Credit Oracle Network
Propelling On-chain Economy
Credio streams machine learning model outputs by top data scientists and rating agencies directly into DeFi smart contracts in an automated, privacy-preserving and decentralized manner
On-chain Issuers
Attract institutional capital
with credit ratings from top modellers
RWA Credit Investors
Analyse, execute and monitor RWA transactions,
real-time and automated
DeFi Protocols
Smart contracts can integrate with Credio
to automate their economic risk management
Credio supplies machine learning model outputs
directly to smart contracts
Consumers: are DeFi issuers, investors or protocol DAOs that define risk parameter needs, set performance benchmarks, and establish rewards for Modellers.
Modellers: build machine learning risk models and provide them as a service to earn revenues from ongoing use by Consumers.
Validators: ensure models' integrity and quality by evaluating models against consumer needs, verifying zkML proofs and ensuring they meet the performance benchmarks.
Oracle: the smart contracts that coordinate and incentivize the interactions between Consumers, Modellers and Validators to ensure the Credio provides high quality, decentralized model output feeds in a privacy-preserving manner.
Trusted by
Fasanara | Untangled
Programmable money
needs programmable creditworthiness.
Because money is credit.
High quality inference feeds
- - Built by top modellers and rating agencies
- - Validated independently by third parties with 'skin in the game'
- - Automated by smart contracts avoiding manipulation
Decentralized, reliable and secured infrastructure
- - Proven smart contracts end-to-end security
- - Transparency through blockchain immutability
- - Accessible and shared data infrastructure
Privacy-preserving inferences through zkml technology
- - Verifiable computation outputs without revealing the model workings
- - Top modellers and rating agencies are incentivised to participate
- - Cutting edge technology integrating AI/ML with blockchains
Credio incentivizes top data scientists to build models and generate inferences that are fed directly to on-chain applications
Modeling challenge
- - Consumers set out risk criteria/model outputs as challenges to Credio's decentralized community of modellers
- - Modellers build and commit their models. They then compute unique test outputs against a challenge dataset along zkML proofs. Validators verify proofs and evaluate the inferences against performance benchmarks.
- - Top performing model(s) are deployed and act as trusted modelling source, ready for consumption.
Oracle operation
- - A Consumer calls to request the model output feed through Credio as a custom feed from a particular model
- - The request is then routed to a trusted model which computes and outputs risk parameters against the on-chain and off-chain data submitted by the Consumer.
- - Inference is then provided to the Consumer along with zero knowledge proof. The entire process is seamless and programmable according to certain time-based or event-based triggers.
Roadmap to a decentralized
risk intelligence ecosystem
01: Data engineering and core model building
Engineer custom datasets combining both off and on-chain data and build machine learning models for a sample asset classes - working capital private credits
02: Apply privacy-preserving technologies
Evaluate and incorporate zkML and OPML to both data inputs and model inferences
03: Decentralizing modelling
Develop an ecosystem of modelers and output users (model consumers)
Talk to an expert
Tell us about your credit intelligence needs or joining Credio's modelling community?