KlugDossier's platform contains a different set of tools to deliver unique data securely to a consumer after proper validation done by paid validators. It strives to solve the BigData problems in every industry; below are a few use cases in AI, Finance, NFT and Insurance industries; this will help understand how Klugdossier will be used in these industries.

Financial Use Case

  • Consumers request Equity Analyst to enter the requirements using KlugDossier Web UI.

  • Based on the requirement of the consumers, the Web app requests data from the data providers.

  • KlugDossier Oracle node fetches the trading data and validates it using Validators Appointed based on Proof of stake.

  • AI models based on Consumer requirement has been selected to apply on top of the data for validation.

  • Strict confidentiality terms applicable to the communication from Data Owners to Data Consumers using Zero-Knowledge Protocol.

  • Smart contracts filter data of the Data owners and then feed it to AI models.

  • Predicted Accuracy generated from AI models is passed on to the equity analyst. If they are satisfied, it is then passed on to stakeholder and then Validators, who then check the models used and requests any changes to the KlugDossier team (if required).


Insurance Use Case

  • Reinsurance consumers, insurance providers/sellers can approach KlugDossier to sell their insurance policies to a consumer base. In large claims, they can also benefit from Reinsurance, using KlugDossier Oracle insurance, whereby the sellers can collect sufficient documents from different data sources in a time-efficient manner.

  • On-Demand Insurance Providers can sell the insurance on-demand and keep track of policies through KlugDossier Smart Contracts. They can predict the risks based on data collected and made available from multiple sources.

  • Microinsurance Providers can sell in tiny policies for a large community and predict risks within that community based on data collected by KlugDossier.

  • When a consumer approaches Insurance Provider with a claim, any Insurance Provider can collect Satellite Images, Weather data, Drone Images, other Insurance Dependent Data from social networks and verify the claim's validity, making the process much faster and transparent.


NFT Use Case

  • Consumers input the details of the images they need, e.g. Sports stars, Game characters, male, female, with/without etc.

  • Consumer inputs are given to the validators to verify and clarify with the consumer (if needed).

  • Validator (if all the inputs are good enough), then NFT data will be forwarded to KlugDossier Oracle Node, which contains Crypto Punk image generation based on the consumer inputs.

  • Crypto images generated will be given back to the consumer.

  • If a consumer is satisfied, then will pay the necessary fees; otherwise, validators will re-perform the task.

  • After making necessary changes in a model generation, validators will validate again in the KlugDossier Oracle node, and then the same loop will continue.


AI Use Case

  • Consumer reaches out with a request to predict cryptocurrency prices in a year time and chooses which model to use to predict the data or leave the selection of the model to the validators and nominators of the KlugDossier.

  • The Nominators identify the consumer's requirements and collect data related to cryptocurrency prices, news, online media data, etc.

  • The consumer pays the fees for providing such data

  • A Predicted AI model (Neural Network) is developed based on the consumer option.

  • Input data gathered from the Data Producer is then converted to Train and Test Data.

  • Train and Test Data is then passed on to the selected Neural Network, and the predicted data with accuracy point is delivered to the consumer.

  • If the consumer is satisfied with the prediction accuracy, then the workflow ends at this stage. If the consumer is not satisfied, they can raise the points to the validator, whereby the AI models are tweaked accordingly to the consumer comments. The whole process of execution of the model in the Oracle platform is re-performed.