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Tech Check to AI Model Development

We help you implement AI into your software, products, or company with our individually developed AI models tailored to your solution.

Tech Check:

IT Environment:

  • Existing Technology

  • Integration with the existing Technology

  • MLops

  • Computational Resources

  • Scalability

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Data:

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  • Availability & accesibility

  • Identify data sources & acquisition

  • Quality / Quantity

  • Data pipelines

Implementation:

Model Training: Train the AI model using collected data, selecting appropriate algorithms, and tuning parameters for optimal learning.

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Model Testing: Validate the model with a separate dataset to ensure high performance and reliability. Measure key metrics like accuracy and precision.

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Model Deployment: Deploy the model into production, ensuring seamless integration with existing systems. Continuously monitor and maintain the model to keep it updated and effective.

Repid Protype Testing:

Very basic version of a product that tests only a specific assumption
to show whether the AI solution will work and if it can provide the
value expect.

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Goal of the prototype:

  • Eliminate project risk

  • Get feedback early on

  • clear picture of the scope,

  • complexity, and feasibility

After Service:
 

Performance Optimization: Continuously optimize the model for efficiency by fine-tuning parameters and updating algorithms as needed.

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User Support: Provide ongoing user support, including training sessions, updates, and troubleshooting assistance.

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Additional Assistance: If you need help with user support, performance optimization, or deploying additional models, we are here to assist you.

Programmierkonsole

AI Concepts Developed:

Some examples of our AI Tech Team and Partners.

RAG System for Mazda Car Manuals

Developed a RAG (Retrieval Augmented Solution) for Mazda car manuals, specifically designed to expedite the car repair process. This system enables mechanics to enter descriptions of car damage, after which it automatically displays detailed, specific instructions for repairing the identified damage. Implementing this solution significantly increased the efficiency and productivity of the workshops.

System that generates content and creatives at scale 

Developed a system that automates the creation of content and creatives, using Retrieval Augmented Generation (RAG) and the no-code UI platform Airtable. This system is designed to automatically ingest content from specific YouTube channels and generate unlimited original content like Linkedin posts together with its visual creatives. The system also schedules and automatically posts. This solution streamlines content production and sharing, making it efficient and effective for maintaining an active online presence.

Ad Approval solution

Developed an Ad Approval Solution that automates the manual verification process for advertising creatives. Previously, the brand had to manually review each advertisement to ensure adherence to brand guidelines and accuracy of details. The AI system detects instantly and marks incorrect elements within the ad creatives. This automation significantly streamlines the approval process, ensuring consistency and accuracy while reducing the workload on brand managers.

Customer Feedback Classifier

Developed a customer feedback classification system for one of the top five global food chains. Initially, the process required human intervention where they had to read and manually classify each piece of customer feedback into appropriate categories. The Customer has now automated this task, allowing for instant and accurate classification of feedback based on content.

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