Institutional AI Decision Support
Garuda Kapitalika combines AI predictive models with continuous, real-time market data to give institutional investors a validated basis for decisions — not a forecast, but a structured view of risk and probability updated as conditions change.
Dashboard view: live position sizing, volatility bands, and model confidence scores, refreshed on each data cycle and logged for audit.
The Engine
Each layer of the Garuda Kapitalika engine handles a distinct task. Together they reduce the amount of raw market noise an analyst has to process manually, without removing human oversight from the final call.
Structured and unstructured data — order books, on-chain flows, macro indicators, and news sentiment — are ingested continuously and normalized into a single comparable dataset. This removes the manual work of reconciling inconsistent data formats across sources.
Refresh interval: continuous, sub-minute
The model assigns a probability-weighted risk score to each asset or position based on historical volatility patterns and current market structure. Scores are expressed as ranges, not single numbers, to reflect the genuine uncertainty in the data.
Output: confidence interval, not point estimate
Position sizing and rebalancing suggestions are generated against a defined risk tolerance. Every suggestion includes the data inputs that produced it, so an analyst can trace the reasoning before approving an adjustment.
Rebalance proposals: logged with input trace
Transparency by Design
Cautious investors tend to distrust systems they cannot inspect. The workflow below is built so that each step of the process leaves a record, and that record is delivered to you daily rather than on request.
Market, on-chain, and sentiment data streams are pulled and timestamped around the clock. Gaps or anomalies in a data source are flagged automatically rather than silently interpolated.
Before any output reaches a portfolio recommendation, data passes a consistency check against independent reference feeds. Inputs that fail validation are excluded and noted in the audit log.
A report is compiled each day summarizing model decisions, position changes, and the realized versus predicted outcome for the prior period. The report is delivered in the same format every day, so variance is easy to spot over time.
Position changes with rationale, risk score movement, data sources used that day, and a comparison between the model's prior-day prediction and the actual market result. No figure in the report is retroactively adjusted after delivery.
About the Approach
Garuda Kapitalika was designed around a simple premise: an AI system that cannot explain its own reasoning is not suitable for institutional capital. Every scoring model used in the platform produces an input trace alongside its output, so a human reviewer can verify the basis for a recommendation before capital moves.
The platform does not replace an investment committee. It compresses the volume of data a committee would otherwise need to review manually, and flags where the model's confidence is low so attention can be directed there first.
Application Scenarios
These scenarios describe how institutional and high-net-worth clients typically structure an engagement with the platform. Outcomes depend on market conditions and the risk parameters set by the client.
Allocation across digital assets is weighted using correlation data rather than static percentages, so exposure adjusts as correlations between assets shift. The goal is to limit concentrated exposure to a single volatility regime.
News flow and social sentiment are scored and weighted against historical price reaction to similar sentiment shifts, giving analysts an early, quantified signal rather than a qualitative impression.
Methodology & Security
These are the questions we are asked most often during due diligence. If a concern you have is not addressed here, it can be raised directly during an initial consultation.
Portfolio and account data are stored separately from the market data used to train general models, and are not used to retrain models shared across clients. Access to raw client data is restricted to the systems required to generate that client's own reports.
Risk scoring models are recalibrated on a rolling basis as new validated data arrives, with a full retraining cycle at fixed intervals to incorporate longer-term structural shifts in the market. Recalibration events are noted in the daily audit log so clients can see when model behavior changed.
The model does not claim to predict low-probability, high-impact events before they occur. Instead, the validation layer is designed to detect abnormal data patterns quickly and widen risk score uncertainty ranges, which typically triggers a reduction in recommended position sizing rather than a speculative response.
Yes. Every recommendation is stored with its input trace and timestamp, and is retrievable from the client's reporting history, consistent with the daily reports already delivered.
Garuda Kapitalika is built for investors who want a systematic, auditable basis for AI-assisted decisions, not a promise of outsized returns. An initial consultation is used to review your risk parameters and reporting requirements before any engagement begins.
Get Started with Garuda KapitalikaNo-obligation initial consultation. Full daily reporting begins once an engagement is confirmed.