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AI & Big Data Services and Solutions

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Recognization Systems

Include image, sound, text and behavior recognization

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recommendation
Recommendation Systems

Include product, service, person and place recommendation

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detection system
Pattern Detection Systems

Include but not limit to fault and risk pattern such as anti-money laundering and financial risk predicting 

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Industry-Standard Technologies

Deliver your ideas into impactful and scalable products.

Tesseract always applies proven and production-grade technologies and frameworks to build AI-based systems

How we Help you Succeed

Build AI Models & Software Systems

There are always two separate stages involved in an AI system: 1. Build AI models based on the training & test data 2. Deploy them to a scalable software system for production ready

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Data Collection

Data preparation is one of the most important aspects of creating a useful AI/ML project. Various forms of data: image, sound, text, statistics data

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Data Labeling

This may include but is not limited to tagging a picture, identifying a name in a sentence, or summarizing the sentiment in a body of text.

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Feature Extraction

Based on the categories within your dataset, you can choose which features are best suited to train your model

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Model Building

After building a feature set using neural network architecture (deep learning), you can train and validate your model

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Model Deployment

Getting models into production can be difficult and challenging. After a model is trained, it will be deploy to an AI infrastructure such as AWS or GCP.

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Model Monitoring

Once a model has made it into production, it must be monitored in order to ensure that everything is working properly