaluate models and comes with embedded active-learning techniques.

AI Studio adds significant value in pre-processing for Fashion. With the pre-trained models available in AI studio, the focus is automatically directed towards the most crucial areas, leading to a boost in classifier accuracy while also decreasing the amount of data needed for training.


"AI Studio's layer of domain awareness automatically focuses the AI to learn from more relevant areas, increasing the accuracy of classifiers while reducing the data required for training. With a No Code environment, it is easy even for our stylists to train classifiers."

Read the case study here

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Data Centric Approach to Fashion ML

In the face of high failure rates of Machine Learning projects, the shift to the iterative, collaborative data-centric approach is imperative. With data-centric ML platforms with advanced capabilities like AI Studio, enable fashion users to easily adopt intelligent automation in their everyday functioning and drive real digital transformation while eliminating the risks associated with the model-centric approaches to ML.

How do self-learning systems work?

Algorithms are also necessary for the software to learn. As our expectations of modern computer systems rise, programmers will not anticipate all possible scenarios and prepare their machines accordingly. As a result, the program must make independent judgments and react correctly in unexpected scenarios. As technology advances, companies and consumers will gain more and more from self-learning systems.
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