Description
Quantir is a DeFi risk monitoring and explainability platform that turns fragmented protocol activity into real-time, explainable risk intelligence. The platform continuously collects on-chain, market, and protocol activity data, computes normalized risk signals, detects abnormal behavior, and delivers machine-readable alerts together with human-readable explanations through API and WebSocket interfaces.
For RingDAO / Darwinia, Quantir proposes to build a cross-chain risk monitoring module focused on bridge-related activity, EVM transactions, cross-chain flows, abnormal transfer patterns, liquidity stress, and ecosystem monitoring.
The problem this proposal addresses is that cross-chain risk is difficult to observe through raw explorers and dashboards alone. Risk can build through unusual flows, repeated address patterns, bridge-related activity, liquidity movements, or suspicious transaction clusters before it becomes obvious through user-facing issues or market impact. Quantir aims to transform those fragmented signals into normalized risk scores, explainable alerts, and integration-ready outputs.
Team
Team Background
Quantir is built by a three-person engineering team based in Kyiv, Ukraine. The team works across blockchain integration, backend infrastructure, data pipelines, monitoring systems, risk scoring, alert delivery, and product implementation.
The current Quantir platform is already past the concept stage. It includes a working dashboard, live data collectors, transaction monitoring pipeline, protocol snapshots, normalized risk scoring loop, alert delivery system, explanation generation, API/WebSocket interfaces, deployed infrastructure, and support for 11 protocols across 3 chains.
Team Members
Ilya Berdar — Senior Blockchain Developer. https://www.linkedin.com/in/ilya-berdar-6063a11b6/
Leads blockchain architecture, protocol integration, and technical delivery.
Andriy Boichuk — Senior Software Developer. https://www.linkedin.com/in/andriy-boichuk-519291b/
Focuses on backend systems, infrastructure, service reliability, and API delivery.
Alex Grishenko — Senior Software Developer. https://www.linkedin.com/in/alex-grishenko-66167b62/
Focuses on data pipelines, integrations, monitoring workflows, and product implementation.
Proposal Details
Proposal Overview
We propose to build a RingDAO / Darwinia-specific Quantir module for cross-chain risk intelligence.
The module will monitor selected Darwinia/RingDAO ecosystem activity and convert raw cross-chain and EVM activity into structured risk signals. It will focus on detecting abnormal behavior, identifying early signs of stress, and delivering explainable alerts that can be consumed by dashboards, bots, ecosystem operators, and external monitoring systems.
The first version will focus on:
cross-chain activity monitoring;
bridge-related flow tracking;
EVM transaction pattern analysis;
abnormal transfer and address-cluster detection;
liquidity or flow stress indicators;
normalized risk scoring;
explainable alert generation;
API and WebSocket delivery;
documentation and validation examples.
Technical Approach
The implementation will extend the existing Quantir architecture.
Quantir’s architecture includes:
on-chain data collectors;
protocol-specific adapters;
transaction monitoring pipeline;
normalized risk scoring loop;
strategy layer for abnormal behavior detection;
explainability service;
alert delivery system;
API and WebSocket interfaces.
For RingDAO / Darwinia, we will add a dedicated adapter and monitoring layer that maps relevant ecosystem activity into Quantir’s internal risk model.
Technical work will include:
identifying initial Darwinia/RingDAO contracts, bridges, assets, and monitored entities;
building ingestion for selected EVM and cross-chain activity;
normalizing transaction and flow events into structured entities;
defining risk features for abnormal transfers, unusual bridge flows, repeated address patterns, liquidity/flow stress, and suspicious clusters;
connecting these features to Quantir’s scoring logic;
generating explanation payloads for risk events;
exposing outputs through API and WebSocket interfaces;
documenting schemas, alert formats, and integration patterns.
Challenges
Potential technical challenges include:
Cross-chain activity may be fragmented across different explorers, contracts, and data sources.
Some risk signals may require careful calibration to avoid false positives.
Bridge-related behavior can be noisy and context-dependent.
Historical data quality may vary depending on the monitored chain or contract.
Risk explanations must be clear enough for operators without oversimplifying technical causes.
We plan to address these challenges by starting with a limited monitored scope, validating signal quality against historical and live examples, documenting assumptions, and separating high-confidence alerts from lower-confidence monitoring observations.
