Product application

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Product Applications

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Product Application Workflow

A product application contains two components:

  • A predictive model that can tell you what effect a possible dose of your product may have on the microbial health.
  • A user interface on our KytoApp digital platform that enables easy communication of these complex software outputs to a farmer or grower.

We currently have workflows in place to develop sanitizer applications and probiotic applications. In both, we go through a series of laboratory, in silico, and commercial scale testing to create a powerful and optimized algorithm for your product.

Step 1 (probiotics only)

Product Characterisation

  • Product profiling on KYTOS platform
  • Accurate spore counts of probiotics
  • Vitality screening of probiotics
  • Assess batch variability

Step 2

Laboratory Testing

  • Experiments for testing product efficacy
  • Probiotics: germination & colonization
  • Sanitizers: hygienization efficiency
  • Define target health variables

Step 3

Dose-Response Modelling

  • Combine lab data with KYTOS database
  • Dose-response modeling of product
  • Build dose-recommendation model

Validate Your Model

Step 4: Commercial Scale Testing of Prototype Model

If all went well, steps 1-3 will have yielded your first (prototype) product application model. Congratulations!

Before we can bring this model to the real world, some additional commercial-scale validation is required. This step involves the following activities:

  • Treatment-control experimental set-up at demo or commercial farm
  • Run business-as-usual dosing vs. KYTOS dynamic dosing model recommendation
  • Evaluate performance in terms of farm Key Performance Indicators
  • Incorporate business-rules and install practical constraints to recommendation model

Scale, Reuse, Profit

Step 5: Implementation into KytoApp

Once we’ve made it successfully through step 4, we are now ready to incorporate the recommendation model into the KytoApp platform!

Here the user will be able to inspect the product portfolio recommendations for each sample analyzed by the KYTOS platform. Depending on the product and model performances, additional parameters such as maximum improvements to the microbial health can be evaluated.

After the model has been succesfully “deployed“, the following maintenance activities are required to ensure optimal performance:

  • Retraining of model based on updated KYTOS database
  • Incorporate product batch variations in dose recommendation models
  • Adjust for changes in business rules