Capital-allocation intelligence
Constraint-aware scenarios, portfolio effects, reasons, uncertainty and decision records.
SYSTEM ARCHITECTURE
A layered intelligence architecture that keeps measured evidence, AI interpretation, portfolio context and capital constraints clear and inspectable.
SYSTEM DESIGN
Prospect is not an LLM wrapper around portfolio data. The system uses the best method for each problem: statistics, time-series methods, change detection, machine learning, optimisation, explicit rules and frontier AI models where reasoning or synthesis adds value.
Measured evidence and AI interpretation remain distinct so an investment team can see what the system observed, what it inferred and why.
INTELLIGENCE STACK
Constraint-aware scenarios, portfolio effects, reasons, uncertainty and decision records.
Correlation, concentration, common exposure, diversification and simultaneous degradation.
Versioned market context, strategy × regime compatibility and dependency changes.
Behavioural fingerprints, strategy-specific monitors, change detection and significance.
Structured identity, thesis, expected edge, dependencies, assumptions and lineage.
TRUST & PROVENANCE
Measured claims trace back to source data, transformations, feature windows, configuration and model versions.
AI explanation is grounded in saved evidence and separated from supplied facts and deterministic measurements.
Eligibility, concentration, exposure, drawdown, capacity and approval rules remain first-class controls.
CURRENT BOUNDARY
Prospect is currently being built as institutional analytics and decision-support software. It can produce evidence, interpretation and allocation scenarios; the customer retains discretion over capital.
The architecture can support deeper automation later if product trust, validation, regulation and customer demand justify it.
BUILDING V1
It is being built toward strategy understanding, regime intelligence, portfolio intelligence and explainable allocation support—not a standalone monitoring dashboard.
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