Expected Outcomes
The expected outcomes are:
a working Darwinia/RingDAO monitoring adapter;
4-5 cross-chain risk alert categories;
normalized risk scores for selected monitored activity;
explainable alert payloads;
API and WebSocket outputs;
example alert schemas and payloads;
documentation for ecosystem operators and integrators;
2-3 validation examples or short case studies.
The result will be a practical monitoring layer that helps the RingDAO / Darwinia ecosystem detect abnormal cross-chain behavior earlier and interpret risk conditions more clearly.
Code Repositories
https://github.com/quantirintelligence/quantir-risk-engine
Development Roadmap
Milestone 1 — Scope Definition, Data Mapping, and Adapter Design
Schedule: Weeks 1-3
Funding requested: USD 8,000
Specification:
Identify initial Darwinia/RingDAO contracts, bridge flows, EVM activity, and monitored entities.
Define monitored categories such as cross-chain transfers, bridge-related activity, high-value flows, repeated address patterns, and abnormal transaction clusters.
Design event schemas and internal entity mappings.
Implement initial adapter structure for ingesting and normalizing selected activity.
Deliver sample structured outputs for testing.
Testing criteria:
Selected events can be ingested and mapped into structured internal objects.
Monitored entities are documented.
Sample decoded outputs are available for review.
Initial data assumptions and limitations are documented.
Milestone 2 — Risk Features and Scoring Layer
Schedule: Weeks 4-6
Funding requested: USD 9,000
Specification:
Build risk features for abnormal transfers, unusual bridge flow intensity, suspicious address clusters, repeated transaction patterns, liquidity/flow stress, and high-value movement.
Connect these features to Quantir’s normalized scoring loop.
Produce risk score outputs and risk score deltas for selected monitored entities.
Define 4-5 initial alert categories.
Testing criteria:
Risk features are generated from ingested data.
Risk score outputs are available for selected event categories.
Alert categories are documented.
Example risk events can be reproduced from sample or live data.
Milestone 3 — Explainable Alerts and Delivery Interfaces
Schedule: Weeks 7-8
Funding requested: USD 8,000
Specification:
Generate explanation payloads for each alert category.
Include supporting evidence such as transaction hashes, addresses, event types, timestamps, and key risk factors.
Expose alerts through API and WebSocket interfaces.
Provide example JSON alert payloads.
Testing criteria:
API returns structured alert data.
WebSocket stream delivers alert updates.
Each alert contains severity, risk score, reason codes, evidence, and explanation text.
Example integrations can consume the output.
Milestone 4 — Validation, Documentation, and Final Release
Schedule: Weeks 9-10
Funding requested: USD 5,000
Specification:
Run validation on selected historical or live activity.
Publish 2-3 short validation examples or monitoring case studies.
Provide documentation for alert schemas, API/WebSocket usage, monitored scope, and limitations.
Deliver final implementation report and future roadmap.
Testing criteria:
Documentation is available for developers and operators.
Validation examples are published.
Final report describes delivered scope, limitations, and next steps.
The module is ready for review by the RingDAO / Darwinia community.
Total Funding Requested
USD 30,000 equivalent
Long-Term Plans and Intentions
After the initial module is delivered, Quantir intends to maintain the Darwinia/RingDAO monitoring integration as part of the broader Quantir platform. Future work may include deeper bridge monitoring, additional cross-chain routes, expanded protocol coverage, more advanced risk models, dashboard integrations, and public ecosystem reports.
Quantir’s long-term goal is to provide reusable risk intelligence infrastructure for cross-chain DeFi ecosystems. The Darwinia/RingDAO module can become a foundation for broader monitoring of bridge-heavy and cross-chain activity.
Additional Information
Quantir’s core differentiator is that it combines monitoring, scoring, explainability, and alert delivery in one workflow. It does not only display charts or raw events; it translates protocol behavior into actionable, interpretable, machine-readable outputs.
The requested grant would fund ecosystem-specific integration, validation, and documentation rather than creating the entire platform from scratch. Quantir already has a functional monitoring stack, deployed infrastructure, and existing protocol coverage.
