B2B Data Intelligence
A single dashboard to manage your financial decisions between two missions
Dogiri Nipure aggregates data from multiple exchanges into a single interface and applies predictive models to assess risk before each arbitrage.
Dogiri Nipure aggregates data from multiple exchanges into a single interface and applies predictive models to assess risk before each arbitrage.
A freelancer active on several exchange platforms often manages several separate accounts, each with its own reporting format. This dispersion requires manually reconstructing an overview before any allocation decision.
The time spent on this consolidation reduces the ability to react to market movements, particularly for professionals who only devote part of their week to managing their capital.
Estimated manual processing time before and after centralizing data flows in a single dashboard.
The Dogiri Nipure engine connects to the programming interfaces of multiple exchange platforms and standardizes heterogeneous data formats — orders, balances, transaction histories — into a single schema. This standardization makes it possible to apply the same analysis models to the entire portfolio, regardless of its origin.
Secure connection of existing accounts via read-only access keys.
Normalization of raw data into a common structured format.
Analysis of each position with regard to history and market context.
Reporting of results in a single dashboard, continuously updated.
Compatible source categories
Indicative list of connector categories supported by the aggregation architecture.
Each position is rated based on historical volatility, liquidity and correlation with the rest of the portfolio. This score is updated with each new market data, without manual user intervention.
Unlike periodic reporting, the analysis is carried out in a continuous flow. Significant deviations are reported as soon as they appear, which reduces the time between a market change and its integration into the decision.
Forecast models rely on long time series to estimate probable scenarios, not certainties. They serve as decision support, in addition to the user's judgment, not as a replacement.
The framework combines risk classification models and regression models for trend projection. Each model is documented and versioned, which makes it possible to trace the origin of a recommendation.
Platform access keys are strictly limited to reading and encrypted at rest. No wallet data is shared with third parties for commercial purposes.
The processing follows four stages: collection, standardization, modeling, restitution. Each step is logged, which allows a posteriori audit of the decisions resulting from the analysis.
A freelancer active on several platforms visualizes his actual asset distribution without reconstituting a table manually, and adjusts his allocation according to the consolidated risk score.
Risk signals allow us to anticipate excessive exposure before a phase of high volatility, rather than reacting after the decline.
Between two missions, the time available to monitor the markets remains limited. Consolidated alerts reduce the need for constant manual monitoring.