Project Sentinel

Platform

What the platform does

Project Sentinel is a market-data and research platform under active development. Its work falls into the areas below.

Market-data collection

Collection of live and historical digital-asset market data from public sources for research use, with consistent handling of symbols, timestamps, and units.

Historical processing

Structured preparation of time-series data into datasets suitable for repeatable analysis over long histories.

Data validation and reconciliation

Data-health checks that identify gaps, conflicts, and stale or inconsistent records, and cross-check data across sources before it is used in research.

Data provenance

Records of where data came from and how it was transformed, so datasets can be traced back to their origin.

Quantitative research infrastructure

Tooling for defining hypotheses, running experiments against historical data, and preserving outcomes so results can be re-run and reviewed.

AI-assisted workflows

Assistance for software development, research organization, and experiment review. AI is used as a working tool, not as a source of claims.

Operational reliability

Health-oriented controls intended to keep data collection and research systems running predictably and to surface problems early.

Cortex research intelligence

An internal component that helps organize datasets, research notes, experiments, and their results in a form that can be searched and revisited over time.

Research approach

Scientific hypothesis testing.

Quantitative research is treated as exploratory work. Hypotheses are defined up front, tested against historical data, and recorded with the data version they used. Negative results are recorded as well as positive ones, and a result is only considered meaningful when it can be reproduced.

Scope

What the platform does not do

Specific internal architecture, research methods, and datasets are not published. This page describes the platform at a high level only.