We propose to build a Hydration-focused Quantir risk intelligence module for Omnipool, Stablepools, HOLLAR, lending, liquidations, and cross-chain DeFi flows.

Quantir is a DeFi risk monitoring and explainability platform. It 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 Hydration, the goal is to create an operational monitoring layer that helps the community, ecosystem operators, dashboards, bots, and protocol stakeholders detect liquidity stress, abnormal swaps, pool imbalance, HOLLAR-related stress, lending/liquidation risk, and XCM-flow anomalies earlier.

This post is intended as an initial community discussion before submitting a formal Treasury/OpenGov funding request.

Hydration combines several important DeFi components: Omnipool, Stablepools, Isolated Pools, HOLLAR, lending and borrowing, OTC trading, DCA, and cross-chain liquidity through XCM. These systems create a strong liquidity layer for Polkadot, but they also introduce risk patterns that are difficult to interpret through standard dashboards alone.

Risk can accumulate through pool imbalance, sudden liquidity withdrawals, abnormal swaps, large LP position changes, HOLLAR stability pressure, lending and liquidation stress, unusual XCM flows, and correlated activity across assets or pools.

By the time these risks become visible through price impact, liquidity deterioration, or user-facing issues, the best reaction window is often already gone. Quantir aims to surface these signals earlier and explain why they matter.

The proposed module would include data mapping for selected Omnipool, Stablepool, HOLLAR, lending, and XCM-related activity; risk features for liquidity stress, pool imbalance, abnormal swaps, LP behavior, HOLLAR stress, lending/liquidation pressure, and cross-chain flow anomalies; normalized risk scores; explainable alerts with supporting evidence; API and WebSocket outputs for dashboards, bots, and ecosystem operators; documentation and validation examples.

The goal is not to replace existing Hydration analytics. The goal is to add an explainable risk intelligence layer that converts protocol behavior into actionable alerts.

Quantir is already past the concept stage. The current platform includes a working dashboard, live collectors, transaction monitoring pipeline, protocol snapshots, normalized risk scoring loop, alert delivery system, explanation generation, API and WebSocket interfaces, deployed infrastructure, and support for 11 protocols across 3 chains.

The proposed scope consists of three milestones.

Milestone 1: Hydration monitoring scope and data mapping. We will select initial Omnipool/Stablepool assets and monitored entities, map pool, swap, LP, HOLLAR, lending, and XCM-flow signals, and define risk categories and evidence fields for alerts.

Milestone 2: Risk features and scoring. We will build Hydration-specific risk features for liquidity stress, pool imbalance, abnormal swaps, LP behavior, HOLLAR stress, lending/liquidation pressure, and cross-chain flow anomalies. These signals will be connected to Quantir’s normalized scoring loop.

Milestone 3: Alerts, API/WebSocket, and documentation. We will create 4-5 Hydration-specific alert categories, expose API/WebSocket outputs, and publish example payloads, documentation, and validation examples.

Estimated timeline: 8-10 weeks.

We are considering a first request of approximately USD 25,000 equivalent in HDX.

Suggested allocation: USD 7,000 for Hydration data mapping and adapter design, USD 6,000 for risk feature engineering and scoring calibration, USD 4,000 for alert logic and explainability, USD 3,000 for API/WebSocket outputs and examples, USD 3,000 for validation and documentation, and USD 2,000 for infrastructure, reporting, and contingency.

We are open to community feedback on how this work should be structured: a smaller proof-of-concept first, retroactive Treasury compensation after delivery, a milestone-based Treasury/OpenGov request, or another format preferred by the Hydration community.

If supported, the first version would deliver a Hydration-specific monitoring module, 4-5 risk alert categories, explainable risk outputs, API/WebSocket delivery, example alert payloads, documentation, and validation examples.

Quantir is built by a three-person engineering team based in Kyiv, Ukraine. Ilya Berdar(https://www.linkedin.com/in/ilya-berdar-6063a11b6/) works on protocol integration and system architecture, Andriy Boichuk(https://www.linkedin.com/in/andriy-boichuk-519291b/) works on backend infrastructure and API delivery, and Alex Grishenko(https://www.linkedin.com/in/alex-grishenko-66167b62/) works on data pipelines, integrations, and product implementation.

Website: https://landing.quantirintelligence.com/
App: https://app.quantirintelligence.com/
GitHub: https://github.com/quantirintelligence/quantir-risk-engine
Mail: [email protected]

We would appreciate feedback on four questions: whether this type of risk intelligence is useful for Hydration; which areas should be monitored first - Omnipool, HOLLAR, lending/liquidations, XCM flows, or Stablepools; whether the community would prefer a smaller proof-of-concept before a full Treasury request; and what funding structure would be most acceptable - retroactive compensation, milestone-based Treasury request, or another route.