Machine learning

With our machine learning services, you get a solution that fully meets your requirements and enhances your ability to use AI to streamline and grow your business. Protecting all processed data to the highest degree possible is a matter of course with us. We design and build projects in AzureAI.

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Qualitative testing of AI/AGI/LLM models

AI model upgrades

We focus on providing services to help you improve the performance of your AI models. Based on the provided parameters, we create inputs to extract control data from your AI/AGI/LLM models and then perform a detailed comparison and evaluation of these data according to your specific requirements.

Reinforcement Learning from Human Feedback (RLHF)

Reinforcement Learning from Human Feedback (RLHF) is a method that combines traditional reinforcement learning with direct feedback from humans. This technique allows AI models to better adapt and respond to complex human preferences and understandings. RLHF uses human feedback to create a reward signal that is used to fine‑tune the AI model's behaviour. The advantage of RLHF is the model's ability to capture and integrate complex human values and preferences, leading to greater accuracy and relevance of outputs.

Objective control of model outputs (Data Relevance)

We analyse model outputs and identify any biases, confounding elements or systematic errors to ensure the highest possible accuracy and objectivity of results. Our quality control processes include:

Reviewing the accuracy of data inputs.
Verifying the accuracy of algorithms and computational processes.
Evaluating model outputs for relevance and accuracy.
Implementing feedback to continuously improve the model.

Objective review of model outputs is key to ensuring the accuracy and reliability of models used in a variety of fields, from financial services to engineering applications.

Qualitative checking of model outputs (Data Rating)

We assign flags and identify groups of terms based on sentiment and other qualitative criteria to ensure the highest quality outputs from your model.

Qualitative checking of model outputs is a key part of the research and data analysis process. This process involves several important steps that are necessary to ensure the accuracy and reliability of the results obtained. One of the first steps is data preparation, which involves collecting data from the instrument to be solved with possible transcription of the audio tracks. This is followed by an annotation section, in which the data is expertly described based on predefined specifications that help in further analysis, interpretation and evaluation of the quality of the AI tool output. The qualitative check of model outputs is therefore a complex process that requires thorough preparation, a systematic approach and careful analysis. This is a necessary step to ensure valid reliable research results and this process will ensure that the outputs can be used for further scientific purposes or practical applications.

Neural network implementation

Comprehensive solutions for your business

We specialise in the orchestration and tuning of neural networks, GPT models and related services for your specific needs. Based on your requirements, we will create a comprehensive ecosystem around the AI, which includes command definition, specified datasets, predefined processes and parameters.

What you can expect from our services

Expert design and customisation of models: Our AI experts will design or customise your chosen neural network or GPT model to fully meet your specific needs and requirements.
A comprehensive AI ecosystem: We'll build a complete ecosystem around your AI that includes command definition, detailed datasets, processes and parameters, allowing you to maximise the potential of your AI.
Protecting the data you process: Objective checking of model outputs is crucial to ensure the accuracy and reliability of models used in a variety of fields, from financial services to engineering applications.

Unsure or need advice? Do not hesitate to contact the professionals.