What We
Do

Advanced Analytics

Advanced analytics encompasses various techniques and processes that enable data-driven decision-making. It involves data collection, exploration, feature engineering, hypothesis testing, and generating insights to support informed choices. By leveraging advanced analytics, organizations can extract valuable information from their data, uncover patterns, relationships, and trends, and utilize these insights to make data-driven decisions.

Data Collection & Cleaning

No matter whether you store your data in the cloud or on-premise. We can build a robust pipeline that will end up in one place.

Data Exploration & Feature engineering

Preprocess the data to handle missing values, remove duplicates, and correct inconsistencies. The stability of the features is also crucial to cover in this step.

Hypothesis & Evaluation

Properly chosen statistical methods can provide robust characteristics, correlations, causations, and outlier detection, among other valuable insights.

Insights & Decisions

Interpret the results from the models to generate actionable insights. Use these insights to inform business decisions and strategies.

AI Solution

An AI solution involves a systematic approach to address a problem. It begins with problem identification, where a specific challenge or opportunity is defined. Next, data collection takes place, where relevant and representative data is gathered to train the AI model. The collected data is then used to train the model, enabling it to learn patterns and make accurate predictions or classifications. Finally, after the model has been trained, it is deployed to make predictions or provide intelligent solutions in real-world scenarios.

Identify the Problem

A detailed understanding of the business problem will highly increase the chances of success.

Collect Data & Choose the Model

Gather relevant data and preprocess it for AI algorithms. We prefer the approach “less is often more” in terms of code and data.

Training & Evaluation

We prioritize hyperparameter search paired with selecting the appropriate evaluation metric for modeling and evaluation.

Deploy, Monitor & Update

Models with good interpretability, fast iterations, and high performance are emphasized to facilitate insights generation and decision-making.

AI Strategy

AI strategy involves a comprehensive approach to leveraging artificial intelligence in an organization. It begins with understanding the business objectives and identifying areas where AI can add value. Next, an assessment of AI readiness is conducted, which includes evaluating data availability, technology infrastructure, and organizational capabilities. Based on this assessment, a roadmap is developed to outline the steps for implementing AI initiatives. The strategy also involves piloting initial projects to validate the effectiveness of AI solutions and gather insights for improvement. Finally, the strategy is continuously updated based on feedback, emerging technologies, and evolving business needs to ensure the organization stays at the forefront of AI innovation.

Understand Business Objectives & Identify Opportunities

Identify the business goals and how AI can help achieve them. Look for areas within the business where AI can bring value. This could be improving existing processes or creating new products or services.

Assess AI Readiness & Develop an AI Roadmap

Evaluate the organization’s readiness to implement AI, considering factors like data availability, technical infrastructure, and skills. Create a detailed plan outlining the steps to implement AI, including the necessary resources and timeline.

Implement Pilot Projects & Scaling

Start with small-scale projects to test the effectiveness of AI and learn from the results. If the pilot projects are successful, scale them up and continuously iterate based on feedback and results.

Monitor and Update the Strategy

Regularly review the strategy to ensure it remains aligned with business goals and takes into account the latest AI developments.

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