Website: https://landing.quantirintelligence.com/
Application: https://app.quantirintelligence.com/
GitHub: https://github.com/quantirintelligence/quantir-risk-engine
Description
Quantir is a DeFi risk monitoring and explainability platform that turns fragmented protocol activity into real-time, explainable risk intelligence. The platform continuously collects on-chain, market, and protocol activity data, computes normalized risk signals, detects abnormal behavior, and delivers machine-readable alerts together with human-readable explanations through API and WebSocket interfaces.
For RingDAO / Darwinia, Quantir proposes to build a cross-chain risk monitoring module focused on bridge-related activity, EVM transactions, cross-chain flows, abnormal transfer patterns, liquidity stress, and ecosystem monitoring.
The problem this proposal addresses is that cross-chain risk is difficult to observe through raw explorers and dashboards alone. Risk can build through unusual flows, repeated address patterns, bridge-related activity, liquidity movements, or suspicious transaction clusters before it becomes obvious through user-facing issues or market impact. Quantir aims to transform those fragmented signals into normalized risk scores, explainable alerts, and integration-ready outputs.
Team
Team Background
Quantir is built by a three-person engineering team based in Kyiv, Ukraine. The team works across blockchain integration, backend infrastructure, data pipelines, monitoring systems, risk scoring, alert delivery, and product implementation.
The current Quantir platform is already past the concept stage. It includes a working dashboard, live data collectors, transaction monitoring pipeline, protocol snapshots, normalized risk scoring loop, alert delivery system, explanation generation, API/WebSocket interfaces, deployed infrastructure, and support for 11 protocols across 3 chains.
Team Members
Ilya Berdar — Senior Blockchain Developer. https://www.linkedin.com/in/ilya-berdar-6063a11b6/
Leads blockchain architecture, protocol integration, and technical delivery.
Andriy Boichuk — Senior Software Developer. https://www.linkedin.com/in/andriy-boichuk-519291b/
Focuses on backend systems, infrastructure, service reliability, and API delivery.
Alex Grishenko — Senior Software Developer. https://www.linkedin.com/in/alex-grishenko-66167b62/
Focuses on data pipelines, integrations, monitoring workflows, and product implementation.
Proposal Details
Proposal Overview
We propose to build a RingDAO / Darwinia-specific Quantir module for cross-chain risk intelligence.
The module will monitor selected Darwinia/RingDAO ecosystem activity and convert raw cross-chain and EVM activity into structured risk signals. It will focus on detecting abnormal behavior, identifying early signs of stress, and delivering explainable alerts that can be consumed by dashboards, bots, ecosystem operators, and external monitoring systems.
The first version will focus on:
cross-chain activity monitoring;
bridge-related flow tracking;
EVM transaction pattern analysis;
abnormal transfer and address-cluster detection;
liquidity or flow stress indicators;
normalized risk scoring;
explainable alert generation;
API and WebSocket delivery;
documentation and validation examples.
Technical Approach
The implementation will extend the existing Quantir architecture.
Quantir’s architecture includes:
on-chain data collectors;
protocol-specific adapters;
transaction monitoring pipeline;
normalized risk scoring loop;
strategy layer for abnormal behavior detection;
explainability service;
alert delivery system;
API and WebSocket interfaces.
For RingDAO / Darwinia, we will add a dedicated adapter and monitoring layer that maps relevant ecosystem activity into Quantir’s internal risk model.
Technical work will include:
identifying initial Darwinia/RingDAO contracts, bridges, assets, and monitored entities;
building ingestion for selected EVM and cross-chain activity;
normalizing transaction and flow events into structured entities;
defining risk features for abnormal transfers, unusual bridge flows, repeated address patterns, liquidity/flow stress, and suspicious clusters;
connecting these features to Quantir’s scoring logic;
generating explanation payloads for risk events;
exposing outputs through API and WebSocket interfaces;
documenting schemas, alert formats, and integration patterns.
Challenges
Potential technical challenges include:
Cross-chain activity may be fragmented across different explorers, contracts, and data sources.
Some risk signals may require careful calibration to avoid false positives.
Bridge-related behavior can be noisy and context-dependent.
Historical data quality may vary depending on the monitored chain or contract.
Risk explanations must be clear enough for operators without oversimplifying technical causes.
We plan to address these challenges by starting with a limited monitored scope, validating signal quality against historical and live examples, documenting assumptions, and separating high-confidence alerts from lower-confidence monitoring observations.
