We make the latest AI easily available

Long term goal is to make a general AI. We specialize in the tech that we need as building blocks for the general AI and and also are useful for our customers now.

Start saving your data. The more you have, the better the AI will be. Let us see what you can do with it. Book a free consultation

We specialize in cloud deployments and software as a service (SaaS) where you pay for what you use. We also take a care of your whole project and any specialization you want.

We have over 10 years of database and software development experience. Most SQL flavours and some NoSQL as ElasticSearch and MongoDB. This makes it easy for us to fetch, convert and train the machine learning model.

ML Ops

Short for "Machine Learning Operations," refers to the practices and tools that are used to manage the machine learning (ML) lifecycle from development to deployment and monitoring. MLOps is a discipline that combines machine learning, data engineering, and software engineering to build, test, and deploy predictive models at scale.

MLOps aims to streamline the entire ML workflow, from data preparation and model training to model deployment and monitoring, to ensure that machine learning models are reliable, scalable, and performant. MLOps typically involves the following steps:

  1. Data Collection and Preparation: This step involves collecting, cleaning, and processing the data to prepare it for use in machine learning models.

  2. Model Training and Validation: This step involves training the model on the prepared data and validating its accuracy and performance.

  3. Model Deployment: This step involves deploying the trained model to a production environment, where it can be used to make predictions on new data.

  4. Model Monitoring: This step involves monitoring the performance of the deployed model in production and making any necessary adjustments or updates to ensure that it continues to perform well over time.

Benefitswith SaaS

With SaaS (Software as a service), you can access software applications through the cloud, eliminating the need for expensive hardware and IT infrastructure. This means lower upfront costs and reduced maintenance and support burdens.

But the benefits of SaaS don't stop there. With a subscription model, you can easily scale your usage up or down based on your business needs, and you'll always have access to the latest features and updates. Plus, with secure, reliable cloud hosting, you can trust that your data will be safe and accessible whenever you need it.

 We have a tech stack covering the whole IT landscape around AI, such as:

  • Cloud

  • PyTorch

  • MLOps

  • Machine learning

  • Graph neural networks

  • image recognition 

  • YOLO v8

  • OpenCV

  • NumPy

  • Pandas

  • TensorFlow

  • TorchServe

  • AWS Sagemaker

  • AWS Sagemaker Studio Lab

  • AWS Lambda

  • AWS Gateway

  • AWS SaaS

  • AWS Marketplace

  • Anaconda

  • Python

  • Roboflow

  • Git

  • Azure

  • Databases

  • Data warehouses

  • Datalakes