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Overview
Our service provides a controlled testing environment to evaluate how AI models interact with your agricultural or food processing applications. We set up the necessary infrastructure to host and run the AI model you want to test, making it accessible through a standardised interface during the testing period. Instead of permanently integrating an AI model into your system, this service lets you first validate its performance and behaviour in a controlled setting. We configure our infrastructure to host the AI model and create a testing setup where your application can interact with it remotely. This allows you to thoroughly assess how the AI model performs as part of your system before making any commitments to full integration. The service includes setting up the testing environment, configuring the necessary interfaces for your application to communicate with the AI model during testing, and providing the technical documentation needed for the testing phase. This approach helps you evaluate AI capabilities in a low-risk environment while maintaining your existing systems' integrity.
More about the service
Discover more about our service, including how it can benefit you, the delivery process, and the options for customisation tailored to your specific needs!
This service helps when you need to evaluate whether an AI model will work effectively within your agricultural or food processing system before full implementation.
For example: Before the service: You have an AI model that could potentially improve your operations (like detecting crop diseases or product classification), but you're unsure how it will perform in real-world conditions or interact with your existing systems.
You face uncertainty about its reliability and integration challenges.
After the service, you have a ready-to-use model to test your app using it, and then, you are ready to find out, for example:
- How well the AI model performs when interacting with your application.
- Whether the response times meet your operational requirements.
- If the model's outputs are compatible with your system's needs.
- Potential technical challenges you might face during full implementation.
- The model's behaviour under different testing scenarios.
This testing environment lets you make informed decisions about AI adoption while avoiding the risks and costs of premature integration into your production systems. Think of it as a dress rehearsal for your AI implementation—you can identify and address potential issues before making major investments or changes to your existing operations.
For example: Before the service: You have an AI model that could potentially improve your operations (like detecting crop diseases or product classification), but you're unsure how it will perform in real-world conditions or interact with your existing systems.
You face uncertainty about its reliability and integration challenges.
After the service, you have a ready-to-use model to test your app using it, and then, you are ready to find out, for example:
- How well the AI model performs when interacting with your application.
- Whether the response times meet your operational requirements.
- If the model's outputs are compatible with your system's needs.
- Potential technical challenges you might face during full implementation.
- The model's behaviour under different testing scenarios.
This testing environment lets you make informed decisions about AI adoption while avoiding the risks and costs of premature integration into your production systems. Think of it as a dress rehearsal for your AI implementation—you can identify and address potential issues before making major investments or changes to your existing operations.
The service operates within our private infrastructure, providing access to deployed AI models. We can also consider performing the AI model deployment on user-provided infrastructure.Service implementation typically begins within 3-10 business days after receiving all required information and access credentials from the customer.
The initial deployment process, including validation and testing, usually takes 1-2 weeks.To initiate the service, customers need to provide their AI model specifications, including model format, required computational resources, and expected usage patterns. If using external models, customers must furnish necessary licensing documentation and model access credentials. The service can accommodate models developed in standard frameworks, and our team will work with customers to ensure compatibility.
Customers will receive comprehensive API documentation, access credentials, and a detailed integration guide. Our team provides technical support during the initial integration period to ensure smooth implementation. Performance reports can be generated if needed, detailing model usage, response times, and system health metrics.
The initial deployment process, including validation and testing, usually takes 1-2 weeks.To initiate the service, customers need to provide their AI model specifications, including model format, required computational resources, and expected usage patterns. If using external models, customers must furnish necessary licensing documentation and model access credentials. The service can accommodate models developed in standard frameworks, and our team will work with customers to ensure compatibility.
Customers will receive comprehensive API documentation, access credentials, and a detailed integration guide. Our team provides technical support during the initial integration period to ensure smooth implementation. Performance reports can be generated if needed, detailing model usage, response times, and system health metrics.
The deployment of AI model service offers several customisation options to meet specific business requirements while operating within certain technical parameters to ensure reliable performance and security.Our service supports customisation of model deployment configurations, including computational resource allocation, scaling thresholds, and access control policies.
Customers can specify their preferred authentication. However, customers should be aware of certain limitations. The service currently supports models developed in mainstream frameworks such as TensorFlow, PyTorch, and similar industry-standard platforms. Custom frameworks may require additional evaluation and setup time. There are also practical limits on model size and complexity based on available infrastructure resources, which we'll discuss during the service setup phase.
Our team works closely with customers to understand their requirements and recommend the most suitable customisation options while ensuring service reliability and security standards are maintained.
Customers can specify their preferred authentication. However, customers should be aware of certain limitations. The service currently supports models developed in mainstream frameworks such as TensorFlow, PyTorch, and similar industry-standard platforms. Custom frameworks may require additional evaluation and setup time. There are also practical limits on model size and complexity based on available infrastructure resources, which we'll discuss during the service setup phase.
Our team works closely with customers to understand their requirements and recommend the most suitable customisation options while ensuring service reliability and security standards are maintained.
Location
Remote
Type of Sector
Arable farming
Food processing
Greenhouse
Horticulture
Livestock farming
Tree Crops
Viticulture
Type of service
Test setup
Accepted type of products
Design / Documentation
Software or AI model