Expected Outcomes
The expected outcomes are:
a working Darwinia/RingDAO monitoring adapter;
4-5 cross-chain risk alert categories;
normalized risk scores for selected monitored activity;
explainable alert payloads;
API and WebSocket outputs;
example alert schemas and payloads;
documentation for ecosystem operators and integrators;
2-3 validation examples or short case studies.
The result will be a practical monitoring layer that helps the RingDAO / Darwinia ecosystem detect abnormal cross-chain behavior earlier and interpret risk conditions more clearly.
Code Repositories
https://github.com/quantirintelligence/quantir-risk-engine
Development Roadmap
Milestone 1 — Scope Definition, Data Mapping, and Adapter Design
Schedule: Weeks 1-3
Funding requested: USD 8,000
Specification:
Identify initial Darwinia/RingDAO contracts, bridge flows, EVM activity, and monitored entities.
Define monitored categories such as cross-chain transfers, bridge-related activity, high-value flows, repeated address patterns, and abnormal transaction clusters.
Design event schemas and internal entity mappings.
Implement initial adapter structure for ingesting and normalizing selected activity.
Deliver sample structured outputs for testing.
Testing criteria:
Selected events can be ingested and mapped into structured internal objects.
Monitored entities are documented.
Sample decoded outputs are available for review.
Initial data assumptions and limitations are documented.
Milestone 2 — Risk Features and Scoring Layer
Schedule: Weeks 4-6
Funding requested: USD 9,000
Specification:
Build risk features for abnormal transfers, unusual bridge flow intensity, suspicious address clusters, repeated transaction patterns, liquidity/flow stress, and high-value movement.
Connect these features to Quantir’s normalized scoring loop.
Produce risk score outputs and risk score deltas for selected monitored entities.
Define 4-5 initial alert categories.
Testing criteria:
Risk features are generated from ingested data.
Risk score outputs are available for selected event categories.
Alert categories are documented.
Example risk events can be reproduced from sample or live data.
Milestone 3 — Explainable Alerts and Delivery Interfaces
Schedule: Weeks 7-8
Funding requested: USD 8,000
Specification:
Generate explanation payloads for each alert category.
Include supporting evidence such as transaction hashes, addresses, event types, timestamps, and key risk factors.
Expose alerts through API and WebSocket interfaces.
Provide example JSON alert payloads.
Testing criteria:
API returns structured alert data.
WebSocket stream delivers alert updates.
Each alert contains severity, risk score, reason codes, evidence, and explanation text.
Example integrations can consume the output.
Milestone 4 — Validation, Documentation, and Final Release
Schedule: Weeks 9-10
Funding requested: USD 5,000
Specification:
Run validation on selected historical or live activity.
Publish 2-3 short validation examples or monitoring case studies.
Provide documentation for alert schemas, API/WebSocket usage, monitored scope, and limitations.
Deliver final implementation report and future roadmap.
Testing criteria:
Documentation is available for developers and operators.
Validation examples are published.
Final report describes delivered scope, limitations, and next steps.
The module is ready for review by the RingDAO / Darwinia community.
Total Funding Requested
USD 30,000 equivalent
Long-Term Plans and Intentions
After the initial module is delivered, Quantir intends to maintain the Darwinia/RingDAO monitoring integration as part of the broader Quantir platform. Future work may include deeper bridge monitoring, additional cross-chain routes, expanded protocol coverage, more advanced risk models, dashboard integrations, and public ecosystem reports.
Quantir’s long-term goal is to provide reusable risk intelligence infrastructure for cross-chain DeFi ecosystems. The Darwinia/RingDAO module can become a foundation for broader monitoring of bridge-heavy and cross-chain activity.
Additional Information
Quantir’s core differentiator is that it combines monitoring, scoring, explainability, and alert delivery in one workflow. It does not only display charts or raw events; it translates protocol behavior into actionable, interpretable, machine-readable outputs.
The requested grant would fund ecosystem-specific integration, validation, and documentation rather than creating the entire platform from scratch. Quantir already has a functional monitoring stack, deployed infrastructure, and existing protocol coverage.
Website: https://landing.quantirintelligence.com/
Application: https://app.quantirintelligence.com/
GitHub: https://github.com/quantirintelligence/quantir-risk-engine