CASE STUDY — BLOCKCHAIN
A Swiss DeFi platform improved transaction throughput by 27% and reduced failures by 21%—while also increasing adoption by building stronger user trust in security.
By deploying GRDigital’s Predictive Intelligence Scenario
Engine™ (PISE™), the firm embedded predictive foresight
into exchange protocols, optimized transaction performance,
and launched automated onboarding tutorials.
Opportunity
The platform faced network congestion, frequent transaction failures, and user distrust in the reliability of protocols. Up to a quarter of transactions stalled or failed under high load, creating losses and discouraging adoption. Security concerns further undermined confidence, as new users hesitated to commit assets in an unpredictable environment. These issues demanded a predictive framework that could anticipate risks, stabilize exchanges, and restore trust to sustain growth.
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The platform had reached a breaking point where volatility and failures limited user growth. GRDigital deployed PISE™ to simulate thousands of scenarios, identifying stress points before they could destabilize operations. By embedding predictive foresight into both protocol design and user onboarding, the system improved throughput and bolstered confidence. This transformed a fragile environment into one where stability and trust enabled sustainable scaling.
Results & Metrics
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27% improvement in transaction throughput
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21% reduction in failed transactions
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18% increase in new user adoption
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PISE™ provided foresight that secured both performance and adoption, proving that predictive modeling can be as critical as technical execution in scaling DeFi.
Key Takeaways
Learnings and Adaptability
The case underscored how predictive foresight reduces volatility and builds user trust, with outcomes varying by platform scale and ecosystem maturity. This case study data is hypothetical, however, it reflects blockchain benchmarks on scalability, security, and adoption; results are illustrative and will vary by implementation.
Service Provided
